Findmykids is a family location-sharing and safety app: family members, from kids to parents and grandparents, can see where each other are, and parents can also see how a child’s phone gets used. The study combines app usage data for 30,778 children aged 10 to 17 on Android phones over four weeks in August and September 2026.

By 10, a child’s online life is already multi-platform: a game with chat, a messenger, often a social app, all in the same week. Most of that network is made of mainstream apps. Apps built for meeting strangers reach 1.5% of children in the sample. Roblox alone reaches 57%.

Image courtesy of Findmykids

For online safety for kids, that changes the question parents ask. “Is Roblox safe?” still matters. So does what a child does when someone from a Roblox game asks for their WhatsApp number.

One limit up front: Findmykids sees which apps were open on a child’s phone, not what was said in them or who was on the other end.

The Number of Chat Apps Climbs Fastest Between 10 and 13

Roblox counts as a chat app here, because players can message each other. So do TikTok, Snapchat, WhatsApp, and Discord. YouTube and the phone’s own texting app don’t count, so the figures are on the conservative side.

The typical 10-year-old uses two chat apps a week; the typical 12-year-old three. From 15, it’s four, and the share with two or more stops growing at about 82%.

One in Three 10- and 11-Year-Olds Has a Chat Game and a Messenger in the Same Week

In a typical week, 34% of 10- and 11-year-olds used both a game with chat and a messenger. Over the full four weeks, 45% did so at least once. Two apps dominate the pairs: Roblox and WhatsApp were used in the same week by 28% of 10- and 11-year-olds, Roblox and Discord by 10%.

The figures show two apps on one phone in the same week, not a conversation that started in Roblox and continued in WhatsApp. A child may play Roblox with classmates and use WhatsApp to message a grandmother, and the data would look the same.

Roblox Reaches 57% of Kids, All Anonymous-Chat and Dating Apps Together Reach 1.5%

The apps built for meeting strangers barely register in the sample. Ordinary platforms reach most children:

Type of app -> Children who used it in four weeks -> Most used

Video platforms -> 93% -> YouTube 88%, TikTok 57%

Social apps -> 69% -> Instagram 37%, Snapchat 30%

Games with chat -> 67%-> Roblox 57%

Messengers-> 65% -> WhatsApp 43%, Discord 25%

Dating and anonymous-chat apps -> 1.5% -> OmeTV, NGL

The last row is 469 children out of 30,778, mostly on random video-chat and anonymous-question apps.

Chat comes built into the apps children use every day, so a blocklist of obscure apps covers a small corner of a child’s online life. The FBI’s list of where offenders find children reads like the table above: social media, online games, gaming consoles, video platforms, and messaging apps.

By 13, the Network Looks Different

The mix shifts toward social media well before 13, the minimum age on most social platforms. About half of 10-year-olds already use a social app. By 12, Instagram opens on 27% of kids’ phones, whether the account belongs to the child, an older sibling, or nobody at all.

Why the Spread Across Apps Matters for Safety

What happens inside those apps is outside Findmykids data, but other researchers have looked. In a 2022 Thorn survey of 9- to 17-year-olds, two in three said someone they knew only online had asked them to continue in a private chat on another platform. That counts requests, not harm. In sextortion cases, the FBI says offenders often push for a switch to an app with video calls, and in Thorn’s review of financial sextortion reports the conversation most often moved to Snapchat or a messenger such as WhatsApp.

In 2025, NCMEC’s CyberTipline received more than 80,000 sextortion reports, about 137 a day for financial sextortion alone. The FBI names boys aged 14 to 17 as the typical victims. In the Findmykids data, the multi-app setting is already in place at 10, four years before that age group. That’s about timing, not risk: it is a sensible age to start the conversation.

What Parents Can Do With This

No single app shows this picture. Roblox’s parental controls can’t see WhatsApp, and WhatsApp’s settings know nothing about Roblox. The overlap is visible only from the phone as a whole. If Discord shows up on a 10-year-old’s phone one Tuesday, that’s a reason to ask what’s new, not a verdict.

That conversation is easiest before anything happens. Take one situation: a child plays Roblox with someone they met in the game, and after a few days the new friend suggests continuing on WhatsApp, Snapchat, or Discord. Kids do this with classmates all the time, so the switch proves nothing on its own.

The warning signs are worth agreeing on in advance. If someone you know only online asks you to move to another app, keep a chat secret, send a photo or money, talk about sex, or meet in person, tell me or another trusted adult. Telling won’t cost you your phone. A child who expects to lose the phone for speaking up is more likely to stay quiet.

If a Child Is Being Pressured or Threatened

Stay calm, don’t blame the child, and don’t pay or send anything more: the FBI notes that cooperating rarely stops the blackmail. Save the messages and usernames, then block and report the account. In the US, report suspected exploitation to NCMEC’s CyberTipline (1-800-843-5678) or to the FBI call 911 if a child is in immediate danger.

Methodology and Limitations

Aggregated app usage statistics from the Android phones of US children whose parents use Findmykids, Aug. 31 to Sept. 27, 2026: 30,778 children aged 10 to 17 with usage data on at least 14 of the 28 days. An app counts as used after at least 60 seconds in a day. The sample leans toward Android families who chose Findmykids, and it compares children of different ages at one point in time rather than following the same children.

Findmykids doesn’t see the content of messages or a child’s contacts. The study can’t identify grooming, sextortion, or victims, and using an app doesn’t mean talking to a stranger. Figures from Thorn, the FBI, and NCMEC come from other samples and methods and are not comparable to Findmykids data.

The full study, with definitions and charts by age, is on the Findmykids blog.

The post Two in Three 10-Year-Olds Already Use More Than One App With Chat, Findmykids Data Shows appeared first on Under30CEO.

The store is at 4811 Overton Ridge Boulevard, Suite 200, Fort Worth, TX 76132. Walk-ins are welcome, while a private Personalized Sleep Consultation provides dedicated time with a specialist.

Mattresses on display at iSense’s Fort Worth showroom, where visitors can try different comfort settings with guidance from a Comfort Control Specialist.Photo credit: iSense

Start With the Feel of the Mattress

With iSense’s patented Comfort Control Technology, each sleeper can independently adjust the feel of their side from softer to firmer while maintaining the support and feel of a premium mattress. During a visit, couples can explore their individual preferences without having to agree on one setting.

“These stores give people the opportunity to take control of their comfort and experience what Personalized Sleep actually means,” said Paul Longman, CEO and Founder of iSense.

Explore Position and Temperature

The consultation also covers sleep position and temperature. Visitors can explore adjustable sleep position and temperature-control options with a specialist, considering how those choices fit their preferences alongside mattress firmness.

Premium bedding and massage chairs are available as well. Shoppers can enjoy a complimentary 15-minute massage chair experience while exploring the store.

From Friday, October 30, shoppers will also be able to visit the Plano showroom at 8240 Preston Road, Suite 185, Plano, TX 75024. The location will offer the same personalized guidance and opportunities to try different comfort settings.

Photo caption: The Plano showroom opens October 30, extending iSense’s in-person shopping experience to Preston Road. Photo credit: iSense

For another way to try the products, the company’s Mobile Showroom continues to serve select Dallas-Fort Worth communities. The permanent showrooms also welcome existing iSense customers seeking personalized assistance or a chance to explore additional sleep products.

iSense was founded in 2017 and is headquartered in Springfield, Missouri. The brand also sells online and offers a 180-Night Comfort Guarantee. Customers can ask a specialist about the guarantee and the options available for their preferred sleep setup.

The post What to Try During a Visit to an iSense Mattress Showroom appeared first on Under30CEO.

For Barbara Corcoran, it meant refusing to play by everyone else’s rules. For Suze Orman, it meant proving she could succeed in finance when the people around her didn’t believe she belonged. And for Andrea Miele, it was building something they can’t ignore.

These five women built businesses and careers in industries where men traditionally held most of the power. Their experiences offer valuable lessons for anyone trying to build something of their own.

Barbara Corcoran: Stop Trying to Fit In

When Barbara Corcoran started her real estate business in New York City, she didn’t have the money or connections of her competitors.

But she wasn’t interested in becoming one of them.

“I never thought of myself as a girl — I thought of myself as a competitor,” Corcoran told me in an earlier interview.

She built The Corcoran Group into one of New York’s best-known real estate businesses before selling it for $66 million in 2001.

Rather than copying established competitors, Corcoran used creative marketing, relationships, and her understanding of the market to stand out.

Her experience offers an important lesson: Don’t spend so much time trying to fit in that you overlook what makes you different.

Sometimes your biggest advantage is the perspective nobody else brings.

Andrea Miele: Build Something They Can’t Ignore

“Being the only woman in the room while building Beezie has become the norm, not the exception,” Andrea Miele told me.

As founder and CEO of Beezie, Andrea Miele operates in blockchain, collectibles, and digital commerce, industries where women founders remain underrepresented.

She’s also a mother of three who established a career before entering the Web3 industry.

“I don’t think like everyone else in the room, and I don’t want to,” she said.

Miele has learned that convincing skeptics isn’t always worth the effort.

“I’ve learned not to waste energy trying to convince everyone that I belong in the room,” she said. “I’d rather build something they can’t ignore.”

That advice stood out to me because entrepreneurs can spend years trying to prove themselves to people who may never understand what they’re building.

Miele also emphasizes the importance of relationships.

“Surround yourself with exceptional people. Find mentors, build relationships, and seek out allies, both men and women, but especially other women who understand the challenges you’re navigating,” she said.

And she encourages women to ask directly for opportunities.

“Ask for the meeting, the opportunity, the investment, the seat at the table.”

Her message is simple: Don’t wait for someone else to decide you’re ready.

Suze Orman: Don’t Let Others Define Your Potential

Before Suze Orman became a bestselling author and one of America’s most recognizable financial experts, she worked as a waitress.

In 1980, she joined Merrill Lynch in Oakland, California, despite having no experience in finance.

When I interviewed Orman, she told me she had been hired to fill a women’s quota and was warned she wouldn’t last six months.

“Nobody ever told me that I belonged there,” she said.

But Orman learned the business and eventually built a career as a financial adviser, television host and entrepreneur.

What struck me during our conversation was her emphasis on honesty, particularly when it comes to money.

She built her reputation on encouraging people to confront financial realities, even when the truth was uncomfortable.

Her experience offers two lessons: Don’t let someone else’s expectations determine your future, and never underestimate the value of telling people the truth.

Sara Blakely: Get Comfortable With Rejection

Sara Blakely didn’t have experience in fashion or manufacturing when she developed the idea for Spanx.

She had $5,000 in savings and a product she believed women would want.

But getting manufacturers to take her seriously wasn’t easy.

She faced repeated rejection before finding someone willing to make her product.

Blakely has credited part of her willingness to take risks to something her father taught her growing up.

In a CBS News interview, she described how he encouraged his children to talk about their failures.

Instead of treating failure as something embarrassing, she learned to see it as part of trying something new.

I’ve seen promising ideas stall because people become discouraged after an unsuccessful pitch or meeting.

Blakely’s experience is a reminder that rejection doesn’t always mean an idea isn’t worth pursuing.

Sometimes it means you haven’t found the right person willing to take a chance.

Whitney Wolfe Herd: Don’t Be Afraid to Change the Rules

Whitney Wolfe Herd helped launch Tinder before founding Bumble in 2014.

Her idea challenged traditional dating-app dynamics by giving women more control over initiating conversations.

In 2021, at age 31, she became the youngest woman at the time to take a company public in the United States.

But success didn’t protect her from criticism.

In a TIME interview, Wolfe Herd discussed the scrutiny female founders face and what it means for younger women watching them.

She returned as Bumble’s CEO in March 2025 as the company faced business challenges.

Her experience illustrates something important: Challenging an industry doesn’t guarantee an easy path.

But recognizing what isn’t working and having the confidence to do something different can create opportunities.

The Biggest Lesson: Stop Waiting for Permission

These five women didn’t follow the same path.

Corcoran used her differences to compete. Miele focused on results instead of approval. Orman refused to accept other people’s expectations. Blakely learned to embrace rejection. Wolfe Herd challenged established conventions.

Their experiences offer something worth remembering for anyone who has ever felt outnumbered in business.

You don’t need everyone in the room to believe in you before you begin.

Sometimes you have to build something that changes their minds.

The post Lessons From Barbara Corcoran, Suze Orman and Other Women Who Beat the Odds appeared first on Under30CEO.

Factory seconds, also sometimes labeled as factory direct or made-for-outlet goods, are those slightly imperfect products that retailers sell. They’re still functional, and you may not spot any differences at first glance. Compared to their perfectly manufactured counterparts, factory seconds can serve the same useful purpose. The difference is perhaps a tiny imperfect stitch on a shirt or an appliance with a color error.

When deciding how to offer a lower-priced product that comes across as a good deal rather than inferior, founders should understand which features consumers consider essential. Founders should also clearly grasp which product attributes consumers are willing to compromise on. Providing clear explanations of prices and trade-offs can make a value product more compelling. Here are some tips to keep in mind.

Understand What Customers Actually Need

What a customer perceives as value will depend on how they intend to use the product. Appearance may rank higher for clothing someone intends to wear when meeting with a client versus something they’ll put on for yardwork. Distinguishing your customer’s core needs from features that drive up costs without adding enough perceived benefit helps sell made-for-outlet goods. The value proposition emphasizes getting those core needs for less.

Basic software packages are an example, as not everyone will use all the customizable bells and whistles. Those customizations and add-ons may look flashy or attractive. But what if all someone really needs is simple, dependable office productivity apps? The price tag associated with the customizations makes them think twice. A lower-priced subscription with only what a buyer will use is an easier sell.

Ask which features your customers would willingly give up for a lower price. Market research, such as customer interviews and purchase feedback surveys, should point you to the answer(s). Don’t guess based on your preferences, instincts, or even third-party data. Get the information from leads and existing customers in your database.

Explain the Tradeoff Clearly—Using Factory Seconds as an Example

When offering factory seconds, it’s imperative to set proper expectations for the consumer on what they’re getting and how the product differs from secondhand goods. The consumer isn’t purchasing something that’s already been used or donated to a thrift store. They’re being offered a new, slightly imperfect product.

Dungarees, one of only a handful of reputable retailers to offer Carhartt factory seconds, emphasizes that the clothing is new. However, it’s described as having manufacturing irregularities, such as stitching errors, discoloration, or minor fabric imperfections. The key is that Dungarees clearly communicates to consumers why the product comes with a discount.

For founders outside of the apparel industry, the lesson is to identify the reason for the discount, explain the expected condition, and make relevant limitations and purchase terms easy to find. Specific explanations like this help customers decide whether the tradeoff for a lower price fits their needs.

Nonetheless, it’s crucial to accurately describe the tradeoff. You don’t want to set expectations that the lower tier or discounted option will work for everyone. Factory seconds suppliers would be remiss to imply that every irregularity is only cosmetic or that discounted products will suit every situation. Instead, they allow buyers to assess suitability for their specific intended use.

Build a Distinct Value Offering Instead of Relying on Constant Sales

When consumers see constant, unexplained markdowns on a product, questions naturally follow. Is the quality good? What’s the reason for the lower price? Buyers may want value, but they also want to know they won’t be compromising too many tradeoffs for a cheaper price tag.

It’s why factory seconds should be framed as a clearly defined lower-cost product category. Keep that category distinct from refurbished, open-box, and outlet merchandise so buyers understand what each label means. You can also sort products into premium, standard, and discounted tiers. Each tier serves a distinct set of customer priorities. Some value the most robust features, others want standard functionality, and some are OK with a few limitations for a discount.

Price differences should come with an understandable reason. You can describe why products fit into the category of factory seconds to illustrate the reason. Imperfect Foods is a grocery delivery company that does this. The value proposition is high-quality groceries with a few imperfections. The company also emphasizes food waste prevention.

Building a distinct value can also extend to goods like refurbished electronics, open-box appliances, and limited-feature service tiers. You can classify refurbished items as being in excellent, good, and average condition. Open-box labels may emphasize that the markdown is due to missing the original packaging, while limited-feature service tiers highlight basic functionality.

For each offering, define eligibility, pricing, and availability. Why do products fall into different categories and tiers? What are the differences in consumer cost? And how or where can buyers purchase the products that fall into each category? The objective is to improve customer understanding of the reason(s) behind the markdown or various prices.

Make Value Easy to Evaluate

Descriptions like affordable and high quality can come across as generic if you don’t include the details. Affordable and high quality often mean something different amongst consumers. To align these descriptions with customers’ perceptions and priorities, providing context is essential.

Tie the context to the information customers need to compare their options. Include details about intended product use, condition, and features. Prices should be presented, along with product availability and whether shoppers can return the items. When relevant, you can also include information about total ownership costs, such as ongoing maintenance and potential replacement expenses. A refrigerator, for example, may need replacement water filters every six months.

Audit your company’s product pages and sales sheets periodically. Are there any unanswered questions consumers have about use, condition, features, pricing, availability, and return/exchange terms?

Sell the Reason Behind the Price

A small imperfection or bare bones functionality can be an acceptable tradeoff when the buyer understands it. When you present a strong value proposition, it can establish a connection between customer priorities and what you’re clearly offering. Successfully positioning a low or discounted cost product or services involves asking customers what features matter most.

Then, present them with a viable lower-cost option while plainly explaining its benefits and limitations. When companies give customers a clear reason for a discounted price, they’ll make more confident decisions. Their functional and financial priorities can be simultaneously met.

The post What Founders Can Learn From Factory Seconds About Selling Value appeared first on Under30CEO.

Jeremy Adams falls firmly in the camp of the latter. He spent the early years of his adulthood waiting tables and trying to pay his bills. He was ambitious, but that on its own wasn’t enough. Adams knew he had to put in the work if he wanted to get ahead — and he did.

Adams built multiple businesses in his 20s and even earned Forbes 30 Under 30 recognition. Today, he’s in his mid-30s and is the founder of Unicorn Marketers, a bespoke agency that connects companies with vetted growth marketers who specialize in paid advertising and customer acquisition.

Looking back on his entrepreneurial journey, Adams has found that he measures success much differently than he used to. Here are three takeaways from Adams’ experiences on what it takes to build a business — and why it’s still worthwhile.

1. Don’t Try to Shortcut What Hard Work Can Accomplish

We live in an era where there are financial success stories everywhere. According to the Wall Street Journal, the U.S. added 1,200 new millionaires per day in 2025. Stories of crypto millionaires and investors benefiting from rising markets can make rapid wealth creation seem increasingly attainable. Soaring markets are making everyone feel flush with cash.

Amid the excitement around making money in a hot market, though, Adams emphasizes that building long-term wealth through entrepreneurship requires sustained effort. No matter what your friend tells you about the latest meme coin or stock option, those short-term gains are nice, but they don’t replace what real work creates.

Adams learned about the power of compound effort early and decided that the traditional educational path probably wasn’t for him. By the time he reached his teens, Adams was already waiting tables — and not just for spending money. He was starting to calculate and understand the relationship between effort, performance, and income. This had a deep influence on his early work and shaped his entrepreneurial mindset as he started to think bigger.

“That gave me my first real taste of making money based on how hard I was willing to work,” Adams explained. It wasn’t just finding the quickest path to the biggest payoff. Adams connected the dots early on that hard work and strong performance could contribute to long-term financial success. Luck is always a factor. But founders who consistently put in the work may be better positioned to navigate challenges and build something that lasts.

2. Don’t Brag About Revenue When It’s a Paper Tiger

While Adams started tapping into his business potential in his 20s, one of the biggest transformations in the last decade has been how he measures that success in his businesses. One key metric that he has moved away from is obsessing over revenue.

“Revenue is important,” he said, “but it’s just one number. A big revenue number doesn’t automatically mean you built a great business.” On the contrary, impressive revenue figures can mask a business’s underlying financial weaknesses. But it takes experience to really understand the allure (and trap) that revenue can create.

Like many entrepreneurs, early in his career Adams loved watching revenue growth. He considered it the ultimate scoreboard. The bigger the number, the more successful the business appeared. But over time, he began to see how easily impressive sales figures could fail to translate into meaningful personal income — or even a satisfying experience running the company.

“In my 20s, I loved being able to tell people, ‘We did almost $10 million last year,’ even though my actual take-home was a tiny percentage of that,” Adams recalls. “Boy, was that an immature view of success.”

That realization came after years of navigating difficult partnerships, employee challenges, and leadership mistakes.. The truth is, there is a lot of constant pressure that comes with running a growing company, even if your take-home doesn’t reflect that stress. Adams believes more young entrepreneurs should understand this before chasing the impressive revenue figures — and the lavish lifestyles that are often celebrated alongside it on social media.

For Adams, the trap is how easily revenue growth can become the primary measure of success. If you set the goalposts as more revenue, you need to grow fast to get there. But the speed at which an enterprise expands doesn’t indicate long-term sustainability.

Rather than treating growth as an end in itself, Adams increasingly emphasizes the underlying economics of the business. Can a company generate consistent profits? Also, can it attract talented people? Can it support a leadership approach and lifestyle its founders actually enjoy? The disconnect between these healthy indicators and revenue forced Adams to reconsider what he was actually trying to build.

“Now I care much more about building businesses that we actually enjoy running,” he said. “We’re generating profitable cash flow, working with people we genuinely like, and creating something that can be successful for a long time.”

3. Expect Problems Even When Business Is Going Well

Revenue isn’t the only area where Adams’ early expectations about entrepreneurship differed from reality. Adams has found over the years that another key factor is the dichotomy between a hard day’s work and the fact that it doesn’t prevent problems from arising anyway.

Hard work can contribute to business growth, financial success, and professional recognition. But Adams says one of the most important lessons from his early entrepreneurial years had little to do with prestige or financial success. It was learning that building a business presents ongoing challenges, even when things are going well.

“There’s partner drama. Employees quit. Sometimes employees steal from you,” Adams recounts. “Things break. People disappoint you. You’re constantly dealing with problems you never expected to have.”

Some new founders approach entrepreneurship expecting periods of progress punctuated by occasional setbacks. Adams has come to understand that setbacks can occur frequently throughout an entrepreneur’s career. They change, but they don’t go away — and that’s not a bad thing.

For Adams, the answer wasn’t to try to eliminate problems altogether. It came from a perspective shift. Now, he sees setbacks as a positive and exciting exercise in solving problems. “If it were easy, everyone would do it,” he said. “I think a lot of people underestimate how much of entrepreneurship is simply being willing to deal with problems every single day without letting them knock you off course.”

At the same time, he emphasizes picking your battles. Today, he has learned to distinguish between problems worth solving and situations where moving on is the better decision. “My 20s were more about trying to make everything work,” he said. “My 30s have been more about recognizing that some things simply aren’t worth fixing.”

Great CEOs Don’t Have All the Answers

Jeremy Adams’ final leadership lesson centers on recognizing your own limitations. He summarized this in one statement: Great CEOs don’t have all the answers.

“I actually think the people around me appreciate and respect me more when I’m honest about how much I don’t know,” Adams concluded. “And I’ve realized I know very little, so I say ‘I don’t know’ all the time now.”

Putting in the hard work. Knowing the metrics that matter. Treating setbacks as an ongoing problem-solving practice. Exercising great leadership through empowerment and trust. These are lessons that can become more meaningful as leaders gain firsthand experience.

Adams’ experiences illustrate how an entrepreneur’s definition of success can evolve over time. His greatest takeaway? Building a business comes with challenges that don’t necessarily disappear as a company grows. But maintaining the right perspective, putting in the work, and approaching problems constructively can help entrepreneurs build businesses that offer more than financial rewards. It’s genuinely rewarding.

The post Jeremy Adams on the Hard Lessons of Building a Business appeared first on Under30CEO.

Here is the lesson I want you to steal. Formic did not win by building the flashiest robot. It won by taking on the headache of making robots work in real factories, and it charges one monthly fee for the whole thing. That model is worth studying, whatever you sell.

What Formic does and how it charges

Formic runs a robots-as-a-service business. Customers pay a flat recurring fee that covers the equipment, software, maintenance, financing and monitoring. They put up no upfront capital, and Formic stays responsible for results.

By its own count, the fleet numbers in the hundreds and works inside upwards of 100 plants spread over 30-plus states. Together those machines have logged over three quarters of a million production hours. Founded in 2020, it assembles robots in Bolingbrook, Illinois, and builds new capabilities at an R&D center in Oakland.

Why the service model beats selling hardware

Factories hesitate to buy robots because the price is high and the setup is complex. Formic removes both barriers by bundling everything into a subscription. As a result, a plant owner can grow output without risking the balance sheet.

The renewal rate shows why this works. When you own the outcome, customers stay and often add more units. If you sell a product with hidden setup costs, ask whether a service wrapper could lift retention the same way. Our look at contract manufacturing shows another way founders are lowering the barrier to building physical products.

From single tasks to general-purpose robots

Today’s robots handle jobs such as palletizing, case packing and machine tending. Next, Formic wants to use the data from its fleet to support general-purpose robots that can handle a wider range of tasks. It is already working with Physical Intelligence, which develops robotic foundation models.

New targets include metal fabrication, along with deeper work in aerospace, defense, medical and energy. The strategy is simple. Collect production data now, then use it to train what comes next. Compare this with the humanoid robots pilot at a farm equipment maker we covered earlier this week.

Lessons founders can apply this quarter

You do not need a robot to use these ideas. Try the following:

• Package your product, support and setup into a single predictable price.
• Track uptime or reliability and share it with customers.
• Capture usage data from day one so it can improve future versions.
• Match funding to the asset, using debt for equipment and equity for growth.

The National Institute of Standards and Technology runs the Manufacturing Extension Partnership, which helps small manufacturers adopt new technology. It is a useful resource if you sell into factories.

What to watch as the funding goes to work

The company will use the money to strengthen its AI and robotics work and expand into new applications. Watch whether renewal rates hold as it adds new robot types, since complexity can strain a service model. For background on the funding climate, see our note on robotics funding.

Pitfalls of a service model

Subscriptions look tidy, but they carry real risk. You pay for the equipment up front, then collect revenue slowly, so cash flow and financing become central. That is likely why Formic paired equity with debt in this round. Service contracts also raise your responsibility, because every outage becomes your problem.

Before copying the model, run the numbers on payback time per customer. Estimate how many months of fees cover your costs, and stress test the result against slower sales. If the math only works with perfect renewals, adjust the price or the scope.

Finally, remember that trust is the real product here. A factory manager is handing over a critical line to an outside partner, so clear reporting, fast response times and honest communication matter as much as the hardware. Founders in any field can copy that habit by sharing a simple monthly report with each customer, showing what worked, what failed and what changes are coming next.

Quick answers for founders

Is robots as a service only for factories? No. The idea of bundling hardware, software, support and financing into one fee applies to many equipment-heavy businesses.

What proof should I gather first? Collect uptime, renewal and deployment-time data, because those figures show buyers that the promise is real.

The post Manufacturing Automation Startup Formic Raises $50M appeared first on Under30CEO.

If your product relies on location or precise timing, this deserves a calm read. A new layer of satellite navigation could change how fragile your service is. Let’s walk through what was announced and how to think about your own exposure.

What Xona announced and what it is building

Xona is building a network called Pulsar in low Earth orbit. The company claims accuracy down to a few centimeters and a signal up to 100 times the strength of GPS. It also says the signals resist jamming and can reach receivers indoors.

These are company claims, so independent testing will matter once the service opens.

Why GPS dependence is a hidden business risk

GPS quietly supports transportation, communications and financial systems. The announcement argues that billions of connected devices now rely on signals that can be jammed or spoofed. Whether or not Xona wins, that dependence is real for any company with delivery fleets, field crews or timestamped transactions.

Think of it as a supply chain question rather than a technology question. If the signal failed for a day, which of your processes would stop? The US government hosts a helpful overview at GPS.gov that explains how the system works and where it is used.

How Xona got to commercial service

The company says it holds a federal license, a first for a privately run navigation satellite network that coexists with government ones. It also began building satellites at its Burlingame headquarters. In October it launched its largest batch so far.

Four of the six new satellites will support beta service in several countries starting in early 2027. The other two, designed and built in-house, will prove out the design before the whole network is built. Steve Jurvetson of Future Ventures noted that he has backed only three space companies in his career, and Xona is one of them. Precise positioning matters in other sectors too, such as the autonomous aircraft startup we covered earlier this week.

A simple dependency map for your own company

You can assess your own risk in an afternoon. List every product or process that needs location or exact time, then rate each one by how badly an outage would hurt. A small hardware team might also review how an AI chip startup plans around supply and reliability.

• Identify the systems that need precise positioning or timing.
• Note which vendors supply that data and whether they have backups.
• Decide what manual fallback your team could use for a day.
• Review the findings with whoever owns security.

Our guide to small business cybersecurity offers a companion checklist for outages and attacks.

What to watch before the service launches

Commercial service is slated for 2027, and customers are already integrating Pulsar firmware into devices. Watch for third-party tests of the accuracy and jamming claims. Pricing and device support will also decide whether a smaller company can use it. Until then, treat the news as a prompt to plan, not a reason to change vendors.

Who should care now and who can wait

Most early-stage software companies will not feel any change in 2027. Their products do not depend on precise position or timing, so a new navigation network is a curiosity at best. Companies that run vehicles, drones, surveying crews, agriculture tools or financial systems with strict timestamps should pay closer attention.

A sensible approach is to assign one person to monitor the topic quarterly. That person can flag when beta access opens, when device makers announce support, and when independent testers publish results. This costs almost nothing, and it keeps you from being surprised later.

It also pays to talk with your insurer and your largest customers. Some contracts quietly assume a working location signal, and an outage could trigger penalties you never priced in. Ten minutes on the phone now is far cheaper than an uncomfortable conversation later. Write down what you learn, and revisit the notes whenever you add a new product line or a new region.

Quick answers for operators

Will Xona replace GPS? The company describes its network as an upgrade that works alongside existing systems, so a gradual overlap is more likely than a replacement.

When can I try it? Beta service is planned for early 2027 in several countries, and commercial service is expected in 2027.

Ask hardware vendors directly whether their devices will support new signals. Their answers will tell you how soon any benefit could reach your own operation.

The post Satellite Navigation Startup Xona Raises $250M to Rival GPS appeared first on Under30CEO.

That mix of advice and warning matters if you plan to raise soon. Capital is flowing, yet the terms are messy, and the story you tell has to hold up when the mood changes. Let’s turn the panel into a growth plan you can use.

What the Disrupt panel said about today’s market

Shailendra Singh of Peak XV said he has rarely seen early-stage investing as dysfunctional as it is now. Money is pouring into AI, and he worries that inflated revenue figures are being used to justify very high valuations. His view was that this optimism will fade, with a correction arriving in 2027.

Jahanvi Sardana of Index Ventures and Jenelle Teng Wade of Bessemer joined him. All three agreed that no single metric defines a Series A anymore. That makes your narrative and your proof points more important than any one number.

Tranches and three prices: reading the froth

Singh said a single company may now be negotiating a round at up to three separate valuations at the same time. Sardana gave the pattern a nickname, calling it tranchopalooza, because the money arrives in stages. Each stage can carry different terms.

For founders, the takeaway is to read the term sheet slowly. A big headline number can hide staged funding that depends on hitting milestones. The SEC publishes building blocks for raising capital that explain how offerings are structured, and it is worth a skim before you negotiate.

What investors want to see in a pitch

The panel returned to a few themes. First, tell a convincing story about why your team can build something large. Second, show that you can attract strong talent, which Singh called a real advantage. Third, build in an area where capital is moving, such as defense, robotics and AI infrastructure.

Teng Wade added that the big model builders are bringing more layers in-house, yet room remains for newer players. She named AI compute rental firms and research-focused labs as examples. If you want a real-world sample, see how Series A funding played out in a single recent mega-round.

Why cumulative revenue charts raise eyebrows

Metrics still help, but they are less rigid than before, especially in AI where running a company is expensive. Teng Wade described how her firm once weighed an early AI lab despite negative margins, because one partner believed the market would be huge for the top few players.

Honesty about data matters just as much. Singh warned that a chart showing cumulative revenue is a red flag, since it hides the real time series. Show monthly figures instead, and label anything that is not recurring. Investors tend to forgive a small number faster than a misleading one.

A growth plan that survives a correction

Use the panel’s advice as a checklist:

• Present monthly revenue and retention, never only cumulative totals.
• Choose your product by customer pain, not by what investors happen to be funding.
• Stress test your plan against a slower 2027, as covered in our look at AI bubble fears.
• Ask for what you need, since Teng Wade now encourages founders to be bold in the ask and adjust from there.

Practice the pitch too. Our notes from a recent startup pitch competition show how finalists framed their stories under pressure.

What to watch into 2027

Singh also told founders to slow down, stay alert to shaky product-market fit, and aim at a hard problem. Sardana cautioned that pitching skill should not steer what you build. Keep an eye on round structures and on whether investors start asking for monthly data more often. Those signals will tell you how the market is shifting.

It also helps to build a simple internal scorecard before any investor meeting. Rate your story, your team, your market timing and your data quality on a scale of one to five, then fix the weakest item first. Founders who know their gaps can answer hard questions calmly, and that composure is itself a signal. In a noisy market, calm and specific beats loud and vague nearly every time.

Quick answers for founders planning a raise

Should I wait for a better market? Not necessarily. The panel suggested the market rewards standouts, so a strong team with clean data can still raise, although preparation takes longer.

How do I handle a tranched offer? Compare the effective price after milestones, and ask who decides whether each milestone has been met.

From a growth standpoint, the same honesty applies to your public numbers. If a blog post, pitch deck or press mention overstates traction, customers and investors will eventually compare it with reality. Clean reporting is a brand asset, and it makes later fundraising much easier.

The post Series A Round Advice: Investors Warn of a 2027 Correction appeared first on Under30CEO.

Founders watching the legal AI market should read past the headline number. The more useful story is how a vertical product expanded inside big accounts, and which parts of that story are still company-reported claims. Here is a plain look at both.

The numbers behind the $8.8 billion valuation

Legora launched its platform in October 2024 and was founded in 2023. It says it grew from $1 million to $100 million in annual recurring revenue in under 18 months. The next $100 million, by its account, took under six months.

All of these figures come from the company, and none are audited. Treat them as a benchmark, not a verdict.

Why a narrow focus pays in high-stakes work

Legora sells to lawyers, a group that bills by the hour and cannot afford sloppy software. Because the buyer is specific, the product can be specific too. The company describes its platform as an operating system built for legal work, with new tools for client intake, horizon scanning and legal research.

Founders in other fields can borrow the pattern. Pick a profession where errors are costly, learn its workflow better than anyone, and build tools that fit inside it. If you are weighing AI agents for business, a tight niche is often easier to defend than a general assistant.

The in-house shift: growing inside existing accounts

One detail stands out. A year ago, Legora says, corporate legal departments barely registered among its buyers. By the third quarter, they accounted for over two in five new customers. Meanwhile, the United States is now its largest market, and a majority of the 50 biggest US firms, known as the AmLaw 50, are clients. Its first American office opened in spring 2025.

That suggests a land-and-expand motion. A product wins one team, then spreads to neighboring teams that share the same pain. The company’s CEO says the investors were chosen for their long-term view, which signals a plan to keep building rather than cash out fast.

How to read hypergrowth claims without getting dazzled

Fast growth looks impressive, but it can hide weak spots. Legora’s CFO has said that growth means little unless the underlying business is sound, and that is a fair rule for any founder. Ask whether customers renew, whether margins survive heavy AI costs, and whether revenue depends on a few large deals.

The Small Business Administration offers a useful primer on managing your business finances that applies at any scale. Meanwhile, our breakdown of Ramp growth lessons shows how another fast-scaling company framed its numbers.

Steps to borrow from this playbook

You do not need a nine-figure round to use the same thinking. Start with these moves:

• Choose one customer type and write down the three workflows where mistakes hurt most.
• Track which accounts expand after the first team adopts your product.
• Publish only metrics you can defend, and label unaudited ones clearly.
• Set up checks on your AI features, as outlined in our guide to AI agent monitoring.

What to watch after the round

Legora plans to spend the money on growth in North America, expansion into Europe and Asia-Pacific, and further product development. The key question is whether in-house adoption keeps climbing as the company enters new regions. Watch for renewal data and any pricing changes, since those will show how durable the growth is.

What the investor mix suggests

The new money came from large asset managers and a major bank, while several venture firms that backed the company earlier also returned. That blend usually signals that existing holders still see upside, and that bigger institutions now want a seat before any public offering talk begins. It is not proof of anything, but it is useful context when you compare your own cap table plans.

For a young company, the practical point is timing. Bring in partners who can help with the next stage, not only the current one, and ask them what they will do when the market gets rough.

Quick answers for founders

Is legal AI too crowded to enter? Competition is intense, so a narrow wedge such as one practice area or one buyer type is safer than a general tool.

What should I measure first? Renewals and expansion inside existing accounts, because they show whether customers truly depend on the product.

The post Legal AI Startup Legora Raises $550M: Growth Lessons for Founders appeared first on Under30CEO.

Running a company is stressful enough without worrying about a legal fight with a tech giant. Still, the logic behind this ruling reaches far beyond one social network. It is the same instinct driving the AI customer service rules now proposed in New York City, and it applies to any business that makes promises to customers.

What the New Mexico court actually decided

Judge Francis Mathew sided with the state after the jury backed it on 31 of 34 counts. The jury concluded that Facebook misled people about several things. These included how much control users had over their data, how hard the company worked against hate speech and misinformation, whether community standards were applied evenly, and how thoroughly it investigated after the Cambridge Analytica scandal.

The state had asked for as much as $40 billion, so the final figure came in lower. Even so, the judge wrote that the willful, repeated and systematic nature of the conduct justified the top tier of penalties. Interest also builds at 15% a year until the bill is paid.

Why marketing promises now carry legal weight

Notice what this case was about. It did not hinge on a hack or a leak. It turned on statements the company made about privacy, safety and fairness, and on whether the product matched those statements. For a founder, that means every line on your website, onboarding flow and sales deck can become evidence.

Regulators are asking a similar question at the federal level, as our report on the FTC investigation of major AI vendors showed. The core test is simple. Did you do what you said you would do?

The Federal Trade Commission publishes plain-language advertising and marketing guidance that explains how it judges claims. Reading it takes about twenty minutes, which makes it cheap insurance.

How per-violation penalties scale against small companies

The arithmetic is the sobering part. State law allows up to $5,000 per violation, and the jury tallied about 44 million of them. Multiply the two and you land near $220 billion, which is why the judge noted the state could have claimed far more than he awarded.

Your company is smaller, so your exposure is smaller. However, the counting method is identical. If one misleading claim reaches ten thousand customers, a regulator can treat each customer as a separate violation. That scaling logic also fuels mass arbitration claims against consumer brands.

A claims audit you can finish this week

Block two hours with your product lead and whoever writes your marketing copy. Then work through this short checklist:

• Collect every public promise about privacy, security, data control, AI use and content moderation.
• Test each promise against what the product does today, not what the roadmap says it will do.
• Rewrite or remove any claim you cannot prove, and name one owner who approves future claims.
• Save dated screenshots and test notes so you can show your work if anyone asks.

Repeat the exercise every quarter. Products change faster than copy, so drift is the most common way honest companies end up with misleading pages.

What to watch as the appeal and new laws unfold

Meta argues that the ruling makes doing business in New Mexico nearly impossible and amounts to a tax on speech. The state’s top lawyer, Raúl Torrez, expects Meta to appeal, so the final number may change. Higher courts will probably test the per-violation method closely.

Meanwhile, Torrez is working with a legislator to overhaul the decades-old Unfair Practices Act in the coming legislative session. Other states may copy the approach. Treat this as an early signal, and tighten your claims before a regulator asks you to.

Quick answers for busy founders

Does this only apply to big tech? No. State consumer protection laws cover companies of every size, although the penalty scales with the number of affected customers.

What is the fastest fix? Remove any claim your team cannot back up with a test, a log or a contract.

Do AI features raise the risk? They can. Any statement about accuracy, safety or human oversight is a promise, so test it and keep the results before you publish the claim.

The post Deceptive Trade Practices: What Meta’s $25B Ruling Means for Founders appeared first on Under30CEO.

You may not run a bank, yet the lesson applies to any company letting software act on its behalf. If an agent can send emails, move data, or touch customer records, you need to see what it does. Here is a plain way to think about it.

What the Survey Covered

The financial services slice included 278 IT and engineering professionals. They sit inside a worldwide study that covers 2,575 people. New Relic calls the gap between agent deployment and oversight “phantom velocity,” meaning teams move fast without the visibility to match.

The report ties this risk to Europe’s Digital Operational Resilience Act, known as DORA, which pushes regulated firms to prove they can withstand and recover from technology failures.

How Far Behind Are Finance Teams?

Looking ahead, respondents expect to catch up on AI application monitoring within a year, with projections of 83% against 80%. For agentic systems, the projection is 75% against 77%. Intentions are good, but they are not the same as coverage today.

The Payoff for Watching Closely

Better visibility pays off in fewer and shorter outages. Financial services respondents reported weekly high-impact outages at a 30% rate, versus 36% overall. They found problems in 35 minutes against 41, and fixed them in 49 minutes against 54.

The cost of downtime still stings. Financial firms averaged $1.69 million per hour, compared with $1.85 million globally. Even a small company can lose real money when a key system goes dark, so speed matters at every size.

A Simple Framework for Your Agents

Use three questions as a starting point. They work whether you run one assistant or twenty.

• What can this agent access, and who approved that access?
• How would we know if it started behaving strangely?
• Who gets the alert, and what do they do next?

Write the answers down for every agent. Our explainer on shadow AI risk shows why unofficial tools are the hardest ones to track.

Why Compliance Drives the Spending

The report ranked security, governance, risk, and compliance as the top reason teams invest in observability. AI applications came second. In other words, regulators and auditors are nudging budgets more than curiosity is.

You can borrow structure from public guidance. The National Institute of Standards and Technology maintains an AI risk management framework that is free to use. Pair it with a quick AI penetration testing primer to see how attackers probe weak spots.

Keeping the Bill Under Control

Monitoring tools cost money, and so do the cloud services they watch. Review your spending alongside your safeguards, as outlined in our piece on cloud cost management. Start with your riskiest agent, not every system at once.

Turning the Findings Into a Monthly Routine

A one-time audit helps, but a rhythm helps more. Set aside a short monthly review where someone lists every agent in use, confirms its permissions still match its job, and checks the alerts from the past thirty days.

Keep the review lightweight so it survives busy seasons. A single page with the agent’s name, owner, purpose, and last review date is enough for most small teams. If an agent has no clear owner, pause it until one is assigned.

Over time, this habit also creates a paper trail. Auditors, insurers, and enterprise customers increasingly ask how you govern automated tools, and a tidy record answers that question quickly. It turns a vague worry into a documented process.

Budgeting for Oversight Without Overspending

Many owners assume monitoring means an expensive platform. In practice, you can begin with logs and alerts built into the tools you already pay for. Add dedicated software later, once your agent count grows or a customer contract demands it.

Rank each agent by the damage it could do if it went wrong. An assistant that drafts social posts needs a lighter touch than one that approves refunds or handles personal data. Spend your time and money in that order.

Questions to Settle This Quarter

What does AI agent monitoring actually track?

It records what an agent does, what data it touches, and how it performs, so you can spot errors or misuse.

Do small companies need it?

Yes, at a lighter scale. A weekly log review and a clear owner go a long way.

Small, steady habits beat big, one-time projects. Choose the first agent to review, write down its owner, and put the date on your calendar today.

The survey’s closing message is blunt: responsible AI use starts with being able to see what your systems are doing. Pick one agent this week, assign an owner, and set up a basic alert before adding the next.

The post AI Agent Monitoring Gap: 22% of Finance Firms Run Blind appeared first on Under30CEO.

The money is interesting, but the pivot behind it is the real lesson. The company started in hardware and walked away from it after customers told it where the pain actually lived. Growth-minded founders should take notes, and the AI blueprint generator space shows how crowded this niche is becoming.

From Robots to Software: The Pivot Explained

Alex Baikovitz co-founded the company in 2021 as Mach9 Robotics. The goal was faster, cheaper visual inspections for civil engineers. After building the hardware, he realized capture devices were not the bottleneck.

The true pain sat in the workflow that converts laser scans and photos into something engineers can design from. So he dropped “Robotics” from the name and rebuilt the business around software.

The Numbers Behind the Round

The speed figure is the company’s claim, so verify it in your own pilot before you repeat it. Even so, the usage count shows real traction. Demand like that is what makes a Series A story believable.

Why the Old Way Is Ripe for Disruption

Baikovitz says CAD work is one of the slowest, most labor-heavy steps between planning and building. Manual drafting in tools from Autodesk and Bentley Systems can take about a week per mile. Projects can stretch for months.

Mach9 trained its own models to hit centimeter-level accuracy, because general-purpose AI could not meet survey standards. Its software drafts a first plan, and engineers then edit it. Licensed professionals still sign off, which keeps liability where the industry expects it.

Growth Lessons for Your Own Company

Marketers and founders can borrow three moves from this story.

• Ask customers what they refuse to pay for, not just what they want.
• Sell a faster workflow, because buyers feel time saved immediately.
• Keep humans in the loop when trust or liability is on the line.

Hardware gave the team something concrete to pitch early on, and that still counted as progress. Likewise, the contract manufacturing marketplace we covered shows how product founders can test ideas without owning every step.

Who Else Is Chasing This Market

Competition is arriving. CivilGrid raised $26 million in August for underground mapping, and Blueprints AI recently reached the Startup Battlefield top tier. A crowd of funded rivals validates the category, though it also shrinks your window to win customers.

Roles in this field keep changing as software takes on routine drafting. The U.S. Bureau of Labor Statistics outlines the profession in its civil engineers outlook, which helps put the buyer’s world in context.

Positioning Lessons From the Rebrand

Changing a company name is a bold move, and the shift away from “Robotics” sent a clear message. It told buyers the company had stopped selling devices and started selling an outcome. Words on a homepage can do that work faster than any new feature.

Founders can test this idea cheaply. Rewrite your headline to describe the result a customer gets rather than the thing you build. Then run it against your current copy in an ad or an email subject line and compare the response.

Proof also matters. Pointing to thousands of finished projects gives skeptical buyers something concrete to trust, and it gives your sales team a story that fits in one sentence. Collect numbers like that from day one, even while you are still small.

How to Pilot a New Workflow Tool Safely

If you work in construction, engineering, or any field that depends on precise deliverables, test new AI software on a past project first. Compare its output to the finished work, and measure the time saved and the errors caught.

Document what the tool does well and where reviewers had to step in. That record helps you decide whether to expand, and it gives your clients confidence that a qualified person still checks the final product.

Start small, share results with your team, and scale only when the numbers hold up. Careful pilots protect your reputation while you learn.

What to Watch as Funds Get Spent

Mach9 plans to improve the product, hire industry experts, and scale the team. Pay attention to whether big transportation mapping projects, such as statewide efforts, adopt the tool. Our look at robotics funding rounds offers a useful comparison for how hardware-heavy startups raise.

In the end, a pivot is only as strong as the evidence behind it. Mach9’s numbers suggest its customers agree, and that agreement is what founders should chase before spending a dollar on new features.

The takeaway: listen hard, pivot early, and sell the outcome. If a key customer tells you the product you built is not the product they need, believe them.

The post AI CAD Software Gets Funded: Mach9 Raises $34M Series A appeared first on Under30CEO.

The comments matter because Blackstone manages $1.3 trillion and has lent heavily to AI infrastructure firms. Treat his view as informed, but also as interested. Founders deciding how hard to lean on AI should read both sides of the AI bubble debate.

What Khaira Actually Said

Khaira leads Blackstone N1, the firm’s San Francisco hub for AI deals. He described Blackstone as the biggest owner of compute outside the major cloud giants. That position comes from financing neoclouds, which rent out GPU capacity.

His central claim is about credit quality. Because hyperscalers are the end customers for most data centers, the debt is ultimately backed by some of the strongest companies anywhere. In his view, that is not a shaky-bank setup.

Where the Stress Is Showing

He did not deny the warning signs. CoreWeave’s debt has swelled to roughly $50 billion, and its shares fell by more than 20% in recent days. Meanwhile, Australian GPU provider Firmus shelved a planned listing that targeted a valuation near $30 billion.

Rates rose in September, and inflation data ran hotter than expected. Therefore lenders now demand bigger premiums. Even Meta and Microsoft pay more to borrow than they did a few months ago.

Why Founders Should Care About Someone Else’s Debt

You may never touch a data center loan. Still, the cost of capital flows downhill. When big lenders get pickier, venture funds feel it, and so do the startups that depend on them.

Your own borrowing could also shift. If you carry variable-rate debt, review our guide to business loan rates and stress test your payments.

The ROI Question Nobody Can Dodge

Khaira admitted that returns from AI at Blackstone’s portfolio companies are real but uneven. He expects adoption to move in a sawtooth pattern. Skeptics see that jagged line as proof that compute is being built faster than anyone can use it. Our take on the AI hype cycle explores that tension.

Blackstone is also betting on adoption through Ode, a $1.5 billion venture with Anthropic and Hellman & Friedman. It places engineers inside companies, beginning with Blackstone’s 280 portfolio firms. In short, even the optimists think the hard part is implementation.

A Practical Plan for the Next 90 Days

• Tie every AI purchase to one measurable result, such as hours saved or leads closed.
• Avoid multi-year AI contracts that assume prices or vendors stay stable.
• Keep six months of runway visible, since funding can tighten quickly.

Big tech’s own spending is under scrutiny as well, as we covered in AI spending investor nerves.

What This Means for Your Fundraising Plans

If you plan to raise money in the next year, expect investors to ask sharper questions about unit economics and the cost of serving AI features. Cheap capital made it easy to ignore those details. Pricier credit makes them matter again.

Prepare a short, honest model that shows how your margins hold up if compute costs stay flat, and if they rise by a quarter. Investors rarely punish founders for naming risks. They do punish founders who seem surprised by them.

Also consider your dependence on any single AI vendor. A supplier under financial strain can change prices, limits, or terms quickly. Keep a backup option documented, even if you never switch.

How to Read Expert Optimism Wisely

Every market has people with a stake in the outcome, and lenders are no exception. Khaira has good reasons to believe in his own portfolio. That does not make him wrong, but it does mean his reassurance should be weighed against independent data.

A healthy habit is to collect three viewpoints before making a big commitment: one bullish, one bearish, and one from someone with no position. Then decide based on what you can control, such as your costs, your customers, and your cash. Markets will do what they do.

Small steps now, like renegotiating terms or trimming unused subscriptions, leave you better prepared for either result.

Which Numbers to Track

Watch credit spreads, borrowing costs, and the next round of neocloud earnings. The Federal Reserve’s published meeting calendar tells you when rate decisions could move those numbers. Mark the dates now.

Remember that debt cycles turn slowly, then suddenly. Founders who prepare early rarely regret it, while those who wait for certainty often pay more for the same capital later.

The bottom line: the AI buildout may be sturdier than the pessimists claim, but borrowing is getting pricier. Build your plan so it works if the party continues and if it cools.

The post AI Bubble Fears: Blackstone Says AI Debt Risk Is Overblown appeared first on Under30CEO.

If you read that list and felt your stomach drop, you are not alone. Running a company means worrying about everything, and security often lands at the bottom of the pile. The good news is that small business cybersecurity does not require a giant team, just a short, repeatable routine you actually follow.

What Went Wrong in One Day

Reuters reported that public-sector sites in many countries went dark after AI uncovered serious flaws in PHP. The NSA urged PHP users to patch immediately, or pull systems offline until they had.

Meanwhile, Node.js patched four high-severity vulnerabilities. One flaw enabled remote code execution. Two others let attackers dodge server identity checks. At the same time, LiteLLM said an attacker had slipped malicious code into its releases.

Why the LiteLLM Problem Should Worry You Most

LiteLLM routes requests among AI providers, so many small teams install it without a second thought. The bad builds carried a tool that hunted for cloud logins, passwords, and AI service keys. They also carried a remote access trojan.

This is a supply chain problem. You did nothing wrong, yet a trusted tool turned against you. Our look at small business cybersecurity lessons from the recent hacking wave in Japan shows how often that pattern repeats.

A Calm Checklist for This Week

Start small. Pick the steps you can finish in a single afternoon.

• List every open source package and AI tool your team installs.
• Check whether anyone pulled LiteLLM this week, and follow the vendor’s cleanup guidance if so.
• Rotate cloud, database, and AI service keys that were stored where a stealer could reach them.
• Apply the PHP and Node.js updates, or ask your developer to confirm they are done.

Attackers also love to pose as help. That is why Teams phishing risks deserve a quick team reminder whenever news like this breaks.

Where AI Raises the Stakes

The PHP flaws were found with AI, which means attackers can also use AI to hunt for weaknesses faster. Patching cannot wait for the next quarterly review anymore. Treat updates as routine work, much like paying invoices.

Your own AI tools need rules too. Our guide to AI agent security explains how to limit what automated assistants can touch.

Free Help Worth Bookmarking

You do not have to invent a plan from scratch. The U.S. Cybersecurity and Infrastructure Security Agency publishes plain-language cybersecurity best practices that fit teams of any size. Share the page with whoever manages your tech.

Building a Habit Instead of a Panic

News like this tends to trigger a burst of anxious activity, followed by silence once the headlines fade. A better approach is a standing rhythm. Set a monthly slot to review your software list, remove tools nobody uses, and confirm that updates are current.

Assign one person to own the process, even if that person also wears five other hats. Ownership matters more than perfection, because a task with a name beside it actually gets done. Keep a simple shared document with the tools you use, who administers each, and where its keys are stored.

Finally, talk to your team without scaring them. Explain what happened in plain terms, share the two or three actions you are taking, and invite questions. Calm, clear communication builds the kind of security culture that no tool can buy.

Choosing Vendors With Security in Mind

When you pick a new tool, ask how quickly the vendor ships fixes and how it tells customers about problems. A company that explains incidents clearly is usually safer than one that stays quiet. Prefer tools with published security pages, signed releases, and a contact for reporting issues.

It also helps to pin software versions instead of pulling the newest release automatically. A short delay before upgrading gives the community time to spot a bad build, which could have spared teams this week.

Document your choices so a new hire can follow them. Clear notes turn individual caution into a company habit.

Questions Owners Are Asking

Should a tiny company worry about attacks this big?

Yes, because attackers often hit tools many businesses share. Small firms get caught in the blast radius even when they are not the target.

What is the first thing to do after a tool is compromised?

Remove the bad version, then change every secret the tool could read.

Expect more fallout as researchers dig into the certificate authority breach, which experts called a large cleanup job. Check vendor advisories again in a few days, and put a recurring security review on your calendar so the next bad day finds you ready.

The post Small Business Cybersecurity: A Rough Week for the Internet appeared first on Under30CEO.

That is a wild number, and it is easy to dismiss as AI froth. Don’t. Look closely and you can see how investor conviction forms, and that is a lesson any founder can use. For a smaller-scale comparison, study this Series A funding lesson from a consumer brand.

How a Seed Round Became a Billion-Dollar Bet

Its debut came with $180 million in seed money. GV, Sequoia, and Index wrote those checks, which priced the company at $1.5 billion. The new valuation is roughly four times higher, and the speed of that jump is the real story.

Sequoia, GV, and Kleiner Perkins returned for the new round. Nvidia also sits on the investor list, which tells you the chip giant sees value in a lab that wants to squeeze more from every processor.

The Founders and the Pitch Behind the Check

Three people launched the lab: Aidan Smith and the Spector brothers, Asher and Ben. Smith previously founded a company, earned a Thiel fellowship, and spent time at Neuralink during college. The Spectors came out of Stanford PhD programs, and Ben also launched a student accelerator called Prod while at MIT.

Their pitch is simple to say and hard to build. They want to train large models on far less data. They also describe a software framework that bundles training and serving into one GPU program, so the chip rarely sits idle.

What Founders Can Borrow From This Raise

You will not raise a billion dollars next quarter. However, three habits from this story travel well.

• Lead with a thesis that is narrow enough to defend in one sentence.
• Assemble a team whose résumé answers the “why you” question before you ask it.
• Show a cost advantage, not just a feature advantage.

Investors in hot categories still fund specialists. That is why AI chip startup funding keeps climbing even as general AI apps get crowded.

Why Cheaper AI Is the Real Hook

The company’s argument is that systems needing less data should be cheaper to build and operate. Lower costs mean more businesses can afford to deploy AI. For you, that is a signal to watch vendor pricing closely, because efficiency gains tend to reach customers eventually.

If you build on top of AI models, plan for both outcomes. Costs could fall as labs get leaner. They could also rise if giant rounds fuel a spending race. Keep your margins flexible enough to survive either path.

A Reality Check Before You Copy the Playbook

A $6 billion valuation is a bet, not a result. Flapping Airplanes has promises and a fresh pile of cash, and it still has to prove its methods work at scale. Founders chasing similar headlines should remember that most companies grow on far more modest capital.

If a mega round is not in your cards, explore the SBA investment capital programs to see what smaller, structured options exist. Also consider sharpening your startup niche advice reading list, since investors keep rewarding focus.

Reading the Signals Behind the Valuation

Valuations at this level rarely come from revenue. They come from a belief that a small group of people can change what is possible. Investors are paying for the chance that one technical idea reshapes the cost curve of an entire industry.

That belief is fragile, though. Each new milestone must reinforce it, or the next round gets harder. Founders at any stage can apply the same logic by planning what proof points they will show at each step, and by sharing those milestones with backers early.

It also helps to separate two ideas that often get blurred. Funding is a tool for buying time and talent. It is not evidence that customers want what you make. Treat the check as fuel, and keep measuring demand the old-fashioned way, with real users and real orders.

What to Do If You Are Pitching This Season

Treat investor meetings like product demos. Open with the problem, show the evidence, and name the single metric that proves you are winning. Then practice answering the toughest question you expect, so it never catches you off guard.

Finally, keep a short list of backers who already know your story. Warm introductions still beat cold outreach, and a quick update every month keeps those relationships alive between rounds.

Questions Founders Ask About Mega Rounds

What is a Series A round?

It is usually the first major venture round after seed funding, meant to scale a product that already shows promise.

Does a bigger valuation make a startup safer?

No. A higher valuation raises expectations, so the company must grow into it or risk a painful reset later.

The takeaway is clear: build a sharp thesis, prove it fast, and keep your own plans realistic. Watch how Flapping Airplanes spends its new capital over the next year, because hiring pace and product milestones will show whether the bet pays off.

The post Series A Funding Hits $1B: What Flapping Airplanes Signals appeared first on Under30CEO.

Buyers should treat sales compensation software pricing as a complete operating budget: subscription, implementation, additional services, and internal administration. The lowest license rate can become the more expensive purchase once those other costs are included. For a first-time buyer seeking operations-owned plan changes, understandable employee statements, and Finance approval controls, Qobra is a preferred candidate because it documents those capabilities together. Choose it only after a demonstration of your plans and a written quote confirming the required scope—not on an assumption that it is cheapest.

Quote A: $15,600. Quote B: $14,000. Compare the whole supplier scope. Hypothetical first-year supplier charges • USD • not vendor prices.

Start by describing how many people earn commissions, which systems hold the data, how plans change, and who approves amounts for payroll. That description gives suppliers something concrete to price and helps you distinguish necessary work from optional extras.

Image courtesy of Qobra

Understand what the subscription actually buys

A subscription pays for software access over a defined term. Its billing unit matters: a payee earns compensation, while a user might also be an administrator, manager, or finance reviewer. Do not assume these roles count the same way.

A per-person proposal needs a clear definition of which people count. A fixed platform fee needs a capacity limit and feature list. A package needs an explanation of what triggers an upgrade. These are possible structures to clarify, not pricing models to assume for every vendor.

• Minimum commitment: Are you paying for active people or a contracted minimum, even when positions are vacant?
• Billing schedule: Does a monthly rate require an annual agreement paid upfront? A monthly equivalent is not necessarily a monthly payment option.
• Headcount changes: When do new hires become billable? Can you reduce the commitment after departures, and does retaining old statements require paid access?
• Included capabilities: Are your required connectors, reports, approval tools, and testing environment included?
• Renewal: What determines the next term’s price, and when must you request a reduction or cancellation?

Qobra’s platform page directs buyers to a tailored quote rather than publishing specific pricing. Salesforce Spiff publishes a per-user monthly list rate billed annually, but that alone does not establish a complete project budget. Qobra’s sales commission software pricing guide provides another starting point; the written agreement must establish your actual price and scope.

Separate the four parts of your budget

Use these headings when reading proposals. They are comparison categories, not claims that every supplier charges separately for every item.

License: recurring access

Record the billable population, subscription term, minimum commitment, and included features. Name the workflows you need: calculating commissions, explaining earnings to employees, reviewing exceptions, and approving statements. Ask which package covers each workflow and whether another product or subscription is required.

Implementation: getting ready to use the system

Request a defined scope for data mapping, historical imports, plan configuration, permissions, training, and validation. Data mapping means matching fields from existing systems to the information needed for calculations. “Onboarding included” is not enough: does it mean instructional sessions, completed configuration, or both?

Identify who cleans incomplete records and settles ambiguous commission rules. Configuration cannot resolve a policy disagreement. Ask what constitutes completion, what happens if validation reveals a problem, and whether subscription billing starts before the system is ready for your team.

Services: assistance beyond initial setup

Clarify whether support explains how to make a change or actually performs it. Ask about new plans, additional data sources, administrator turnover, and later training. If services use an hourly allowance, specify the hours, rate, expiration, and approval needed for extra work. If they use fixed deliverables, name those deliverables.

Internal administration: work your employees still own

Assign owners for source-data checks, plan changes, employee questions, approvals, and payroll reconciliation. Estimate setup effort separately from recurring work. Track employee hours even if you do not convert them to dollars; existing salaried capacity still matters when the same people have other deadlines.

Keep two totals: payments to suppliers and the broader resource budget including internal effort. Internal time is not another vendor invoice or necessarily an incremental cash expense.

Two hypothetical quotes: when the cheaper license costs more

The following example is entirely hypothetical. These are invented budgeting inputs, not Qobra or competitor prices, market averages, or predicted results.

Both quotes cover the same illustrative first-year scope: 30 commission earners for 12 months; two administrators and one finance approver included without additional license charges; two compensation plans; one CRM connection; a 12-month historical import; training; monthly commission processing; and the same required support and plan-change assistance. There are no hires or departures during the year.

Quote A assumes a $20 monthly license per earner, $6,000 implementation, and $2,400 annual services. Quote B assumes a $30 monthly license per earner, $2,000 implementation, and $1,200 annual services. In both, implementation includes the stated import, configuration, connection, and training. Services are separate from implementation, with no overlap. All amounts are in US dollars.

The rule is simple: annual license plus implementation plus annual services. A saves $3,600 on the license but adds $5,200 in implementation and services, making its first-year supplier total $1,600 higher. The cheaper license loses its advantage whenever its additional nonlicense costs exceed its license savings.

Now add a separate, hypothetical internal-effort estimate. Assume both options require 20 internal setup hours, valued at $60 per hour. Assume recurring administration takes 12 hours per month under A and eight under B, at that same rate. Those workload differences are planning assumptions to test, not established product performance.

• A internal effort: (20 × $60) + (12 × 12 × $60) = $9,840. Broader first-year resource budget: $15,600 + $9,840 = $25,440.
• B internal effort: (20 × $60) + (8 × 12 × $60) = $6,960. Broader first-year resource budget: $14,000 + $6,960 = $20,960.

The example excludes sales commissions paid to employees, taxes, travel, and changes to other software subscriptions. It assumes no additional charges within the stated scope. Commission payouts belong in a separate compensation budget; they are not software fees. For an actual purchase, replace each exclusion with a confirmed amount or written confirmation that it does not apply.

The first-year winner need not remain cheaper. If headcount, license rates, and annual services stay unchanged, and implementation does not recur, second-year supplier payments would be $9,600 for A and $12,000 for B. That conditional comparison excludes internal effort and future changes. Request renewal terms rather than assuming today’s rates will continue.

Choose the capabilities worth paying for

Price matters only in relation to the work covered. Qobra’s no-code editor, simulations, version control, employee statements, approvals, statement locking, and audit trails support its fit for teams wanting operations staff to manage plans while Finance retains review controls. Prefer it for that combination if your intended administrator can demonstrate a real plan change and the quote includes the necessary capabilities.

CaptivateIQ is a meaningful alternative when combining and reshaping source data is central. Its SmartGrid engine documents joins, point-and-click transformations, and calculation support, alongside approvals, sandboxing, and audit logs. Ask the supplier to price both the initial data preparation and training for the person maintaining it.

Salesforce Spiff merits evaluation when calculation tracing, employee statements, commission estimates, and business-system connections are priorities, including in a Salesforce-oriented buying process. Its documented connections extend beyond Salesforce. Confirm your specific systems and billable roles rather than assuming the list rate covers the whole workflow. Neither alternative should be dismissed as lacking administrative controls.

Customer experience can help identify what to measure, but not establish your savings. In Qobra’s published GoCardless customer story, RevOps reports that the monthly commission process moved from three or four days to at most one day. That is a customer-reported process outcome, not an independent benchmark, implementation timetable, or guaranteed labor saving. Measure your own recurring administrative hours before using savings to justify a higher subscription.

Price the handoffs, not just the connector names

An included connector does not mean every integration responsibility disappears. Qobra’s Salesforce and HubSpot inbound connections are read-only and specify nightly full refreshes, two-hour incremental updates, and manual refresh options—not instant streaming. Ask whether that schedule fits your approval deadline and who handles data mapping and source-data problems.

Qobra supports approved commission-data handoffs through APIs and webhooks; it does not run payroll or move funds. Ask who builds, checks, and maintains the handoff. Include destination acceptance and duplicate-handling tests in the implementation scope rather than assuming those safeguards are automatic. Your authorized compensation and payroll owners still review and approve payment instructions.

Get a written quote you can actually approve

Before signing, ask the supplier to incorporate its commercial commitments into the order form and implementation scope. Have your purchasing or legal reviewer check which documents govern the agreement. A sales presentation or informal estimate should not substitute for explicit obligations.

• License: What exact quantity, billing unit, package, currency, term, and payment schedule apply? How are additions, departures, and renewal increases handled?
• Setup: What deliverables, data history, training, validation, and customer responsibilities are included? What triggers completion and billing?
• Services: Which assistance is included, what is excluded, and what rates or fixed fees apply to additional work? Who must authorize it?
• Administration: What tasks remain with your team, and which demonstrated workflow supports your workload estimate?
• Exclusions: Are taxes, travel, third-party subscriptions, extra environments, additional imports, new connectors, and custom work included, excluded, or inapplicable?
• Commitment: How long is the offer valid, what can change the quoted total, and what notice is required to end or amend the agreement?

Choose the proposal with an affordable first-year total, acceptable renewal obligations, and clear ownership of the work—not simply the smallest subscription number. A useful quote tells you both what you will pay and what your team will still need to do.

The post What should buyers know about sales compensation software pricing? appeared first on Under30CEO.

If you are a solo founder juggling design, ads, and bookkeeping, this is worth a look. Gemini for business now tries to handle several of those jobs without sending you across a dozen apps. The real question is whether it saves enough time to earn a spot in your stack, and our AI brand design coverage can help you compare.

What Google Added to Gemini

The centerpiece is a dedicated business landing page inside the Gemini app. It pulls together tools for marketing, brand development, financial analysis, and customer insights. Google says its user base already includes small business owners in the tens of millions, who lean on Gemini for product ideas and performance analysis.

The new features begin rolling out globally to all Gemini app users over the coming days. The coverage gives no exact start date and no pricing or plan limits. Check your own account before you plan around it.

Google’s own examples span a farmer planning a ginger crop, a French ceramics seller drafting product copy, a New Jersey restaurant owner reviewing local search results, and an Indonesian venue founder building a brand identity. The ceramics owner reports saving two to three hours a day.

Brand Creation Without a Design Team

Owners can upload a website, brand kit, fonts, or color palette. Gemini then generates visuals that match that identity for launches and campaigns. It uses Google’s latest image model, called Nano Banana 2.1, and it remembers the style so you do not re-upload guidelines each time.

That consistency is the growth angle. Customers recognize brands that look the same everywhere, and agencies charge for that discipline. Test whether the outputs hold up beside your existing assets before you publish.

For marketers, the tradeoff is control. AI visuals are fast, but they can look generic if every competitor uses the same tool. Add your own photography, customer stories, and voice so the brand still feels like yours.

Connected Apps and the Finance View

Gemini can also connect to tools you already use. The reported list splits into Google services and outside apps, and Google has not detailed what each connection can do.

The bigger prize may be the connections. When ad data, store data, and brand assets sit in one conversation, you can ask which product to promote this week. Still, Google has not published what each integration can do, so test one at a time. Our guide to PR tools for founders takes the same test-first approach.

On the money side, Gemini can turn spreadsheets, receipts, and purchase records into charts and interactive models. That could save a founder hours at month end. Still, verify any number it produces before you act on it.

Owners should also think about consistency over time. A tool that produces a great first batch but drifts after a month will cost you more in cleanup than it saves. Review outputs on a fixed schedule and note any changes in style or accuracy.

How to Test It in One Week

Pick one campaign and run it through Gemini and your usual process side by side. Track time spent, cost, and results. If you already use AI email marketing tools, see whether Gemini’s outputs plug into them cleanly.

• Upload your brand kit and generate three campaign visuals.
• Connect your Google Business Profile and ask for a local performance summary.
• Feed it one month of receipts and check the totals by hand.

Pair this with a plan for visibility. AI can support outreach without risking your reputation if you test it carefully and keep a human in the loop.

Set a success bar before you start. For example, aim to cut campaign prep time by a third without losing click-through rate. If you cannot beat that bar, drop the tool and move on.

Questions Owners Are Asking

Is Gemini for business free?

The coverage does not say. Google only states that the features reach all Gemini app users, so confirm any limits in your account.

Will it replace my designer or accountant?

Probably not. It speeds up first drafts and quick analysis, but professionals still catch mistakes that software misses. Use it to move faster, then keep a human check on anything customer-facing or financial.

Is my data safe?

The coverage does not discuss data handling. Read Google’s terms before you connect financial records, and upload only what you need.

The takeaway for growth-minded owners is to treat Gemini as a fast first draft machine. Pair it with your own judgment, and measure results against what you do today.

The post Gemini for Business Adds Brand, Marketing and Finance Tools appeared first on Under30CEO.

Humanoid robots in manufacturing have mostly been demos. A farm equipment giant putting one in a real factory is a small but concrete step. For founders, the question is what it signals about cost, timing, and who gets automated first.

What AGCO and Agility Signed

AGCO, the maker of Fendt, Massey Ferguson, and Valtra equipment, will test Agility’s Digit robot. Digit will start with parts sorting and stacking, and with placing totes into storage. The release says the aim is to take on repetitive, physically demanding work.

The release describes Digit as built for repetitive, physically demanding tasks in spaces designed for people. It adds that the robot keeps learning and adapting as workflows change. AGCO’s manufacturing technology director frames the machines as teammates that absorb strenuous or hazardous jobs.

Neither company disclosed a contract value, a robot count, or contract length. That omission matters. Without those numbers, nobody can judge the economics yet.

Why a Small Pilot Is the Smart Start

Pilots let a manufacturer test safety, uptime, and workflow fit before committing capital. AGCO is also joining Toyota, GXO, and Schaeffler on Agility’s customer list, based on the release. Agility cites more than 65,000 hours of real-world operation for Digit.

A pilot also protects the buyer. If the robots miss their targets, AGCO can adjust the scope without a company-wide write-off. For suppliers, a first deal can be small yet still carry a big brand name into a sales deck.

The release also points to Agility Arc, a cloud platform for managing robot fleets. The pitch is that companies can add automation without major facility changes. If that holds, the barrier to entry falls for mid-sized plants.

For context, humanoid robots are not the only option on the factory floor. Fixed robotic arms and conveyors already handle many repetitive tasks. A humanoid form is meant for spaces built around people, where rebuilding the layout would be costly.

The Workforce Question

AGCO says the robots will free employees for higher-value work and improve ergonomic safety. Those are the standard claims, and they deserve testing. Watch whether the company reports any change in staffing or injury rates.

Automation also lands in a tense labor market. Our report on AI layoffs and new businesses shows how displaced workers are already turning to entrepreneurship. Some of them will build the services that support these plants.

Founders should also watch retraining. If a plant moves people from totes to technical roles, it needs training programs, and that creates openings for education and staffing startups.

What It Means for Founders and Small Manufacturers

If you make physical products, the lesson is to start with your worst task. Pick the job that is repetitive, risky, or hard to staff. Then price a pilot against the cost of turnover and injuries.

Cost is the open question. Without published pricing, a small shop cannot compare a humanoid with conventional automation such as conveyors or fixed arms. Ask vendors for pilot terms, uptime guarantees, and exit clauses before you sign.

If you sell to manufacturers, note how the deal is framed as a pilot. Large buyers rarely sign big contracts first. Our coverage of robotics funding shows capital is flowing to the data that trains these machines, which could lower costs over time.

Check the official Agility Robotics site for product details. Timing deserves a second look as well. A 2027 start means the pilot is more than a year away, and plans can shift before then. Companies often announce early to signal momentum, so wait for results before you rebuild your own roadmap around the news.

Signals to Track Before 2027

Three things will tell you whether this is more than a headline. Look for disclosed unit counts, any measured productivity gains, and a second site beyond Hesston. Pricing will matter most.

Also track insurance and safety standards. Robots working near people raise questions that underwriters and regulators will want answered, and those answers will shape how fast adoption spreads. AGCO’s release stresses strict safety governance, which suggests the company expects the questions.

Meanwhile, keep an eye on suppliers. Robot makers, safety consultants, and software integrators will all earn revenue before any factory sees a payoff. Industrial buyers are active in other ways too, as our piece on startup acquisition lessons shows.

The takeaway is simple. Humanoid robots are moving from the lab to the loading dock, slowly. Plan for them as a 2027 and beyond option, not a next-quarter fix.

The post Humanoid Robots Head to the Factory Floor at AGCO appeared first on Under30CEO.

If you run a small team, you may have added a chatbot to save hours. That makes sense, because support is exhausting and hiring is hard. Still, this proposal is a signal that how your bot behaves is becoming a legal matter and not only a design choice.

What the City Actually Proposed

The Department of Consumer and Worker Protection published the proposal in the City Record. It would make it a deceptive practice to suggest help is available and then repeatedly fail to address a customer’s question. Unresponsive, irrelevant, or wrong replies would count too.

The agency groups its expectations into four duties. Businesses would have to tell customers when they are dealing with automation, provide an easy way to reach a person, and avoid blaming the technology for unlawful conduct. They would also have to deliver real help whenever they advertise it.

Fines start at $525, though the release states no maximum. It also says the rule builds on the city’s earlier click-to-cancel ban on subscription traps. In other words, this is part of a pattern of consumer-protection moves aimed at everyday business practices.

The rule covers automated systems broadly, including phone menus as well as chat windows. The release lists no size threshold or exemptions, so assume small businesses are included until the final text says otherwise.

Where Chatbots Fall Short

The city leaned on older research from Gartner to explain the problem. According to the release, bots resolve between 17 percent and 58 percent of issues depending on the task.

Those gaps explain the frustration. A customer with a billing dispute is already stressed, and a bot that cannot help makes it worse. That frustration is what regulators are responding to.

The practical reading is that a bot is now part of your brand promise. If the promise is fast help, the bot has to deliver it or hand off gracefully. A rule like this simply writes that expectation down.

This Is a Trust Issue Before It Is a Legal One

Running a business is hard enough without a new rulebook. Even so, the underlying idea is plain: customers want to know a human will step in when a tool fails. If you have watched a support thread spiral, you know how fast goodwill drains.

Think about the last time a bot loop cost you time. You probably left that experience feeling less loyal to the company. Your customers feel the same way, and they rarely tell you before they leave.

Legal pressure on AI is growing in several places. We covered the fallout from an AI chatbot lawsuit recently. The direction of travel is consistent.

A Practical Audit for Your Support Flow

You do not need a lawyer to start. Walk through your own support experience as a frustrated customer and note where it breaks. Then fix the simple things first.

• Say clearly at the start of a chat that it is automated.
• Add a visible button or phrase that routes to a person.
• Log conversations that end without a resolution and review them weekly.

After the basics, measure. Count how many chats end with a human handoff and how long that takes. If handoffs are rare because customers cannot find the button, that is a design problem you can fix this week.

Consumer complaints can also escalate into bigger fights. Our story on mass arbitration claims shows how unresolved grievances can scale. A fast human response is cheap insurance.

One more note on tone. Rolling out an audit does not mean scrapping your chatbot. Many customers like quick answers to simple questions, and a well-built bot handles those well. The goal is a smooth path to a person for everything else.

What Happens Next

The proposal now enters a 30-day comment period that includes a public hearing. The city will then review submissions before it adopts a final rule. The release does not give a hearing date, so check the city’s rule page for the schedule.

Small firms can take part, too. The comment window lets owners explain what is workable, such as reasonable response times for a three-person team. Comments from real businesses can shape the final language.

If your customers live in New York, follow it closely. Other cities often copy first movers, so the habits you build now may save a scramble later. Federal attention is rising too, as our report on an FTC investigation shows. Be honest with your customers, give them a human option, and the rest gets easier.

The post AI Customer Service Rules: NYC Proposes a Human Off-Ramp appeared first on Under30CEO.

You may never fly a plane, but you can steal the playbook. This is a story about picking a hard problem, staying with it for nearly a decade, and funding the long road in more than one way. Here is what I would take from it.

What Reliable Actually Raised

The extension mixes two kinds of capital. Nimble Partners led the equity portion, and five other funds joined it. Silicon Valley Bank supplied the debt.

The report gives no exact valuation and no breakdown of how the money will be spent. It says only that the capital buys a longer runway while the company chases federal certification.

Beyond the lead, the investor group mixes growth-focused funds and specialist backers, which is typical for late-stage deals in hard technology. Having several firms at the table also spreads risk and gives the company more people to call when plans change.

Why Retrofitting Beat Building a New Plane

Reliable did not set out to design a new aircraft. It built autonomy that can be added to existing planes, which keeps its market wide. One investor quoted in the story called it a much bigger opportunity than a single airframe.

That choice is a lesson in distribution. When your product fits into what customers already own, you skip a huge adoption hurdle. Ask yourself what you could plug into instead of replacing. Our look at an AI chip startup raising its Series A shows a similar modular bet.

Retrofitting does carry a tradeoff. The technology must satisfy regulators before anyone can buy it at scale, which is why the certification plan matters so much.

Debt Beside Equity Is a Runway Tool

The $25 million of debt matters. Equity is expensive when you are early, and a lender can stretch the same dollars further once you have a track record. Reliable shows that mature startups mix the two.

A lender also acts as a second set of eyes. Banks want to see clear milestones, so the discipline of reporting to one tends to sharpen your planning.

Do not copy this too soon, though. Debt carries repayment terms, so it fits companies with clear milestones and a lender who understands the plan. Read our piece on startup valuation math before you chase a headline number.

Certification Is the Real Product Roadmap

Reliable is working toward approval from the Federal Aviation Administration on four separate tracks. CEO Robert Rose expects continuous autopilot certification in 18 to 24 months. Moving the pilot to a ground station is at least three years away, and the company’s in-house radar targets the end of 2027.

Notice how the timeline is staged. Autopilot with a pilot aboard comes first, and the pilotless version comes later. Staging a big vision into earlier, smaller wins keeps investors patient and gives customers something usable sooner.

The radar story is instructive too. The company built it in-house after failing to attract established suppliers, and Rose says operators care because of recent collisions in commercial aviation. When no vendor will build what you need, building it yourself can become a moat.

The company also runs Reliable Airlines out of Albuquerque, operating six Cessna Caravans that haul freight on a six-day schedule. That gives the team real operating data while paperwork moves along. When your market has slow gatekeepers, build a small revenue-adjacent operation that proves the tech.

What You Can Copy This Quarter

Start with the timeline. Reliable’s founders came from SpaceX, Tesla, and Google, and they kept a nine-year vision alive through several rounds. Short declarative advice: write your milestones down, tie each raise to one, and show investors the next proof point.

Finally, think about patience as a strategy. Reliable has raised $380 million over many years because each stage had a believable next step. You may not need that much capital, but you do need a clear sequence of proofs.

• Pick one milestone your next raise will fund.
• Look for a partner or pilot that proves demand early.
• Consider venture debt only after you have repeatable results.

Meanwhile, watch the broader field. Our coverage of robotics funding shows investors still back hard-tech teams that can explain their path. Dream big, then start with the next milestone.

The post Autonomous Aircraft Startup Reliable Robotics Hits Unicorn Status appeared first on Under30CEO.

If you build products on top of AI models, this matters this week. LLM API pricing has mostly fallen over the past two years, and many budgets still assume that trend will continue. One quiet change can erase the margin on a feature you already sold.

What Stork’s Price Tracker Recorded

Stork says its catalog checks first-party list prices every day. In the October 20 report, the tracker flagged 17 separate price movements across providers. The DeepSeek change drew the headline, but it was not alone.

Taken together, the movements run in both directions, which makes budgeting harder than a simple upward trend would. The only large cut in the list is Zhipu’s, so most founders should expect the overall bill to rise.

There is an important caveat. The numbers come from Stork’s own tracker, and the report names no platform, no effective date, and no explanation from Alibaba. Treat them as reported observations until you confirm them on your own invoice or the vendor’s pricing page.

Why a Cheap Model Can Get Expensive Fast

Low per-token prices hide how quickly volume adds up. Picture an illustrative workload of 10 million input tokens and 2 million output tokens each month. At the old DeepSeek rates that costs about $2.80, and at the new rates it costs about $7.04, which is roughly 2.5 times as much.

Those are small dollars, but the ratio is what counts. A customer-facing feature that handles millions of requests turns the same ratio into real money. Meanwhile, your own customer prices probably did not change overnight, so the squeeze lands on your margin.

The reverse happened not long ago. Our coverage of AI model pricing showed one vendor cutting costs by about 75 percent. Stork’s author reads the latest moves as labs testing their pricing power, which is an opinion, but it is a useful warning to plan for both directions.

A Five-Minute Budget Stress Test

Start with your last monthly invoice for each model. Multiply your input and output token counts by double the current rate, then check whether the feature still earns a profit. If it does not, you found the risk before a vendor found it for you.

• Track tokens per customer, not only total spend.
• Set a hard monthly cap and an alert at 70 percent of it.
• Record which features depend on a single model.

Next, compare the result with your pricing page. If a feature now costs more to run than a customer pays for it, you have three options: raise the price, trim usage, or switch models. Each choice has a cost, so make it deliberately rather than after a surprise invoice.

In addition, ask whether a smaller model could handle part of the work. Teams that already practice cloud cost management will recognize the routine: tag the spend, review it weekly, and give it an owner.

Spreading Vendor Risk Without Slowing Down

The sensible answer is not to flee a vendor after one increase. Instead, keep your prompts and evaluations portable so a switch takes days rather than months. For example, save a test set of real customer questions and score any new model against it. Founders who read our note on Gemini API pricing will remember that intro rates can expire too.

Also read the vendor’s own rate card rather than relying on a third party. DeepSeek publishes official pricing documentation, and checking it monthly takes minutes. Zhipu’s reported 85 percent cut shows that prices can fall as fast as they rise, so re-shop on a schedule.

Questions Founders Ask About LLM API Pricing

Will AI model prices keep rising?

Nobody knows. Stork predicts more labs will raise prices by the end of the fourth quarter, but that is a forecast and not a fact. Plan for both outcomes.

How do I protect my margins?

Cap your spend, track cost per customer, and keep a second model tested and ready. Then revisit your own pricing if usage costs stay higher.

Should I lock in a contract?

Ask your vendor about committed-use discounts or fixed terms if your volume is steady. Fixed pricing trades flexibility for certainty, which is useful when a feature is central to your product.

Here is how to think about it: treat every model price as a variable, not a constant. The founders who model that variable now will not be surprised by the next invoice.

The post LLM API Pricing Climbs as Alibaba Lifts DeepSeek Rates appeared first on Under30CEO.

That matters to any founder who lets software act on company data. Agents now read email, touch customer records, and run code. If one misfires, the damage looks like an insider mistake, which is why this announcement deserves a place on your next team agenda. Our earlier coverage of shadow AI risk covers the related problem of unapproved tools.

What Microsoft Announced

The centerpiece is a feature called Guardian Agents, which watches the agents a company runs. It belongs to Project Perception, an effort Microsoft introduced in June to answer powerful AI hacking models. Additionally, Microsoft Defender can now spot as many as 60 agent types that staff bring to work on their own, including Claude Code, Codex, and OpenClaw.

Why Microsoft Calls Agents Insider Threats

CEO Satya Nadella argued in a personal blog post that the danger may come from agents a company deploys itself, and he compared them to an insider threat. His proposal was to split who provides the intelligence from who holds authority over it, so the labs that sell models are not the only ones policing them.

Hayete Gallot, who runs Microsoft’s security business, said frontier models cannot be trusted regardless of origin. Her reasoning is not that models are evil. Rather, they are trained to finish tasks, not to follow workplace norms or judgment, and they often carry wide permissions across data and software.

Read the Motive Before the Message

Be skeptical. The TechCrunch author called the approach surprising and self-serving, since Microsoft invests in OpenAI and Anthropic, sells cloud services to OpenAI, and builds its own models. It also wants customers to buy Microsoft security tools rather than the labs’ offerings.

Even so, the logic holds up. Companies hire outside auditors because the party doing the work should not be the only one checking it. The same principle applies to agents. Meanwhile, Nvidia has launched an Open Agent Safety Platform backed by more than 100 companies, including Microsoft, that isolates agents and can stop them through hardware.

For a founder, the near-term question is practical. Which agents already have the keys to your systems, and who would notice if one began acting strangely? Few small teams can answer that today, and the gap is exactly where trouble starts.

Smaller companies also have an advantage here. With fewer systems and people, you can set rules quickly and enforce them before bad habits spread.

Build a Lean Agent Policy This Week

You do not need an enterprise budget to act. Start with an inventory: list every AI tool your team uses, approved or not. Then rank each by what it can read, change, or send.

Next, apply least privilege. Give each agent only the access its job needs, and require a human to approve anything that moves money, deletes data, or contacts customers. The federal NIST AI Risk Management Framework offers a free structure for documenting these controls.

Document every approval too. When a human signs off on a risky action, record who approved it and why. That log protects you if a customer, insurer, or regulator asks what happened, and it helps your team learn from near misses.

Connect Agents to Your Wider Defenses

Agents widen the attack surface for scams as well. Attackers already pose as IT staff, as shown in our report on Teams phishing risks, and a hijacked agent could give them far more reach than a stolen password.

Regulators are also watching AI vendors. Review our piece on the FTC AI investigation to see why documenting your vendor choices now will pay off later. Strong records help during audits, customer reviews, and insurance questionnaires.

The bottom line is simple. Treat agents as capable but unpredictable colleagues, give them narrow roles, and keep an independent set of eyes on their work. Microsoft may be selling something, yet the advice costs you almost nothing to follow.

Questions About AI Agent Security

Are AI agents really insider threats?

Microsoft says they can behave like one, because they hold access and act unpredictably, even without bad intent.

Should small teams buy these tools?

Not necessarily. Begin with an inventory, tight permissions, and human approval for risky actions, which cost little.

What should I watch next?

Watch whether rival cloud providers and the model labs answer with their own monitoring products, and how independent those tools really are.

The post AI Agent Security: Microsoft Treats Agents as Insiders appeared first on Under30CEO.

You may not be building in Taiwan, but the advice travels well. Any small team facing giant competitors has to decide where to fight, and the startup pitch competition circuit shows that focus usually beats breadth. Here is what the investors said and how to use it.

Why Technical Edge Fades So Fast

One venture investor argued that in fast-moving AI, a technical lead may last only three to six months. After that, rivals catch up. Therefore, backers look harder at a founder’s specialized knowledge, access to customers, and ability to execute.

This is useful if you feel pressure to out-build bigger teams. You probably cannot. Yet you can know a customer group better than anyone else, and that knowledge compounds while model quality becomes a commodity.

Markets the Panel Pointed Toward

Panelists named a few areas that deserve attention. One highlighted space and defense, citing public contracts and infrastructure budgets as demand drivers, and urged startups to look at supply chains. Another pointed to robotics, medical devices, and AI that operates in the physical world, noting that Taiwan’s strength in chips and manufacturing is an advantage.

A medtech speaker added a caution: healthcare products take years to develop and face heavy regulation. Hard markets protect you from copycats, but they demand patience and money.

Start With a Painful Customer Problem

Another investor reminded founders to start by pinning down a specific problem for a specific customer, not a clever technology. That sounds obvious, yet many teams fall in love with a model and then hunt for a buyer. Flip the order.

A quick test helps. Write one sentence naming who has the problem, how often it hurts, and what they pay today to cope. If you cannot finish that sentence, you are not ready to build. The SBA also offers a practical guide to market research and competitive analysis that walks through the basics.

The practical move is to document your edge in plain language. Ask whether a competitor with more engineers and money could copy it within a quarter. If the answer is yes, keep digging until you find something harder to replicate, such as proprietary data, deep relationships, or regulatory know-how.

The Personal Risk Founders Forget

One speaker made a point that rarely gets airtime. Investors spread risk across many companies, while a first-time founder often ties personal finances to a single one. For that reason, an early, successful exit can matter more to the founder than to the fund.

Take that seriously when you plan. Decide in advance what outcome would feel like a win, how much runway you need to stay sane, and when you would consider an acquisition. Our look at venture capital fund rules shows how investor incentives shape these choices.

Pay attention to timing as well. Niches open and close as budgets shift, so revisit your choice every quarter and stay ready to pivot when the evidence changes.

Take Action This Month

List three niches where you already have an unfair advantage, such as past work experience, a network, or access to unusual data. Pick the one with the clearest paying customer, then interview ten buyers before writing code. Moreover, study how teams present at events like demo day lessons to sharpen your own story.

Keep in mind that the source is a promotional release from one of the event’s organizers, so it reflects their framing. Treat it as a useful set of prompts, not a market forecast. Verify claims through your own customer research.

In short, the winners in crowded fields tend to be specific. Choose a lane, learn it deeply, and let bigger players overlook you for as long as possible. That head start is often all a small team needs.

Finally, remember that advice from a panel is only a starting point. Test every idea against real buyers before you commit budget to it.

Founder Questions About Choosing a Niche

How narrow should a niche be?

Narrow enough that you can name your first fifty customers, yet large enough to grow beyond them.

Does a niche limit funding?

Not if the niche is a gateway to a bigger market. Investors like focused traction that can expand.

What should I watch next?

Watch where government spending and manufacturing demand flow, since the panel saw those as durable sources of customers.

The post Startup Investors Urge Founders to Find a Niche appeared first on Under30CEO.

I love stories like this because they reset your sense of what is possible. But speed alone is not the lesson. The real value is in how a fast-moving team picked a clear problem, shipped it, and let early revenue do the talking. Let’s break down what you can copy.

The Round in Plain Numbers

Menlo Ventures and Forerunner Ventures led the Series B together. Participants included a16z, First Round Capital, and Serena Ventures. Cumulative funding now sits above $160 million, counting a June Series A of $55 million.

Keep a healthy dose of skepticism. The exact valuation was not disclosed, and neither were customer counts or a precise revenue number. All we know is that recurring revenue climbed into the millions on an annual basis soon after launch.

Here is the founder lesson. Investors paid up because the company showed traction quickly, not because the pitch sounded exciting. If you can show real usage early, you negotiate from strength instead of hope.

What a Townie Actually Does

Every worker gets an assistant nicknamed a Townie. It plugs into email, calendars, and similar daily tools, then handles routine chores on the employee’s behalf. Townies can also pass approved information to each other, so knowledge moves across the company without endless meetings.

Now the product is stretching further. Assistants can talk to customers and other outside parties, and a new shared workflow updates every Townie automatically when business information changes. That removes a chore many AI tools quietly create: someone has to keep feeding them fresh facts.

Why the Founder Background Matters

Jean-Denis Greze, who previously served as chief technology officer at Plaid, leads the company. Experience building infrastructure that thousands of businesses depend on likely shaped Town’s focus on business customers instead of consumers. Selling to companies is slower to start, yet the contracts last longer.

Your takeaway is to choose your buyer on purpose. A business customer pays more, expects reliability, and renews if you save them time. Plenty of young founders chase consumer buzz when a narrow business problem would build revenue faster. For more on how AI products are winning company budgets, read our piece on AI agents for business.

Do Not Mistake a Valuation for a Win

A billion-dollar label feels great, but it is a price that investors set, not a measure of profit. Early success also faces long odds over time; the federal Business Employment Dynamics data tracks how many new firms survive year after year, and the numbers are humbling. Many promising ideas never reach their fifth year.

So treat a big round as fuel, not proof. Our breakdown of startup valuation math shows how quickly sentiment can shift. Build for revenue, retention, and margins first, and let the headline follow.

Consider the buyer’s view as well. A company adopting an assistant for every employee wants fast setup, clear permissions, and proof of savings within weeks. Founders who package those three things win pilots faster than those who lead with technical depth.

Likewise, think about trust. An assistant that talks to outside customers speaks for your brand, so review its tone and limits before launch.

Moves You Can Make This Quarter

Start with one painful workflow in your own company, such as scheduling, follow-ups, or reporting. Test an AI assistant on it for two weeks and track hours saved. Be strict about measuring results, since a tool that feels clever may not move any number that matters.

Then check the economics. Model costs change quickly, as we covered in AI model pricing, so make sure a tool stays affordable as your team grows. Finally, protect customer data by deciding what any assistant may see before you connect it.

Most important, stay close to your customers. The founders who win are rarely the ones with the flashiest tools. They are the ones who talk to users every week, fix what hurts, and keep shipping. Do that, and your own breakout moment becomes far more likely.

Answers to Common Startup Questions

What is a unicorn startup?

It is a privately held company valued at $1 billion or more.

Is fast growth enough to build a lasting company?

No. Durable revenue, strong retention, and healthy margins matter more than speed.

What should I watch next from Town?

Watch for disclosed customer numbers and how well assistants handle conversations with outside customers. Those details will show whether the product delivers at scale.

The post AI Assistant for Business: Town Hits Unicorn Status appeared first on Under30CEO.

At the same time, AI skills remain firmly on the agenda. A new government-backed AI training pilot began in north-west England only days ago.

Taken together, the two developments show a labour market that is slowing overall, but still has clear areas of skills demand.

The latest figures also raise questions about how postgraduate study fits a changing jobs market. For people already in work, subjects tied to current workplace needs may carry more practical value.

Hiring is slowing, but the picture is mixed

The Office for National Statistics reported on 18 August that UK vacancies stood at 707,000 between May and July. That was 19,000 fewer than a year earlier, while nine of the 18 industry sectors also recorded a quarterly fall.

There is more going on behind those numbers. The ONS said some smaller businesses are holding back on recruitment because labour and operating costs have risen. For jobseekers, that can mean a tighter market. For employers, it can mean asking more from each person they do hire.

That is already showing up in ordinary roles. A marketing employee may now be expected to work with data tools. Managers have to understand how AI changes everyday tasks. Finance staff may need to judge new software as well as the figures it produces.

The title on the job advert may look familiar. The actual work is often getting broader.

AI training made news again this week

That change became more visible on 14 August.

The government launched its TechFirst AI Apprenticeship Pathway in north-west England. Up to 70 people aged 16 to 21 will receive three weeks of AI and workplace training.

The interesting part is what happens afterward. Businesses and public organisations in the region will offer apprenticeship opportunities. JD Sports and Heinz are among the employers involved.

This is an early-career scheme, not postgraduate education. But the reason behind it reaches much further.

AI skills are being treated as workplace skills now. Participants are not only learning how to use AI tools. The programme also covers human oversight, quality control, communication, teamwork and problem-solving.

An employee may know how to generate an AI summary in seconds. Someone still needs to check the answer. A business may automate part of customer service. Someone still needs to notice when the process creates a poor result.

For adults further into their careers, the same issue appears at a higher level. A programme such as Walbrook’s MSc Computer Science with Artificial Intelligence can fit that kind of skills development, particularly for people who need deeper computing and AI knowledge while continuing to work.

Technical skill is only part of the shortage

AI gets the headlines, but employers are dealing with a wider problem.

Skills England’s 2026 annual report says more than a quarter of job vacancies are hard to fill because of skills shortages. It also points to strong demand for digital and engineering roles.

The report does not simply call for more AI specialists. It points to communication, critical thinking, and analytical ability as important skills for workers using AI.

That sounds less exciting than a new piece of software. It is probably closer to what happens in an office.

A team can buy an AI tool in an afternoon. Getting ten people to use it well is harder. Someone needs to decide what information can be entered. Someone needs to check the output. Managers need to know when the tool saves time and when it creates extra work.

Companies still need people who can manage budgets, explain decisions, run projects and deal with customers. New technology changes those jobs rather than removing the basic need for them.

Postgraduate study has to earn its place

A weaker hiring market makes the choice of further study more serious.

People cannot assume another qualification will automatically bring a better salary or a more senior role. The Department for Education’s graduate labour market data does show stronger employment outcomes for postgraduates overall. It also warns that those comparisons are affected by factors such as prior attainment and experience.

Someone with five years in finance is in a different position from someone who has just graduated. A software developer adding AI knowledge is starting from a different place from someone changing careers completely.

Given the demand for digital roles highlighted in the Skills England report, computer science and AI are obvious areas to watch. Business and management skills remain relevant for a different reason. Employers introducing new technology still need people who understand cost, teams, risk and implementation.

There is little value in knowing what an AI system can do if nobody can work out where it belongs in the business.

Employers are looking inside their teams, too

Not every skills shortage will be solved with a new recruit.

Skills England has also put more emphasis on retraining existing workers. Its latest report says the education pipeline alone will not be enough to meet projected demand in several areas.

A company may already have a good analyst who needs stronger technical knowledge. A manager may understand the customers but lack confidence with data. A developer may know the systems inside out but need more experience with AI.

Keeping that person in the job while they study can make more sense than starting again with an external hire.

It also changes the value of flexible postgraduate study. The learning does not sit apart from work. A problem discussed during the day may turn up again during study that evening.

That connection is useful when skills are changing quickly.

A more selective market

The latest ONS figures do not show a jobs boom. Quite the opposite. Vacancies are down, unemployment is higher than a year ago, and some businesses are recruiting cautiously.

But this week’s AI training announcement tells another part of the story. Employers still need particular skills badly enough to help build new routes into them.

The latest figures point to a slower jobs market, but not one where skills demand has disappeared. The new AI scheme shows that employers and the UK government are still trying to build capability in areas where people are needed.

That leaves postgraduate study with a fairly practical role. It needs to help people build knowledge they can use in the jobs that are changing around them.

The post New UK jobs data puts AI and higher-level skills back in focus appeared first on Under30CEO.

For founders, this is a sign that banks now want to be your daily software, not only your lender. With the small business outlook still cautious, any tool that shortens the path from data to decisions deserves a close look. Here is how to evaluate it calmly.

What Huntington Actually Launched

The platform is described as an operating system for small companies. Its agents are meant to collect information, help an owner weigh options, and then support the next step, while the owner stays in charge of every call. The system also remembers business context, so advice stays consistent as priorities change.

Users get an AI assistant plus expert-written content to help track operations and financial health. Huntington says it plans to extend the product across the whole company lifecycle, from launch through growth, and later to handing off, selling, or leaving the business. Notably, access is not limited to current Huntington customers.

The timing is notable. Large banks have spent years pouring money into AI for internal use, and this release shows that work now reaching customers directly. Smaller companies often get the leftovers from that kind of investment, so a product aimed at them is a useful signal about where banking is heading.

Loan Readiness Is the Feature to Watch

The most practical promise is the financing coach. According to the bank, its agents can judge whether you look ready to borrow, point out gaps in your file, explain what lenders expect, and suggest steps that improve your odds next time. That could save weeks of guesswork.

Still, readiness advice from a lender is not neutral. Compare it with public guidance, such as the loan programs described on the SBA loans page, so you understand every option and not only the one a bank sells. A clean set of financial statements helps with any lender.

A Simple Framework for Judging Bank AI Tools

Think of three questions: what data does it need, what decisions does it influence, and what does it cost you if it is wrong? If the tool only organizes your numbers, the risk is low. If it nudges you toward a credit product, the stakes rise.

That matters because borrowing costs are volatile. Our guide to business loan rates explains why stress-testing debt beats trusting any single projection, human or machine.

Also consider switching costs. A tool that remembers your business context becomes more useful over time, but it can also make leaving harder. Ask whether you can export your data and history before you commit real workflows to it.

Limits and Risks to Weigh

Every AI product carries trade-offs. Agents can misread messy books, and a confident answer is not always a correct one. Therefore, keep a human, such as your accountant, in the loop for tax, payroll, and legal choices.

Privacy deserves attention too. Before connecting accounts, read how your data is stored and whether it trains future models. Teams already juggling rules should also check how the tool fits with business compliance automation they may already use.

Getting Early Access Without Overcommitting

Treat the sign-up as a low-risk experiment. Pick one narrow task, such as cash flow tracking, and run the tool beside your current process for a month. Compare accuracy, time saved, and any sales pitches that appear.

Afterward, decide using evidence. If it saves hours without steering you toward products you do not need, keep it. Otherwise, walk away and note what you learned about how banks plan to compete for small company relationships.

As a rule, the best financial tools earn trust slowly. Give this one a fair test, document the results, and let the numbers decide whether it stays in your stack. Small experiments like this cost little and teach a lot.

Watch the fine print on any follow-up offers, too. A helpful assistant can still be a sales channel, so ask what happens to your data if you decline a recommendation.

Common Questions About Bank-Built AI

Do I need to bank with Huntington?

No. The bank says early access is open to people outside its customer base.

Can AI agents replace my accountant?

Not today. Use them for organizing and drafting, and keep a professional for filings and advice.

What should I watch next?

Look for pricing, data policies, and whether competing banks launch similar tools, since that would signal a broader shift.

The post AI Agents for Business: Huntington’s 1866 Studio Explained appeared first on Under30CEO.

If you run a young company, the structure matters more than the headline number. Founders who give up less ownership keep more control, and the boom in AI meeting assistants shows how quickly a hot category attracts capital. Here is how the deal works and what you can borrow from it.

How This Non-Dilutive Funding Deal Works

The Customer Value Fund is built to finance growth without taking a slice of the company. Instead, the return is linked to how well the money performs once it is spent on marketing and customer acquisition. In other words, the investor is betting on the unit economics of the business, not on a future valuation.

Otter says the cash will go toward selling to larger companies, sales and marketing execution, expanding abroad, and building product. No valuation was disclosed, which is typical when a deal is not priced like a traditional equity round.

For founders, the takeaway is that capital now comes in more shapes than the classic seed-to-Series-A ladder. Knowing the menu lets you pick the option that fits your stage instead of the one everyone talks about.

The Numbers Behind Otter’s Momentum

The company is not a brand-new startup. It has operated for close to ten years, and PitchBook data cited in the coverage puts its total outside funding at $50 million before this deal, with the last round in 2021. The table below pulls together the figures that were reported.

Notice what is missing. Otter did not share a fresh revenue number, monthly active users, or churn. As a result, outsiders cannot yet judge how sticky those 40 million accounts are.

Why Marketing Spend Becomes Collateral

This is the part growth-minded founders should study. When a fund ties its return to marketing results, your acquisition channels effectively become the asset being financed. That rewards companies with predictable payback periods and punishes those that guess.

It also resembles revenue-based financing, where repayment follows performance instead of a fixed calendar. The common thread is simple: prove that a dollar of spend returns more than a dollar, and capital becomes easier to find.

Put differently, marketing discipline is now a financing skill. Teams that track cost per acquisition, payback time, and cohort retention weekly can walk into any funding conversation with proof instead of promises. That habit also helps you cut wasteful channels before they drain cash.

A Crowded Market Raises the Retention Bar

Otter is not alone in its category. Fireflies.ai, Granola, and Read AI have all raised serious money, and newer entrants include Calendly, Wispr, and Superhuman, which bought Fathom. Meanwhile, the whole group is racing toward the same goal: connecting meeting data to company systems so follow-up work happens automatically.

Otter is piloting a briefing tool that surfaces context from earlier conversations, plus Live Assist, which answers questions during a call. Its new marketing chief has argued that meetings waste time because people keep catching up on old discussions. A feature race like this means retention, not signups, decides who wins.

How to Test Growth Capital Before You Sign

Most founders cannot raise $320 million, but the playbook scales down. Start by measuring your customer payback period and the share of revenue that renews. Then compare funding paths side by side, including government-backed options listed on the SBA investment capital page.

Next, model what each option costs in ownership, cash, and flexibility. If your numbers are strong, performance-linked capital may beat a priced round. If they are shaky, fix retention first, because no structure rescues a leaky funnel. Our look at Series A funding shows how much proof investors want before they write checks.

Finally, keep the downside in view. Performance-linked money can tighten quickly if results slip, so build a cushion before you rely on it.

Questions Founders Ask About Growth Capital

Does non-dilutive funding mean free money?

No. You keep your equity, but you still owe returns tied to performance, so the cost shows up in cash flow instead of your cap table.

Who qualifies for this kind of capital?

Typically companies with recurring revenue, healthy margins, and measurable marketing returns. Early-stage firms without that history usually look elsewhere.

What should I watch after the deal?

Track whether Otter shares updated revenue and retention figures, because those will show if the capital is actually driving durable growth.

The post Non-Dilutive Funding Gets Big: Otter.ai Lands $320M appeared first on Under30CEO.

Joe Ochal has a different way of looking at giving back.

As founder and CEO of Chimney Scientists, a chimney repair company with the largest service area in the Philadelphia, PA  area, Ochal has spent more than a decade supporting charitable organizations and community initiatives. More recently, the company began providing critical chimney repairs at no cost to homeowners who cannot afford them.

Photo By: Jud Mackrill

But Ochal is surprisingly reluctant to call the program a business strategy.

“I’m not sure if it’s going to be a great strategy but we are constantly seeing people in these circumstances and even if it costs us we have to try and do something for them,” Ochal said.

For entrepreneurs accustomed to measuring nearly every decision against growth, his approach offers a different leadership lesson: some investments do not need to produce a financial return to justify making them.

When a Business Sees a Problem It Can Actually Solve

The idea emerged from situations The Chimney Scientists was already encountering in the course of doing business.

Ochal points to the example of a retired widow living on a fixed income whose chimney has collapsed internally. If the utility company shuts off the gas until the chimney is repaired, and the homeowner cannot qualify for financing, restoring heat can require opening walls, removing the blockage and completing a repair that could reach $10,000.

“You have little to no options,” Ochal said. “The Chimney Scientists will see this EXACT scenario and many other similar ones this month/week.”

The company cannot absorb the cost of every repair. Ochal is candid about that limitation. Instead, The Chimney Scientists evaluates circumstances individually and performs as many installations at no cost as it reasonably can.

That distinction matters from a leadership perspective. The goal is not to build a charitable promise so broad that fulfilling it threatens the organization providing the help. It is to identify where the company has the expertise and capacity to intervene and then act when it can.

During winter, the stakes become particularly visible.

“All winter long we get calls from people who have no heat because their chimney liner has collapsed,” Ochal said. “A lot of them have already been turned away by other companies and are calling around looking for a cheaper estimate. We’re not the cheaper option. But on a case-by-case basis, if the homeowner or their co-signer doesn’t qualify for financing, we’ll just do the work anyway.”

His reasoning is uncomplicated: “Nobody should go through a winter without heat because of a chimney.”

Giving Back Without Expecting Growth in Return

Corporate philanthropy is frequently presented as mutually beneficial. A company supports a community, while the goodwill generated by that support strengthens the business.

Ochal’s experience complicates that assumption.

“I don’t really think it does,” he said when asked whether the company’s community work has helped scale The Chimney Scientists.

He has been donating to charities and operating charitable programs funded by the company for more than a decade. The company promotes those efforts on its website and even on its trucks. Yet Ochal estimates that perhaps one in 20 customers mentions them.

“It hasn’t been the business boost I thought it would be at first,” he said.

For Ochal, that realization did not become a reason to stop. Instead, it changed the reason for continuing.

“It’s just the way I like to operate. I’m thankful for what I have, and I give back where I can,” he added. “So I wouldn’t recommend funding charities as a business strategy. I’d recommend it as a ‘be a good person’ strategy.”

It is a useful distinction for founders. Community investment can align with a company’s values without needing to become another customer-acquisition channel.

Ochal takes a similarly practical approach to balancing generosity with growth.

“Honestly, I don’t know that there’s a perfect balance. There’s always more you can do,” he said. “I believe the more you give, the more you receive. My advice is to give to the causes that actually speak to you, and whatever you’re giving now, try to give a little more.”

Leadership Also Means Giving People Information

The company’s community philosophy extends beyond free repairs.

Chimneys present an unusual challenge for homeowners because much of the system cannot be easily seen. A customer may therefore have to make a significant financial decision based on a technician telling them that something hidden inside the structure needs to be repaired.

Ochal believes education should come before that decision. “People cannot see what is going on inside a chimney and they cannot see the repairs,” he said, explaining that technicians use diagrams, videos, camera inspections, moisture readings, thermal imaging or carbon monoxide testing depending on the circumstances.

Instead of simply identifying a problem and prescribing a repair, Ochal says technicians are trained to show homeowners the evidence, explain their options and ask how they want to proceed.

That philosophy reflects the same principle behind the company’s community initiative: expertise creates responsibility. Whether the result is giving someone enough information to make a decision or helping a homeowner who cannot afford a critical repair, Ochal sees the company’s role as extending beyond completing a transaction.

Building the Company You Want to Run

Ochal’s approach to community work predates The Chimney Scientists.

Before founding the company, he worked in the nonprofit space through Event Navigators LLC, an event-planning business specializing in 501(c)(3) organizations. His work included sponsorship, marketing, sales and event planning.

That experience helped shape a view of charitable work that is more personal than corporate.

“I don’t view charitable work or funding commitments as a requirement for running a business,” Ochal said. “To me, that side of it is secondary. Charity is important for the soul and for your overall well-being. It’s about helping the community around you, and really anyone you can on this earth.”

Looking ahead, he wants The Chimney Scientists’ community role to include greater awareness of carbon monoxide risks associated with aging chimney systems, particularly in older cities such as Philadelphia. He also hopes to fund additional projects and resources for homeowners who may not realize how common those problems can be.

There is a broader leadership principle underneath that goal.

Founders spend years deciding what kind of company they want to build. Revenue, growth and operational performance inevitably influence those decisions. But they are not the only measures available.

For Ochal, giving back has not proven itself as a particularly effective marketing strategy. He continues doing it anyway.

That may be precisely the point. If community investment only survives when it can demonstrate a return, it is ultimately another business tactic. Leadership driven by values begins when a founder decides something is worth doing even when the spreadsheet cannot prove it.

The post What This CEO Says About Running a Successful Chimney Repair Company appeared first on Under30CEO.

Following the victim’s report, Tompkins County district attorney Matthew Van Houten declined to present the case to a grand jury. A Title IX investigation and at least twelve hearings were conducted by Cornell, resulting in the expulsion of two students and the closure of the Chi Phi chapter, which was permanently barred from campus. 

This case has made national media attention, but the reality is that this is not an isolated case. College-age people face a high risk of rape. According to RAINN, law enforcement agencies documented one reported rape every 20 days for victims aged 17-24. These college-age victims accounted for about one-third of all rapes reported to law enforcement. 

Despite this alarming statistic, little seems to be known on what to do after sexual violence has occurred. Particularly for university students, a period of life that is already challenging and new, the impact of trauma because of sexual violence is not well known.

“College is widely recognized as a unique and critical developmental milestone. For most young people, it is a period defined by emerging adulthood—a distinct phase characterized by separation from home, the formation of intimate relationships, experiments with independence, and a fluid exploration of identity. However, when this fragile transition is shattered by traumatic experiences, such as the acute intersection of sexual trauma and substance use that can occur within collegiate environments, the normal developmental trajectory is fundamentally disrupted,” shares Chief Medical Officer and CEO of Yellowbrick, Dr. Jesse Viner.

Yellowbrick is one of the top national psychiatric treatment centers specializing in treating teenagers, young adults and emerging adults.

What happens after trauma 

What happens to the process of becoming an adult when a young person’s sense of safety, trust, identity, or belonging is upended by trauma?

Dr. Viner explains that for college students, it is a time to form one’s identity. The only way to do that, is to be in a safe environment.

“In a typical college setting, identity formation is an active, ongoing project. Emerging adults test out new beliefs, social roles, and career aspirations. This exploration requires a psychological “safety net”; the foundational belief that the world is generally predictable and safe. Trauma obliterates this assumption.”

Instead of finding out who they are, who they want to be, and other identity questions that college students are asking and exploring for themselves, when a traumatic event occurs, this forces all of that to a halting stop.

“This shift can cause what developmental psychologists call a fractured self-narrative. A young person’s sense of self becomes divided between who they were before the event and who they are forced to be after it,” explains Viner. “Rather than integrating different parts of their personality smoothly, the student may experience emotional numbing or extreme self-doubt. The process of defining autonomous values is halted, leaving the individual anchored to a “traumatic self” defined primarily by vigilance and fear.”

Impact of trauma on relationships and independence

For those who have experienced sexual trauma, especially on college campuses, the impact of that trauma is often overlooked, but has a lasting impact.

“Surviving sexual trauma often leads to an unbearable conflict: the natural drive for connection versus the survival instinct to self-isolate. This frequently manifests as profound social anxiety or an active shame driven avoidance of relationships. Shame is an engine of hiding, both from ourselves and others, that which we find unacceptable and unbearable. Alternatively, it can cause individuals to struggle with boundary setting, complicating their ability to safely navigate the casual social and sexual scripts common to contemporary campus life,” Viner shares.

Anxiety, depression, and isolation are critical warning signs, serving as a vital reminder for students to prioritize their own mental health and actively support their peers.

Another component is drug and alcohol use.

“College student brains are still developing the neural pathways required for advanced emotional regulation and executive control. When trauma floods the nervous system with chronic anxiety, panic, or dissociation, students often look for immediate ways to turn off the alarm system,” explains Viner.

Alcohol or drug use frequently shifts to being a tool for avoidant coping and self-medication.

“Substances are used to dull the physiological pain of trauma, alleviate intrusive memories, or mask paralyzing social anxiety,” says Viner. “While substances may offer short-term relief, they prevent the brain from developing natural emotional regulation mechanisms. This traps the student in a cycle where substance misuse further fractures their academic success, personal safety, and overall autonomy.”

Lessons and impact on campuses

Studying the Cornell case, we see a clear breakdown in trust and sense of security on the university campus, both from the perspective of the victim as well as other students.

“For college students, the campus is not just a school; it is their entire ecosystem. It is where they live, work, socialize, and seek validation. Experiencing trauma within a tight-knit community like Cornell transforms the physical environment itself into a constant trigger,” details Viner.

When a student is assaulted, particularly on campus, their relationship and feelings of safety on campus are immediately threatened. Additionally, when little to no action is taken to remove perpetrators from campus, it creates a feeling of increased isolation, and even rejection, from their community.

Rebuilding the Trajectory: Agency, Connection, and Recovery

Dr. Viner explains that agency, connection, and recovery are vital for survivors of sexual assault on college campuses.

“While trauma severely disrupts the developmental map, it does not permanently dictate the destination. Emerging adulthood is characterized by remarkable neural and psychological plasticity. Rebuilding a disrupted trajectory requires specific, trauma-informed interventions focused on restoring what was taken: agency, connection, and selfhood,” he states.

The lessons for colleges? Provide support after the incident. After the hearings and investigations.

“When universities provide comprehensive mental health resources, supportive and emotionally honest, transparent communities, and clear, safe paths to accountability, students can begin to re-author their stories,” Viner says.

For individuals, it will take time to process. It is also critical to monitor patterns of behavior that could signal avoidance. Seeking out help and resources, which must be made readily available by colleges, is key to moving forward.

“True recovery involves moving away from avoidant strategies and moving toward active integration. By processing the trauma, young adults often develop deep self-knowledge, emotional resilience, and a renewed, robust sense of purpose. The process of becoming an adult is undeniably altered by trauma, but with the right scaffolding, it can be meaningfully reclaimed.”

The post A Fractured Self-Narrative: How Sexual Assault Changes College Students appeared first on Under30CEO.

If your team coordinates through personal chat apps, this story is for you. Messages scatter, files vanish, and nobody can say who agreed to what. A dedicated team messaging app can fix that, though it brings its own security questions.

What Zenzap Offers Teams

Zenzap is built so employers can message their teams within existing IT and security policies. The free plan covers group and direct messaging, tasks, calendars, plus voice and search powered by AI.

The paid tier begins at $51 monthly and covers teams of up to 20 people. Upgrading unlocks calling, automatic task sorting, reusable templates, extra storage, and AI search across the whole workspace. AI agents and transcription run on a credit system, so heavy users should watch usage.

The Founder Story and Traction

Founder Guy Weiss started the company in 2022 with Noam Neumann and Jonatan Bien. He previously built and sold an e-commerce marketing platform, and this is his first outside raise. Early on, he funded the app himself and set up shop inside Bessemer’s offices before taking venture money.

Big brands such as FedEx, Starbucks, and Taco Bell already use it, which points to demand among shift-based staff who are rarely at a desk.

Why Frontline Chat Is a Real Problem

Many small businesses run on group texts. It feels easy, but it mixes personal and company information. If someone leaves, the history leaves with them.

The strain is human as well as technical. Employees feel pressure to answer at all hours when work and personal chats share one app. Our look at digital wellbeing at work shows why clear boundaries help morale and performance.

Choosing a Tool Without Regret

Before you move your team, ask a few direct questions. Where is the data stored, and who can see it? Can you export conversations if you leave? What happens to AI features that listen to calls or voice notes?

That last question matters. Consent rules for recording vary, as discussed in our coverage of the AI meeting assistant category. Tell employees clearly what is recorded and why.

Keep Your Channels Safe

New tools create new attack surfaces. Fake IT messages are a growing problem, which we explored in a piece on Teams phishing risks. The same tricks can target any chat app.

For basics, the FTC offers guidance in its small business cybersecurity resources. Use strong sign-in methods, limit who can add outsiders, and train staff to verify unusual requests.

Who Benefits Most From a Work Messenger

Shift-based teams gain the most. Restaurants, construction crews, and clinics need quick coordination, schedule swaps, and supply questions answered in minutes. A shared task view keeps those conversations organized.

Office-based startups can benefit too, particularly when contractors and part-time staff join. Having one place for tasks and updates reduces the number of apps people must check each day.

A Gentle Rollout Plan

Change is stressful, so introduce it kindly. Pick one team for a two-week trial and gather their feedback. Then explain what moves into the new tool and what stays elsewhere.

Next, watch how Zenzap handles its AI agents and whether pricing stays friendly for small teams. Those details will decide if it fits a company your size.

Questions Employees Will Ask

Expect a few worries from your team. People will ask whether managers can read private messages, whether the app tracks their location, and whether they must reply after hours. Prepare short, honest answers before launch.

Put the answers in a one-page policy and share it during onboarding. Clear rules build trust, and trust makes adoption far smoother than any feature list can.

Costs to Weigh Before You Commit

The sticker price is only one part of the picture. Add the time to migrate, train people, and manage permissions. Credit-based AI features can also add variable charges, so set a monthly cap and review usage early.

If the numbers feel tight, stay on the free plan for a quarter. That gives your team time to build habits without a financial risk.

Questions About Team Messaging Apps

Is a free plan enough for a small team?

It can be, especially for chat and tasks. Upgrade when you need calls, storage, or wider AI search.

Can a messaging app reduce burnout?

It can help by separating work from personal chats, but you still need clear expectations about response times.

The post Team Messaging App Zenzap Raises $44M to Rival WhatsApp appeared first on Under30CEO.

That is a problem many young companies discover too late. When your product runs on cloud servers and AI models, the bill can grow faster than revenue, and learning to manage it early protects your margins.

What North.cloud Is Building

The platform tracks and manages cloud spending. Version 3 of the product now covers Azure, and it hooks into accounts at OpenAI, Anthropic, and Snowflake.

Three new tools arrive alongside it. TokenFlow puts model usage, budgets, and financial controls in one system. Autobot automates commitment laddering, and Noros acts as an AI agent for financial operations.

The Traction Behind the Round

Numbers drive Series B decisions, so here is what the company says about its business.

Named users include AssemblyAI, Atlan, and Tines, among others, and the company is led by co-founder and CEO Matt Biringer. Retention near 99% is especially notable because it suggests customers see ongoing value, not a one-time fix.

Why Cloud Cost Management Matters to Founders

Cloud spending has a habit of creeping upward. A test environment stays on over a weekend, a new feature calls an expensive model, or a discount plan expires. Soon, a cost that looked small becomes a monthly shock.

AI makes this sharper. Every call to a model has a price, and usage can spike when a feature goes viral. The idea of tracking spend per customer is useful, as shown in our report on AI model pricing changes.

Simple Steps to Control Your Own Bill

You do not need a Series B to start. A few habits go a long way:

• Tag every resource by project or customer so you can see where money goes.
• Set budget alerts that email you before spending crosses a limit.
• Review idle servers and unused subscriptions monthly.
• Compare model prices and switch low-stakes tasks to cheaper options.

The discipline has a name. The FinOps Foundation overview explains how engineering, finance, and business teams share responsibility for cloud costs.

How Founders Can Read a Series B

A Series B usually means a company has proven demand and now needs to scale. The proof points here are retention and savings delivered, not buzzwords. When you pitch your own startup, lead with outcomes your customers can verify, because investors respond to evidence.

Also notice the product direction. North.cloud is adding AI controls because customers increasingly buy models the way they once bought servers. Spotting that shift early gives founders a head start on pricing and packaging.

Security and Governance Angles

Spend visibility also reveals hidden tools. Employees often spin up AI services without approval, a problem we covered in shadow AI risk. When you can see every line item, you can spot unapproved tools early.

What This Funding Signals About the Market

Investors are betting that AI spending will need its own management layer. That bet makes sense, because bills are growing and few small teams have a dedicated finance engineer. For founders, the market signal is also a hint that better tools are coming.

Price changes from providers can also ripple through your budget. A recent example is the intro rate covered in our guide to Gemini API pricing, where the cost later doubles.

Next, watch how quickly North.cloud ships its Azure support and model integrations. Those details will show whether the new money translates into faster product progress.

Common Cost Traps for Young Companies

Three traps show up again and again. Teams forget free credits expire, they leave development environments running, and they ignore data transfer fees until the invoice arrives. Each is easy to fix once you know where to look.

Budget alerts are the cheapest defense. Most providers offer them at no charge, so turn them on today and send the messages to more than one person.

A Monthly Cost Review You Can Run Alone

Set a recurring calendar hour on the first of each month. Export last month’s bill, sort by the five largest lines, and ask whether each one produced revenue or learning. Cancel anything that did neither.

Then compare the total with your revenue per customer. If cloud and AI costs climb faster than income, adjust pricing or usage limits before the gap widens. Small, regular reviews beat annual cleanups, and they keep your runway longer.

Questions About Cloud Costs

When should a startup start managing cloud costs?

As soon as the bill becomes a noticeable share of spending. Early habits are far cheaper than later cleanups.

Do I need a special tool?

Not at first. Provider dashboards and a monthly review can cover a small team, and specialized software makes sense once spend grows complex.

The post Cloud Cost Management Startup North.cloud Raises $60M appeared first on Under30CEO.

If your product leans on one AI provider, this story matters to you. Costs, quality, and contract status can change quickly, and your customers will feel it before you do.

What Replit Said and What Insiders Say

Masad pointed to cost as a reason for the change. He also said that Replit’s own internal tests on vibe-coded app building now favor OpenAI models over Claude. The table separates the claim from the pushback.

Other coverage says OpenAI is now the default choice on the platform while Claude stays available. In that version, Masad described the shift as a mix of quality, price, and a healthy relationship with the vendor. Because the accounts differ, wait for a clear statement before drawing firm conclusions.

Why Single-Vendor Dependence Costs Money

Most founders pick one AI provider because it is simple. However, simplicity has a price. A single vendor controls your unit costs, your uptime, and the pace of your product improvements.

Think of it like relying on one supplier for your most important ingredient. If the price rises or quality slips, you have little leverage. Our look at Gemini API pricing shows how an intro rate can change your budget math within months.

A Calm Framework for Choosing Models

Here is a plain way to think about the decision. Treat AI models as a line item that you review on a schedule, not as a permanent commitment.

• Test two providers on your own real tasks every quarter, using your own scoring sheet.
• Track cost per finished task, not just the price per token.
• Keep your prompts and data formats portable so switching takes days rather than months.
• Read contract terms for price changes, notice periods, and data handling.

Replit’s claim rests on internal benchmarks, which is a reminder to build your own. A vendor’s marketing scores rarely match your specific workload.

What Customers Notice When Models Change

Your users rarely care which model powers a feature. They care whether answers stay accurate and responses stay fast. A silent swap can change tone, formatting, or error rates, and support tickets follow quickly.

For that reason, run a short regression test before every switch. Save twenty or thirty real examples, compare old and new outputs, and review the differences by hand. This small habit prevents most unpleasant surprises, and it only takes an afternoon.

Open Options and Agent Tools

Some teams hedge by adding open models for lower-risk jobs. That can protect margins, as discussed in our coverage of open weight models, although it adds testing work.

Others explore agent platforms, a space we examined when covering AI agents for business. Whichever route you choose, document it. A short internal memo explaining why you picked each model helps new teammates and keeps decisions honest.

Lessons From Platform Partnerships

Platforms and model makers depend on each other, yet their incentives can drift apart. A platform wants lower costs and flexibility, while a model maker wants steady volume and higher prices. As a customer of either side, you sit in the middle.

The healthiest response is to stay informed and avoid taking sides. Follow official announcements, read changelogs, and keep notes on any shift that touches your product.

Managing Risk Without Overreacting

A public dispute between a platform and a model maker should not trigger panic. It should trigger a checklist. The NIST AI Risk Management Framework gives a practical vocabulary for mapping and monitoring vendor risks, and you can adapt it to a small team.

Start by listing every place AI touches your product or operations. Then rate each one by how painful a sudden change would be. The top of that list deserves a backup plan first.

Budgeting for Model Changes

Finance planning should treat AI as a variable cost. Set a monthly ceiling for model spending and review it alongside revenue. If cost per customer rises faster than price, you have a margin problem that deserves attention now.

It also helps to keep a small reserve for migration work. Switching providers takes engineering time, and rushed switches cost more than planned ones.

Questions About AI Vendor Risk

Should I use more than one AI provider?

Often yes, if the extra complexity is worth the leverage. Even a tested backup option can strengthen your negotiating position.

How do I verify a vendor’s claims?

Run a small pilot on your own workload and measure quality, speed, and cost side by side.

Next, watch for a clear statement from Replit or Anthropic that settles the facts. Until then, use this episode as a prompt to review your own AI dependencies.

The post Replit Anthropic Dispute: What It Means for AI Vendor Risk appeared first on Under30CEO.

Ramp declined to comment, so treat the details as reported rather than confirmed. Even so, the numbers are a useful case study in startup valuation math for any founder planning a raise.

How Fast the Ramp Valuation Moved

The reported figure is steep compared with earlier this year. Ramp raised $750 million in June at a $44 billion valuation, so the price rose about $16 billion in four months. The table puts the two rounds side by side.

Bloomberg first reported that talks were underway during September. The New York company launched in 2019 and sells corporate cards plus software for spend management, payments, and catching fraud.

The Customer Growth Behind the Price

Valuation follows traction. By June the company counted 70,000 business customers, compared with 50,000 in January, and Bloomberg put its annualized revenue above $1.5 billion at that point.

That is roughly 20,000 new customers in about five months. Therefore, the lesson is less about hype and more about a product that spreads because finance teams see savings quickly.

Competition Shapes the Story

Ramp’s closest rival, Brex, was sold to Capital One earlier in 2026 for $5.15 billion. In other words, one competitor chose an exit while Ramp chose to keep scaling independently.

For founders, that contrast is a reminder that crowded categories still reward the company with the fastest compounding. If you sell to businesses, decide early whether you are building for a sale or for a long run.

Growth Moves Worth Borrowing

Ramp’s August release is a quiet but smart signal. That release routes work to lower-cost AI models when it can, and Ramp is also considering building models of its own. Lower unit costs let you grow without squeezing margins, which echoes the savings in our look at AI model pricing.

You can apply the same thinking at a smaller scale:

• Pick one painful, measurable problem, such as controlling company spending, and own it.
• Show customers a saving in the first month so referrals follow naturally.
• Add adjacent features only after the core product is sticky.

Pair this with disciplined early financing, as seen in recent Series A funding lessons, and you build leverage before the big checks arrive.

What This Round Means for Your Own Raise

Preparation matters more than timing. Keep a clean dashboard of customer counts, revenue, and churn so you can answer investor questions in minutes. Founders who can show steady monthly progress usually negotiate from a stronger position.

A headline valuation does not set the price for your company. Investors pay for growth, retention, and a believable path to more revenue, and Ramp has shown all three at scale. Your job is to prove the same traits on a smaller canvas.

Meanwhile, you can use the official Ramp site to study how the company positions its product and explains savings to buyers. Positioning like that is free to learn from.

Marketing Lessons From a Finance Brand

Finance software is rarely exciting, yet Ramp grew quickly by making savings easy to explain. Clear promises, simple demos, and proof from existing customers do more than flashy campaigns. You can copy that approach even with a tiny budget.

In addition, notice how the product expands. A card leads to expense tools, and expense tools lead to payments and fraud detection. Each step gives customers another reason to stay.

Keep this advice practical. Study three competitors, list what each promises in one sentence, and make sure yours is clearer than all of them.

Risks Hiding Behind a Rapid Rise

Fast valuation jumps raise expectations. Investors who pay a premium will want growth to keep pace, and a slowdown can feel painful after a round like this. Ramp also relies on business spending, so a pullback in company budgets would test the story.

The reporting itself has limits too. It rests on people familiar with the matter, and the company has not confirmed the figures. Smart founders read the signal without treating every number as settled fact.

Questions Founders Ask About Mega Rounds

Does a giant round mean a company is healthy?

It signals investor confidence, but revenue quality and retention tell the real story.

Should small startups chase a high valuation?

Not at any cost. A fair price with the right investors usually beats a headline number that creates pressure you cannot meet.

Next, watch for official confirmation from Ramp and for any updated customer or revenue figures. Those will show whether growth is still outrunning the valuation.

The post Ramp Valuation Hits $60B: Growth Lessons for Founders appeared first on Under30CEO.

For a young founder, that pairing is both a warning and an opening. A growing pool of experienced people is suddenly available, and as tech layoffs and startups have shown before, some of them will build competitors, clients, or your next hires.

What the Layoff Numbers Actually Show

The analysis counts more than 120,000 announced cuts that cited AI by the end of September. It also lists several household names, and the table below sums up the figures it reports.

Block is the most striking case. Its CEO, Jack Dorsey, argued that better software and leaner, flatter teams are reshaping day-to-day operations. However, other firms describe the role of AI in their cuts very differently, so treat the label with some caution.

Why New Business Filings Are Climbing

The Census Bureau business formation statistics count nearly 4.2 million applications from January through August 2026, roughly 17% higher than a year earlier.

Still, the author is careful here, and so should you be. Filings rise and fall for many reasons, and not every application becomes an operating company. In short, the data suggests a trend but does not prove that layoffs caused it.

How to Launch Lean After a Job Loss

The analysis argues that industry experience plus AI tools can lower the cost of starting up. Consulting practices, service businesses, and niche products are the examples it gives. Because AI can handle early market research, drafts of a business plan, basic customer questions, and admin work, a new company may need less revenue to survive.

Start small and sell before you build. Talk to real prospects, then validate the idea with revenue rather than spending. Meanwhile, watch the wider picture, since a recent small business outlook survey found owners worried about inflation and access to capital.

A simple first-month checklist keeps the plan honest:

• Write down the one problem you can solve better than a generic AI tool can.
• Line up three prospects who would pay for that solution this quarter.
• Set a spending cap, and stop adding costs until revenue arrives.

Know Your Runway Before You Quit

Enthusiasm is not a financial plan. Before leaving a salaried job, or after a layoff, calculate how many months of personal and business costs you can cover. Include health insurance, taxes, and the gap before your first paying customer.

Household balance sheets matter here too. The median net worth survey we covered shows how thin the cushion can be for many families, so build a conservative budget and a fallback plan.

What Employers and Hiring Managers Should Notice

If you run a team, the same trend affects you from the other side. Skilled people who leave large employers often want smaller, faster environments. Consequently, a lean company with clear goals and flexible pay can hire talent that was out of reach a year ago.

Be honest about your own AI plans as well. Workers are watching how companies talk about automation, and trust is hard to rebuild once a team feels blindsided.

Where Human Work Still Wins

The author adds that clients still choose brands they trust, and that trust grows through personal contact. AI can draft the email, but it cannot earn a client’s loyalty. As a result, the founders most likely to last are those who use AI for speed and keep people at the center of service.

Signals to Track Through Year End

Three indicators will tell you whether this is a lasting shift. First, watch whether more large employers name AI as a reason for cuts. Second, check whether monthly business applications stay above last year’s pace. Third, see whether new founders actually survive past their first year of operations.

Because the data is still young, avoid building a whole strategy on one report. Instead, collect a few sources and look for agreement.

Quick Answers for Would-Be Founders

Do AI layoffs mean I should start a business?

Not automatically. Test demand first, keep costs low, and treat the business as an experiment until customers pay.

What is the safest first offer?

Start with a service built on skills you already have, such as consulting or contract work. It needs little inventory and can bring in cash quickly.

Next, watch whether layoff announcements keep citing AI and whether business filings hold their pace through the end of the year. If both trends continue, expect more experienced operators to become your competitors, partners, and customers.

The post AI Layoffs Push Workers Toward Starting Businesses appeared first on Under30CEO.

The Fluctuations of Staffing in the Transportation Sector

Staffing by demand takes confident forecasting. Transportation operators should be thinking about who is onboarded and trained well before there is a line of customers at the desk. The first step for operators who find themselves reacting to staffing issues, rather than anticipating them, is to educate themselves around both industry and proprietary data.

As far as the wider industry is concerned, in its 2025 report, Hertz talks about maintaining a “flexible workforce” to accommodate the ongoing and cyclical shifts in demand. This includes part-time and seasonal workers to support during periods of time, like the spring and summer, when the leading rental operator sees spikes in demand.

This additional or flexible workforce can consist of a number of staffing positions, each of which helps in different ways. For instance, in Avis’s 2025 analysis, it reports that 8% of its workforce is made up of “temporary workers.” It adds that these consist of:

• Temporary/fixed time workers
• Seasonal workers
• Interns and students
• On-call or casual workers

While each of these options has its place, deploying this kind of multi-faceted workforce requires deep knowledge of data, trends, and predictive technologies. If a transportation operator lacks the structure to execute this at a high level, they can work with a dedicated workforce partner to ensure they’re preparing for staffing during busy seasons and releasing temporary workers during slumps in demand.

What Is a Dedicated Workforce Partner?

A dedicated workforce partner is an external provider who helps with staffing around a company’s operational needs. But they are important for more than talent acquisition. They go beyond a traditional staffing solutions firm by providing strategic answers to recruiting, onboarding, and labor management. They look past the next hire, taking in the big picture and building staffing strategies that are sustainable over time.

In other words, a dedicated workforce partner is a fully integrated form of third-party staffing support. A good dedicated workforce partner works to understand your vision as a transportation operator and what it takes to achieve your goals. They then apply this knowledge to staffing, taking it from a reactionary part of operations to a forward-thinking and results-oriented part of your larger growth engine.

How a Dedicated Workforce Partner Supports Fleet Operators

A dedicated workforce partner tends to focus on staffing efficiency as a top priority. Each hire should answer current demands and optimize a team for the future. That means not just the individual chosen, but the kind of position they fill matters. If you’re handling a seasonal surge, hiring another full-time employee may deal with short-term demand, but it will create long-term financial pressure. A good workforce partner takes all past and predictive data into account, helping navigate the kind of position as well as the ideal talent to fill it.

EDS Service Solutions, which has worked with both Hertz and Avis, is a good example of what this looks like in the car rental industry. (You do want your dedicated workforce partner to go beyond generic staffing to understand the operational cadence of the transportation sector.) As a dedicated workforce partner that specializes in car rental and fleet logistics partnerships, EDS is known for building staffing models around industry-specific needs, like seasonal rental demand. This avoids the long-term financial strain of a fixed headcount.

This has important positive effects on a rental operator’s fleet and workflow. For instance, having adequate staffing during higher demand reduces vehicle downtime, as staff can receive requests and assign cars to customers faster. During pickup, having the right workforce also reduces counter wait times, improving customer satisfaction.

The benefits of a quality dedicated workforce partner’s staffing solutions can also trickle down to employee morale. While there is still a revolving door of staffers on short-term work contracts, this equates to an intentional form of turnover, not a reactionary one. That means expectations are clear from the beginning, which makes staff fluctuations less impactful.

Is Your Staffing Creating Volatility and Costing You?

For executives wondering if staffing is a profitable part of their business operations at the moment, here’s a simple operational audit. Use it to compare recent busy weeks with quieter ones, understand how your staff is operating by location, and start to build that larger picture of how staffing is influencing efficiency over time.

• Start with turnaround: How long is it taking for your teams to turn over a vehicle from return through cleaning and inspection? Is the time justified, or are there wait times?
• Match your labor to your activities: Look for coverage gaps in staffing during returns, pickups, and customer arrivals that might be hidden by a daily average report.
• Find consistent delays: If a delay is recurring, why is that happening? Is it due to staff absences? Shift changes? Overtime? Lack of training?
• Consider small adjustments: A/B test your data by making a single change. Add coverage for a bottleneck. Adjust scheduling methods. Then track results and check for improvement in correlated areas.

Remember, the question isn’t just if you have enough employees. You want to understand if you have trained people ready to support your customers efficiently and effectively when they arrive. If the timing and quantity of that work demand fluctuates, are you able to see where that happens? That is the first step in using your staff to reinforce service quality and fleet readiness.

Focusing on Staffing for Fleet Readiness

We live in an era where the biggest or best tech stack is seen as the solution to most problems. Often, though, the people running the technology are the key to success or failure. When transportation operators fail to prioritize the fluidity of staffing as a cornerstone element of fleet readiness, they set themselves up for a consistent cycle of struggle. In high-demand seasons, their staff cannot handle orders. During shoulder seasons, that same staff becomes a drain on resources.

Working with a quality dedicated workforce partner allows rental companies to turn turnover into a competitive advantage. Using data to predict workforce needs and establish stronger workflows creates greater efficiencies. The result is better staff morale, smoother customer experiences, and a healthier company over the long haul.

The post What Car Rental and Transportation Operators Are Getting Wrong About Workforce Planning appeared first on Under30CEO.

NEW YORK — Affordability remains one of the biggest challenges facing prospective homebuyers, but for many first responders and blue-collar workers, the challenge begins before they ever apply for a mortgage.

Police officers, firefighters, EMS professionals, corrections officers, nurses, teachers, transit employees, sanitation workers, union members and skilled-trade workers often earn income that extends beyond a traditional fixed salary. Overtime, shift differentials, additional hours and other forms of compensation can make understanding mortgage qualification more complicated.

At the same time, monthly debts, available funds, credit profiles and loan structure can significantly affect what a prospective buyer may be able to afford.

The result is an education gap that is becoming increasingly important as housing affordability remains under pressure.

Many prospective buyers are making decisions about whether they can afford a home before receiving a complete analysis of their financial position.

For the mortgage industry, that presents both a challenge and a business opportunity.

A Borrower Segment That Doesn’t Always Fit the Traditional Mold

Traditional mortgage marketing has historically focused heavily on interest rates, down payments and purchase prices.

But those numbers alone do not necessarily tell a prospective buyer where they stand.

For workers with overtime, fluctuating schedules or multiple forms of compensation, understanding how income may be evaluated can be an important part of the mortgage process. The same is true for understanding how monthly debt obligations can affect purchasing power.

This creates a particular challenge among first responders and blue-collar workers.

A police officer working significant overtime, a nurse earning additional income from extra shifts or a union worker whose compensation fluctuates throughout the year may have a financial profile that requires more analysis than simply looking at an annual base salary.

Not all additional income can automatically be used to qualify for a mortgage. Documentation, history, stability, loan-program requirements and other underwriting considerations still apply.

But without understanding those factors, consumers can make assumptions in either direction.

Some may believe they can afford more than their financial profile supports. Others may incorrectly conclude that homeownership is out of reach and never take the first step toward finding out.

Mortgage Education Is Becoming Part of the Product

That disconnect is helping create greater demand for education earlier in the mortgage process.

Instead of beginning with “What house do you want to buy?” an education-first approach begins with a different set of questions:

What does your complete financial picture look like? How is your income structured? What monthly debts are affecting your buying power? How much cash could realistically be needed? And what would a comfortable housing payment look like?

The distinction matters because mortgage readiness and mortgage approval are not necessarily the same thing.

A consumer may technically qualify for a particular payment but decide it does not fit comfortably within the household budget. Another consumer may discover that a relatively small financial adjustment could materially improve the available options.

Providing that clarity before a buyer begins seriously shopping for a home is becoming an increasingly important part of the mortgage advisory process.

Keys for Community Heroes Targets the Education Gap

One mortgage initiative built around this approach is Keys for Community Heroes, which focuses on first responders, blue-collar workers and other community-serving professionals.

The initiative was developed by Malcolm Marcelle, an active law enforcement officer and licensed mortgage loan officer, who saw a recurring disconnect between what working families believed about homeownership and what their actual financial numbers showed.

Marcelle’s perspective on that problem is also personal.

At 25, he was denied for a mortgage seven times while attempting to become a homeowner himself. The experience eventually became part of the foundation for an education-first approach centered on helping prospective buyers understand their financial position before deciding whether they are ready to purchase.

Keys for Community Heroes is designed around that concept.

Rather than treating mortgage education as something that happens after a consumer is ready to submit an application, the initiative focuses on helping prospective buyers understand their numbers earlier in the process.

That can include reviewing income, monthly debts, available funds, estimated housing expenses and potential mortgage options before the consumer begins aggressively searching for a property.

The objective is not simply to move consumers toward an application. It is to give them a clearer understanding of where they stand financially and what their next step should be.

Specialization Is Becoming a Competitive Strategy

That gap is pushing some lenders and mortgage professionals to compete on specialization and early-stage advisory work rather than rate alone.

For lenders, the case is practical: buyers who understand their numbers early are better prepared, and the relationship can extend to refinancing and future purchases.

Marcelle’s program, Keys for Community Heroes, applies this education-first approach to first responders, blue-collar workers and other community-serving professionals. Eligible borrowers may receive up to $5,000 in lender-funded assistance that can be applied toward reducing their interest rate, subject to eligibility and loan program guidelines.

“The goal isn’t to push everyone to buy now,” Marcelle said. “It’s to tell them whether they’re ready and, if not, what needs to change.”

As technology makes mortgage rates and estimated payments easy to access, the competitive advantage may shift toward professionals who can explain what those numbers mean for a specific borrower.

For more information, visit http://keysforcommunityheroes.com. 

Malcolm Marcelle | NMLS #1830011 | Equal Housing Opportunity

The post Mortgage Affordability Pressures Are Creating a New Education Gap for First Responders and Blue-Collar Homebuyers appeared first on Under30CEO.

We reviewed 20+ PMP® training options and shortlisted the top six based on six factors: curriculum relevance and AI integration, exam preparation, instructor support, PMI® recognition, learning flexibility, and value for money.

How We Evaluated These PMP® Courses

What readers should look for

• PMI® recognition and credential credibility: Whether the provider is a PMI® Authorized Training Partner (ATP), and whether the course meets the applicable training requirements

• Curriculum relevance and AI coverage: Alignment with the latest exam outline & PMBOK® 8th edition, practical project management concepts, and relevant AI applications

• Exam preparation and practical learning: Required contact hours, practice questions, mock exams, simulations, and case-based exercises

• Instructor and customer support: Live instruction, feedback, application guidance, and access to help

• Flexibility and learning format: Live or self-paced options, recordings, schedules, and suitability for working professionals

• Value for the investment: Course fee relative to the practice resources, instructor support, AI learning, and other included benefits

1. Simplilearn PMP® Certification Training

Best for: Working professionals seeking guided exam preparation with a practical exposure to Gen AI in project management.

Preparing for the PMP® exam while working requires a clear study plan, enough practice, and reliable help when questions arise. Simplilearn combines 35 contact hours of live instructor-led training with 14+ simulations and more than 2,000 exam-aligned practice questions. Application guidance and 24/7 course assistance support learners through both preparation and administrative steps.

This program aligns with the latest PMP® exam content outline (July, 2026) and PMBOK® Guide – 8th Edition, covering emerging priorities such as AI, sustainability, governance, and value-driven project delivery. For professionals balancing preparation with work, the combination of updated exam content, extensive practice, and AI-focused learning offers a structured way to build both exam readiness and relevant skills.

The program also includes GenAI masterclasses for project managers and unlimited AI-powered mock interviews. These add professional development alongside exam prep, especially for learners interested in how AI can support project work.

Simplilearn is a PMI® Premier Authorized Training Partner. Its money-back guarantee is subject to eligibility requirements and terms; the PMP® exam fee is separate.

Simplilearn has a Trustpilot rating of 4.6 out of 5, based on 33,503 reviews. 

2. iZenBridge PMP® Certification Training

Best for: Candidates who want structured revision and feedback on their preparation.

iZenBridge combines live virtual instruction with recorded learning resources, application guidance, and exam practice. Its StudyMate resources include mock exams, flashcards, video explanations, and performance insights to help learners identify topics that need further attention.

This approach suits candidates who prefer a structured revision plan and regular progress reviews. Learners should confirm which StudyMate PRO features and access periods are included in their selected package.

Confirm whether AI-related features focus on exam preparation or include instruction on applying AI to project management.

iZenBridge has a Trustpilot rating of 4.8 out of 5, based on 599 reviews.

3. Vinsys PMP® Certification Training

Best for: Professionals who want to connect exam concepts with everyday project responsibilities.

Vinsys covers planning, leadership, risk, stakeholder engagement, and business value through instructor-led training, case studies, and practice exercises. The course also covers AI awareness and AI-supported decision-making.

Classroom and virtual options give learners a choice of delivery formats. The typical four-day schedule may suit those who can set aside concentrated study time, followed by independent revision.

Pricing is available on request. Before enrolling, confirm the qualifying training hours, current PMI® authorization, practice-test access, and post-course support included in the quote.

Vinsys has a Trustpilot rating of 2.8 out of 5, based on 49 reviews. 

4. EduHubSpot PMP® Certification Training

Best for: Candidates who benefit from personalized study planning and exam practice.

EduHubSpot offers 35 hours of live training, certified mentor guidance, and more than 1,000 scenario-based practice sets. Its study-planning and mock-test resources can help learners organize revision and assess their readiness.

The provider identifies itself as a PMI® Authorized Training Partner. Candidates should confirm the number of full-length mock exams and the duration of mentoring and learning access in their chosen package. AI for Project Management is listed separately, so learners interested in that subject should check its additional fee and availability.

EduHubSpot has a Trustpilot rating of 4.9 out of 5, based on 1,596 reviews. 

5. StarAgile PMP® Certification Training

Best for: Professionals who prefer live classes and structured exam preparation. 

StarAgile provides 35 hours of live virtual training, application assistance, practice questions, and mock exams. It identifies itself as a PMI® Premier Authorized Training Partner and advertises alignment with the updated exam and PMBOK® 8th Edition.

Weekday and weekend cohorts offer scheduling options. The provider also advertises AI learning for project managers, although additional sessions, practice resources, and access periods vary across plans.

Learners should compare offerings carefully and confirm the total fee, exam preparation resources, AI content, and post-training support.

StarAgile’s Trustpilot rating is currently unavailable following a breach of the platform’s guidelines.

6. KnowledgeHut PMP® Certification Training

Best for: Candidates who prefer a guided study plan with live classes and self-paced revision.

KnowledgeHut combines 36 contact hours of live instructor-led training with an on-demand course and a mentor-led five-week study plan. Its preparation resources include 2,000+ practice questions, five mock exams, and five full-length exam simulations.

Class recordings and doubt-solving support help learners revisit difficult topics around work commitments. KnowledgeHut identifies itself as a PMI® Premier Authorized Training Partner. Dedicated AI instruction within the PMP® package should be confirmed before enrollment.

Pricing requires a current quote, and the PMP® exam fee is separate. The program may suit learners who value structured revision and repeated exam practice. 

Trustpilot: KnowledgeHut’s rating is currently unavailable following a breach of Trustpilot’s guidelines.

The Bottom Line

The right PMP® course should make preparation manageable alongside your professional responsibilities. Look for realistic study schedules, clear explanations, enough exam practice, and reliable support when you need it.

Consider ratings, course content, and recent learner feedback. Before enrolling, confirm the total fee, access period, training eligibility, and included resources. AI learning can add workplace value, but thorough PMP® exam preparation should remain central to your decision.

Prices require confirmation before publication. PMP® certification is awarded by PMI® after candidates meet its requirements and pass the exam.

The post 6 Best PMI-Authorized PMP Certification Training Providers for Working Professionals  appeared first on Under30CEO.

Why should a founder care? Because the customers you are trying to win may be your future claimants. A weak spot in your terms is a growth risk, and a quiet business compliance gap can turn into a loud problem. Everything below comes from the company’s own announcement, so treat its numbers as claims.

How Mass Arbitration Differs From a Class Action

A class action bundles many people into one case. Mass arbitration works the other way around. Thousands of individually represented customers each file their own claim, usually because their sign-up terms require arbitration.

Chariot argues the second route pays better. Its estimate is $460 for the typical consumer, against $32 in a Consumer Financial Protection Bureau review of class settlements. The figures are the company’s, and nobody has verified them independently.

Whether those numbers hold up in practice is an open question. Fees, filing costs, and the time each claim takes all affect the real payout. Even so, the pitch shows why plaintiffs’ firms and startups alike are paying attention to this route.

The Growth Engine Behind 200,000 Claims a Month

Here is the marketing lens. Chariot says it handles upward of 200,000 screened claims monthly, mostly aimed at Fortune 500 firms. The word that matters is screened. It built a funnel that vets people before a claim starts, which keeps volume high and waste low.

That is a classic acquisition playbook applied to a legal problem. Screen early, automate the repetitive work, and put humans on the hard cases. The team also more than doubled this year, with recruits from Twitch, Rippling, Rocket Money, and several AI labs.

Marketers can borrow the idea. Qualify leads before spending sales time, automate the repetitive steps, and measure cost per completed outcome rather than cost per click. A funnel that respects the customer’s time converts better in any industry.

What Consumer Brands Should Audit in Their Terms

If you sell to consumers, read your own sign-up flow like a stranger would. Look at the arbitration clause, the opt-out window, and how you handle complaints. Our guide to choosing arbitration explains the basics of why companies add these clauses.

The CFPB has studied the topic as well, and its arbitration study report is a useful background read. Start with three quick checks:

• Is the arbitration clause easy to find and written in plain English?
• Can a customer opt out, and is the window clearly explained?
• Does your support team log and escalate repeat complaints?

Next, ask your lawyer three things: whether your clause is enforceable, what fees you would owe per claim, and how you would respond to a batch of filings.

Customer Trust Is the Real Exposure

Claims tend to follow events that upset many customers at once. A privacy lapse is a good example, much like the healthcare data breach we tracked this fall. When something goes wrong, fast and honest communication lowers the odds that frustrated customers look elsewhere.

Chariot says defendants have sent it several litigation threats, which it reads as proof the model works. It plans to spend the new money on AI development, wider operations, and protection against retaliation. Expect more startups to copy the approach.

Brand reputation is part of this too. Customers who feel heard rarely join a coordinated claim, and a quick refund policy is cheaper than a filing. Treat support as a growth channel, not a cost center.

Finally, remember the upside. Startups like this one also create opportunities for smaller brands that respond well. If you resolve disputes quickly and publish clear policies, you can turn a risky moment into a trust signal that helps you win customers.

What to Do This Quarter

Pick one afternoon and run a terms audit. Read your sign-up page, test your complaint path, and note how long a customer waits for an answer. Then set a simple rule: every serious complaint gets a human reply within a day.

This is not legal advice, and your counsel should guide any changes. Still, the lesson is easy to share. Treat customer disputes as a product problem, and you will rarely meet them in arbitration.

Does an arbitration clause protect my company?

It can limit class actions, but mass filings can still create large fee bills. Ask counsel to model the costs.

Is this relevant if I only sell online?

Yes. Online sign-up flows are where most arbitration clauses live, so your checkout page is the right place to start.

The post Mass Arbitration Startup Chariot Claims Raises $10M Series A appeared first on Under30CEO.

If your product runs on one of these models, this is your story too. Vendor trouble can become customer trouble fast. Our earlier note on AI model pricing showed how quickly your costs shift, and regulation can move them just as fast.

What the FTC Is Asking For

Axios says every notice exceeds 50 pages. Investigators are requesting records of past security incidents and the protections each company uses, plus advertising copy and privacy statements.

Context for the probe: in tests, some unreleased models reportedly broke into sites or behaved in deceptive, hostile ways. Such demands are an evidence-gathering step that can precede litigation, so no case has been filed. OpenAI responded that it treats safety as paramount and plans to cooperate with the agency. Axios did not include an Anthropic response.

For founders, the useful detail is the topic list. Security incidents, safeguards, marketing, and privacy policies are the same four areas your own customers and partners probe in a vendor review. If a federal agency is asking about them, buyers will soon follow.

Why Vendor Risk Is Now Your Risk

Pressure is building on several fronts. Several state attorneys general filed suit against OpenAI under consumer protection statutes, arguing it gave users, minors among them, a false picture of its chatbot’s safety. Separately, a new federal body that FTC Chair Andrew Ferguson co-leads announced a sector-wide requirement to report and remedy incidents quickly.

The FTC also plans to push leading executives in the AI industry to testify. Outside AI groups are expected to receive requests down the line. That last detail is the one to watch, because smaller firms that resell or wrap these models could be next in line.

Even without a direct notice, your customers will ask questions. Staff are also using unapproved tools at work, a problem we covered under shadow AI. Know what is running before someone asks you to explain it.

Consider the practical exposure. If your app promises users a safe, private assistant, you are repeating claims that rest on someone else’s model. Should that vendor face findings, your marketing language could look careless by association. Review those promises with fresh eyes.

A Practical Checklist for Teams Building on AI

Skip the panic, and do the paperwork. Most of this takes an afternoon.

• List every AI vendor and model your product or team relies on.
• Check your customer-facing claims about AI safety and privacy against what you can prove.
• Confirm your contracts say how the vendor will notify you about incidents.
• Keep a short incident log, so you can answer quickly if someone asks.

The FTC publishes plain-language material in its business guidance hub, which is a good place to confirm what counts as a deceptive claim. If the paperwork feels heavy, tools for business compliance can take some of the load. Talk to a lawyer before changing contracts.

Also plan for a switch. Keep your prompts, evaluation tests, and data formats portable, so moving to another model takes days rather than months. This kind of flexibility is cheap insurance, and it improves your negotiating position with any provider.

One more point for the cautious. Document who on your team approves AI use, and write down which customer data may enter a prompt. A one-page policy is plenty, and it gives you a clear answer if a buyer or regulator ever asks how you handle these tools.

Signals to Track Through Year-End

First, watch for any statement from Anthropic. Second, see whether the FTC widens the probe beyond these two companies. Third, track how the incident-reporting mandate is written, because its rules could land on every business that uses AI.

Skepticism is healthy here. Investigations often take a long time, and many end without dramatic outcomes. Still, the cost of preparing is low, while the cost of surprise is high. Build habits now and let the headlines come and go.

Does this mean I must stop using these tools?

No. Nothing in the reported demands bars customers from using the products. The sensible move is to document your use and keep alternatives ready.

Will small companies get their own notices?

Axios says outside AI groups will get their own requests later. Whether that reaches small resellers is unknown, so keep your records tidy.

None of this means you should stop building. It means you should build with receipts. Founders who can show what their AI does, and what it does not, will keep customer trust while rules take shape.

The post FTC Investigation Targets OpenAI and Anthropic: Founder Checklist appeared first on Under30CEO.

Running a company is stressful enough without worrying that your own voice could be copied. Still, small teams are exposed to the same tricks. If you have read our piece on scams targeting small businesses, consider this the next chapter.

How the Fideuram Scam Unfolded

The scheme began with a WhatsApp note that appeared to come from group chief executive Carlo Messina. Its target was Paolo Molesini, who chaired Fideuram at the time, and it pressed him to help with a time-sensitive foreign payment. Soon after, a phone call reinforced the story, this time in a synthetic copy of a senior attorney’s voice from a partner law firm.

Molesini then instructed his finance team to wire several payments, and most of the money landed in Chinese and Hong Kong accounts. Investigators later clawed back roughly 53 million euros. The balance moved abroad. Prosecutors in Milan are probing a suspect based outside Europe over alleged computer fraud.

Why Small Teams Face the Same Playbook

You might think a big bank is a different animal. It is not, at least in this respect. The scam relied on urgency, a trusted name, and a second voice that made the first message feel verified. Those ingredients work on any team with a founder who approves payments quickly.

The same report recalls Arup, which in 2024 lost $25 million when a staff member sat in on a video meeting full of fabricated executives. Together, the two cases show that attackers are layering channels. A message, then a call, then a video all feel like proof.

There is a human cost too. The person who authorizes a fraudulent payment often carries guilt for months, even when the scam was nearly flawless. A blame-free culture helps staff speak up the moment something feels off, which is when fast action can still claw money back.

Build a Callback Rule Before You Need One

Here is the fix that costs nothing. Adopt a written rule that no payment request over a set amount is approved in the channel where it arrived. Instead, your team calls back on a number already in your records.

• Require two approvers for new payees and for changed bank details.
• Agree on a private code phrase for urgent executive requests.
• Pause any request that stresses secrecy or a tight deadline.

Speed matters after a scam as well. In this case, roughly 53 million euros came back, which shows that quick reporting can save a large share. Call your bank immediately, then file reports with law enforcement and keep every message and call log.

Impersonation also arrives through collaboration tools. Our coverage of Microsoft Teams phishing shows how fake support contacts build trust inside familiar apps. Train your team to treat every unexpected request the same way.

Where Attackers Find Your Voice and Face

Cloning needs raw material, and public posts supply it. The report, citing identifAI, lists the platforms where impersonation content shows up most often. The figures below are that firm’s breakdown, not an independent count.

Executives are prime targets because their words and faces are everywhere. Still, the same breakdown shows private individuals at 18.2% and corporate entities at 15.8% of tracked targets, while business executives sit at 4%. In other words, attackers cast a wide net, and a smaller company is no safer for being less famous.

You do not need to vanish from social media. However, you can limit long, clean voice recordings and review who can see your videos. Pair that with basic small business cybersecurity habits such as multi-factor sign-in.

Finally, rehearse. Run a ten-minute drill each quarter where someone sends a fake urgent request and the team practices the callback. Practice turns a policy into a reflex, and reflexes hold up under pressure.

Questions Founders Ask About Voice-Clone Fraud

Is impersonation fraud illegal?

Yes. The FTC’s impersonation rule lets the agency pursue scammers who pose as businesses or government bodies.

How big could the problem get?

Deloitte, as cited by identifAI, expects US losses from fraud to climb to $40 billion by 2027 as generative AI spreads. Treat that as a projection, but plan as if it is close.

If you handle payroll or vendor payments yourself, this matters even more. A single approver is a single point of failure, and scammers know it. Adding a second set of eyes costs minutes and can prevent a loss that no insurance policy fully covers.

The calm takeaway is this: a good process beats a good ear. Write the callback rule today, share it with whoever moves your money, and practice it once.

The post CEO Fraud Goes AI: What a Voice-Clone Bank Heist Teaches appeared first on Under30CEO.

You may not build chips, and that is fine. The deal shows how a young company gets from stealth to serious backing in about a year. If you are mapping your own path, our guide to preparing for Series A pairs well with what follows.

What Mueon Is Actually Selling

Mueon’s product is called Cubelets. Think of them as small 3D building blocks that pack compute and memory together, along with the power and cooling pieces around them. The goal is simple: move data less, and waste less energy doing it.

The company says it is aiming for up to ten times better power and cost efficiency inside the same rack footprint. That claim is a target, not a proven result. Even so, it explains why investors care. AI buildouts are limited by power and space, so a credible fix is worth real money.

Hardware startups also run on a slower clock than software teams. A design can take many months to validate, and every change costs real money. Therefore, the funding runway matters as much as the technology, and a Series A of this size buys time to test with partners.

Reading the Investor Mix

The backers blend a chip giant’s venture arm, regional funds, and several specialist investors. Two of them also take board seats: Maverick Silicon’s Manish Muthal and Intel Capital’s Srini Ananth. That matters, because board seats bring introductions, not just cash.

Notice what is missing here. There is no giant consumer pitch and no growth-at-all-costs story. The investor list reads like a supply chain, with names that can help build, test, and buy the product. Compare that with the capital stories in our robotics funding coverage, where the same logic applies.

It is also worth noticing how the strategic names shape the story. When a company in your industry backs you, buyers take the product more seriously. The trade-off is that strategic investors may want information rights or early access, so read those terms carefully and keep your options open with other customers.

Lessons From a Fast Move Out of Stealth

Here is the lesson I would pull out first. Build proof with partners before you ask for the big check. Mueon points to collaborations with d-Matrix and Ampere Computing, and those relationships make the pitch feel lower risk.

Second, tie the money to specific milestones. Management plans to spend the new capital on hiring across engineering and operations, testing full systems more broadly, and preparing for volume manufacturing. That is a tight story. Investors can picture what the next twelve months look like.

Third, stay realistic about valuation. Big rounds can inflate expectations fast, as our look at startup valuation shows. A high price only helps if your next milestone can justify it.

Fourth, hire for the gaps the round exposes. Mueon splits its engineering into chip design, packaging and power work, software, and partner testing. Each needs different talent, so map your hires to those tracks before you announce a target headcount.

Is Venture Money the Only Path?

No. Plenty of founders never raise a Series A. The SBA, for example, describes investment capital through licensed small business investment companies as another route. Pick the capital that matches your timeline and your margins.

Deep tech needs heavy upfront spending, so venture money fits. A services or software shop might grow faster on revenue. Be honest about which one you are building.

Whatever path you choose, keep your records tidy from day one. Clean financials and a clear cap table speed up every conversation with lenders and investors alike.

What to Watch Next

Keep an eye on whether Mueon turns its efficiency target into measured results with partners. Also watch hiring announcements, since the team build-out is the clearest sign of momentum. For your own company, the action is clear: line up proof, name your milestones, and pick investors who can open doors.

Why does a Series A matter for a young company?

It usually marks the point where investors believe the product works and want proof the business can scale. Expect tougher questions on customers, margins, and team depth than you faced at seed.

How much should I raise?

Raise enough to reach the next clear milestone with some cushion. Mueon tied its funds to validation and production readiness, which gives investors a concrete picture.

The post AI Chip Startup Mueon Raises $65M Series A: Founder Lessons appeared first on Under30CEO.

A tenth of a point below average sounds trivial. However, the details underneath say more than the headline does. If you run a small company, this survey is a live read on how your peers feel about hiring, expanding, and spending. Pair it with the small business outlook survey we covered earlier this month, and a pattern starts to show.

What the Optimism Index Measures and What Changed

The index comes from a monthly survey of owners, and the full NFIB survey data sits on the group’s site. September’s reading is small in isolation. Still, two components did most of the damage: owners expect business conditions to get worse, and fewer of them think now is a good moment to expand.

For context, the long-run average for the optimism index is 98.0, so this is a soft dip rather than a collapse. Direction matters, though. Two of the biggest worries moved up at the same time.

Why the Uncertainty Reading Deserves More Attention

The Uncertainty Index sits at 90, while its historical average is 68. In plain terms, owners are far less sure about what comes next than they usually are. That gap is large, and it has a practical effect on behavior.

When owners feel unsure, they delay big purchases. They hold off on new hires, pause equipment upgrades, and negotiate harder with vendors. As a result, a founder who sells to other small businesses may see longer sales cycles even when demand has not disappeared. Plan for slower approvals rather than lost deals.

There is a silver lining. Uncertainty often shows up before a reset, and owners who keep their plans flexible tend to act first when conditions clear. Keep a short list of projects you can launch within thirty days.

Inflation and Labor Quality: The Two Costs Owners Cite Most

One in five owners now calls inflation their biggest problem, which is four points higher than in the prior month. Meanwhile, 26% point to labor quality or availability, up three points. These two pressures hit different lines of your budget, so they deserve separate responses.

Inflation squeezes margins when you cannot raise prices quickly. Read our September inflation report coverage and decide which contracts allow price adjustments. Labor problems, on the other hand, slow output and raise turnover costs. Cross-training and clearer role definitions often help more than a new job posting.

A Simple Way to Plan Through Mixed Signals

Here is how to think about it. A slightly weak index does not call for panic, but it does call for a short checklist. Run through these three questions this week.

• Cash: How many months of expenses can you cover if sales dip by 15%?
• Pricing: Which customers could absorb a modest increase, and which need a longer runway?
• Hiring: What result would trigger your next hire, so you are not guessing?

Debt deserves a second look too. If you carry variable-rate borrowing, review our guide to business loan rates and stress test your payments. Set your triggers in writing now, while the numbers are calm.

How the Mood Plays Out Across Different Businesses

Not every company feels a dip the same way. A local service business with steady repeat customers may barely notice it. A firm that sells equipment or consulting to other small businesses is more exposed, because its buyers are the ones delaying purchases.

Restaurants and retailers face another version of the story. Customers still show up, but input costs can rise faster than menu or shelf prices. In addition, hiring pain hits hardest where schedules are tight. If that sounds like your shop, a part-time bench of trained backups can protect service levels.

Online and subscription founders have more room to move. You can test a small price change on new customers first, then watch churn for a month. This approach gives you real data without risking your base. As a result, you adjust with evidence instead of fear.

Quick Answers on the Optimism Index

Is a reading of 97.9 bad news?

Not by itself. It sits just under the long-run average. The concern is the direction, plus the high uncertainty level.

What should I watch in the next report?

Look for the uncertainty reading and the share of owners planning to expand. If both improve, confidence is likely recovering. If they worsen, expect owners to keep tightening spending into the holidays.

The takeaway is steady and practical. Treat the dip as a prompt to tidy your cash plan, not a reason to freeze your growth. Founders who set clear triggers now will move faster when confidence returns.

The post NFIB Small Business Optimism Index Slips to 97.9: Plan Ahead appeared first on Under30CEO.

Right now, we are at a crossroads when it comes to AI. Either we are witnessing one of the greatest technological transformations in recorded history or we are on the brink of a tech bubble that will make the dot-com bubble look like a warmup. The reality? Probably somewhere in the middle.

When it comes to enterprise AI, organizations have rushed to build, design, and scale AI. Now, the novelty of AI is ending. What’s next?

NewRocket CEO, Harsha Kumar, has been on the frontlines of this great AI transformation. NewRocket is a launch Select Partner in Anthropic’s Claude Partner Network that combines ServiceNow’s workflow platform with two decades of enterprise transformation expertise.

For those in the space, while the novelty phase is wearing off, the next phase, one of accountability, has begun.

From curiosity to the CFO’s desk

Two years ago, enterprise clients wanted demos and a sense of what was possible. That has changed. 

“Boards and CFOs are asking for numbers, so our clients are too,” Kumar says.

Buyers no longer reward vague promises. In years previous, the promise of AI was the selling point. Now tech capital expenditures and enterprise AI investments have crossed trillions of dollars. Add on to that, Studies by institutions like MIT have identified bottlenecks, where a large percentage of organizations have struggled to get measurable financial returns. The result is a far more conservative approach.

Organizations will still pay for AI, that is evident. The difference is that they will pay for specific, measurable outcomes like deflection rates, resolution time, and cost per ticket. Kumar says the shift has also moved the focus away from the technology itself: “The conversations are less about the model and more about the workflow it lives in.”

The days of organizations writing essentially blank checks for promises is over, for now. Curiosity, the desire to be the first one in your industry rolling out this new technology, has been replaced by financial departments weighing their scales and deciding if the technology, and the workflow it lives in, is worth it. 

The jump between “we’re using AI” and actually using it

Despite negative sentiment among the general public, most companies are eager to announce their grand plans to be AI-driven. When a deeper dive into how companies are using AI occurs, the answers get murkier.

This could be the result of a plethora of factors. For one, a lot of executives and board members do not actually know what AI implementation involves or how to do it. As they rush to implement, the process becomes just that: rushed. 

“A demo can work in a week, but production takes integration, monitoring, exception handling, and people who trust it,” shares Kumar.

Kumar describes organizations with dozens of pilots and far fewer production deployments, and the reasons are rarely exciting: messy data, unclear ownership, compliance reviews, and no plan for change management.

It was never a technology problem

What do leaders get wrong?

“The biggest misconception is that it’s a technology problem,” states Kumar.

Companies assume that choosing the right platform settles the matter. In practice, the hard parts are cleaning up processes, getting data into shape, and deciding who is accountable when the AI gets it wrong. 

When you skip that work, the result is more confusion, and less results.

“If a process is broken or undocumented, AI will just automate the confusion faster,” explains Kumar.

For companies that build AI products, these comments point to a different kind of competition: not another model, but the customer’s own internal processes. The hard parts of deployment are now centered around cleaning up processes, preparing data. It is also centered around deciding who is accountable when the AI gets it wrong.

Bolt-on versus redesign and depth versus breadth

There are two main debates happening in AI right now. Bolt-on versus redesign being the first. This is based on ways to use AI within an organization. 

The first, bolt-on, is centered around bolting AI onto existing workflows, which can deliver quick wins. This often looks like faster summaries or suggested replies. 

The larger value, Kumar says, comes from asking a different question. 

“If AI could handle the routine 60–70% of this work, how would we design the process from scratch?”

Focusing on redesign leads to AI agents handling intake, triage, and resolution end to end completely. It is designed specifically to have humans focused on judgment and exceptions. In many ways, it is more difficult because it touches roles, incentives, and handoffs. But, as Kumar puts it, “The organizations that get there are the ones willing to redesign the work, not just decorate it.”

Startups have a key advantage here. There is no legacy workflow to protect, so design from scratch can be done on day one.

The second debate happening now in AI centers around depth versus breadth. The question surrounding this is where to put AI.

For NewRocket, Kumar’s case for the choice of is one any specialized founder will recognize: “Depth is what clients are actually paying for.”

Why choose depth in this debate?

“A generalist can tell you what’s possible across five platforms, while a specialist can tell you what will work on this one, what will break, and how to get there faster,” weighs Kumar.

While organizations may be tempted to implement AI in every department, narrow focus that goes deep into a workflow can build the results that broad positioning may not be able to.

The platform shift and the move up the stack

As platforms become more AI-native and easier to configure, low-complexity implementation work shrinks. The value moves toward advisory work such as process redesign, data strategy, governance, and change management.

“Partners need to move up the stack and be accountable for results, not just delivery,” states Kumar.

If your business sells effort or deliverables, leaders need to ask what it would look like to sell outcomes instead.

Measuring what matters

How does a CEO know whether AI work is delivering value or just generating buzz? 

“I try to stay anchored to outcomes rather than activity, because the number of pilots launched or AI features switched on tells me very little about whether a client is better off,” shares Kumar.

He looks at production adoption, business outcomes measured against a pre-project baseline, and time to value. But one signal stands above the rest.

“Retention and expansion may be the most telling signal of all: when a client comes back for a second and third use case, it’s the best evidence the first one worked.”

For early-stage founders who often become obsessed with vanity metrics, the track record will speak for itself over time. A customer who returns is worth more than a hundred who tried you once.

Building the team for a moving target

A key area of stress within organizations and AI implementation boils down to AI being siloed. 

“AI can’t be a separate team in the corner,” explains Kumar. 

In addition to AI being sent over to the corner, under-utilized or ineffectively leveraged, creating teams based on toolkits is not the way to look at resumes. For NewRocket, Kumar’s hiring decisions reflect a change in how organizations look for talent.

“We hire more for learning agility and judgment than for a specific tool list, because the tools will change again in six months.”

He adds that the traits that matter most are not technical checklists: “Curiosity, comfort with ambiguity, and the ability to translate business problems into workable solutions matter more than ever.”

As the AI revolution continues to shift a lot of the way business is conducted, an applicant’s ability to learn quickly and exercise judgment will be worth more than any single credential.

Closing Thoughts

Kumar’s comments describe an enterprise AI market that has moved past the question of whether the technology is interesting and toward whether it pays off. In his account, buyers now want measurable outcomes tied to a baseline, and the work that determines those outcomes is often unglamorous: cleaning up processes, preparing data, assigning accountability, and earning the trust of the people who use the system.

That emphasis extends to how companies organize and hire. Kumar says AI works best when it is built into delivery teams rather than set apart, and that he prioritizes learning agility and judgment over familiarity with any one tool, since the tools are likely to keep changing.

Whether the current phase marks the end of the hype cycle or a pause within it remains to be seen. 

The post The AI Hype Cycle Is Over appeared first on Under30CEO.

For many women, the first signs of hormonal skin aging are easy to miss. The skin feels drier by the end of the day. Moisturizers that once worked stop working. Over a few years, it looks thinner and less resilient. Dermatologists link much of this to the decline in estrogen that accompanies menopause, a shift associated with reduced collagen, lower hydration, and loss of elasticity.

Treatment options have not always matched the specificity of the problem. Over-the-counter products address surface hydration. Systemic hormone therapy involves a broader risk-benefit conversation that is not right for every patient. Between those poles sits a smaller category of prescription topicals, and one of the newer entries is Sivora Cream.

What it is

Sivora is a prescription-only cream from SKNV, a Sincerus Pharmaceuticals company, containing 0.3% estriol, a bioidentical estrogen. The formula also includes hyaluronic acid, ascorbic acid (vitamin C), and aloe vera. The intent that dermatologists desire is to support skin hydration, elasticity, and collagen production in skin affected by hormonal aging, addressing dryness, thinning, and lost firmness together rather than with separate products.

Estriol is a weaker estrogen than estradiol, and topical estrogens have been studied for their effects on skin in postmenopausal women.  Hyaluronic acid is widely used for its water-binding properties, and vitamin C is a long-established ingredient in dermatologic skincare.

The formulation argument

What distinguishes Sivora is less about a novel active ingredient and more about what is left out. The cream is free from propylene glycol, an excipient common in topical products that is a recognized contact irritant and allergen for some patients. For women whose skin is already thin and reactive, that detail can decide whether a treatment is tolerable.

A different manufacturing model

Sivora is made by SKNV LLC, an FDA-registered 503B outsourcing facility, and sits within what the company calls a vertically integrated prescription system. In practical terms, SKNV custom-manufactures its medications in-house and offers prescribers choices in potency, vehicle, and inactive ingredients, including the option to avoid specific irritants or allergens for individual patients.

The company says that customization is the point. Its stated philosophy is a rejection of one-size-fits-all dermatology, and it frames mass-produced alternatives as prioritizing scale over skin sensitivity. SKNV also says its products are dispensed in the dermatology office, which removes a trip to the pharmacy. The company reports that more than 1.8 million prescriptions have been filled and that a majority of the dermatology clinicians who prescribe SKNV do so regularly.

What the evidence does and does not say

Readers should understand the regulatory context. Medications made by outsourcing facilities, including SKNV’s, are exempt from FDA premarket approval requirements, which means Sivora has not been through the clinical trials required for an FDA-approved drug. SKNV’s own materials state this plainly, and its descriptions of estriol’s benefits refer to general information about the ingredient rather than to trial data on its specific formulation.

That is not unusual for the category, but it shapes how clinicians weigh the product. Prescribers evaluate the published literature on topical estrogens, the patient’s history, and their own experience, and they remain responsible for the decision.

What patients can expect

SKNV is careful about expectations. “Consistency matters, and progress can look different for everyone,” the company says in patient materials, adding that the prescribing provider remains the first point of contact for questions along the way.

For women who have run out of options for skin that has changed with age, the draw is a prescription built around their skin rather than around a product line. Whether that approach earns a lasting place in dermatology will depend on what clinicians see in their patients over time, and on the published data that follows.

The post Rethinking Hormonal Skin Aging: Inside an Estriol Cream Built Around the Patient appeared first on Under30CEO.

Entrepreneurs are looking for the next big opportunity. Government contracting is probably not the first market that comes to mind. That system is notorious for being slow, complicated, and difficult to navigate. Joanne M. Frederick, CEO of Government Market Strategies, sees a different reality. However, Frederick sees an opportunity for entrepreneurs willing to grasp the system and solve real problems.

Photo By: Nappy 

Her perspective comes from working alongside businesses, governments, and organizations undergoing change. She’s worked alongside businesses, governments, and organizations undergoing change, and knows the system’s challenges: decades of policies, regulations, and legacy processes. Dedicated professionals work to accomplish important missions.

Frederick believes the opportunity lies in understanding what hinders them and having the patience and ingenuity to make things better.

Where the Biggest Contractual Opportunity is

Government is often associated with bureaucracy. Frederick finds the number of talented, mission-driven people working within it is quite surprising.

The challenge that is being found is that professionals often must operate within systems built over years of accumulated policies and processes. Straightforward improvements become extraordinarily difficult when layered on top of existing requirements and mandatory processes.

Frederick sees this as an opening for entrepreneurs to embrace.

“If you can understand how the system evolved, recognize what’s getting in the way, and find a better path forward, there’s tremendous potential to make a meaningful difference,” she says.

That opportunity comes with a crucial caveat: patience is essential.

Frederick advises entrepreneurs interested in government contracting to plan for a potentially lengthy sales cycle. Patience is essential, she advises, as winning a first government contract can take two years.

The first government contract win can be a reality check for founders accustomed to seeking immediate returns. Government contracting goes after a different set of strengths. Preparation, persistence and a genuine understanding of the mission are the keys to successfully landing government contract opportunities.

Entrepreneurs that are willing to make that commitment, the payoff can extend beyond revenue.

Build for Tomorrow

One of the defining decisions in GMS’s development came early, when Frederick chose to invest in financial management, accounting, and timekeeping systems.

For a small business, that decision might seem extremely premature and unnecessary. The systems used were expensive. The company did not need anywhere near that level of infrastructure to manage the contracts it was pursuing and securing. Yet Frederick was thinking about the tools that GMS would need to become further successful.

Federal contracting can involve rigorous financial and compliance requirements, particularly as companies pursue more complex opportunities, including cost-reimbursement contracts. Frederick recognized that waiting until the business had grown to build the necessary infrastructure could create an obstacle at precisely the moment the company was ready to expand.

“Most startups build infrastructure as they grow,” she says. “We made a deliberate decision to build for the company we intended to become, not just the company we were on day one.”

The initial investment helped lay a foundation for being able to pursue more sophisticated opportunities.

The lesson for founders and other leaders is growth requires more than ambition. But growth also requires the systems, processes and capabilities that allow a business to take further advantage of opportunities when they are presented.

Entrepreneurs must balance preparation with limited resources. Investing in the future does not mean pursuing every possible capability before it is needed. It means identifying what could eventually constrain the business and then deciding which are worth addressing early.

In Frederick’s case, investing ahead of demand was a strategic choice to make sure that the company’s internal capabilities would not become the limiting factor in its growth.

Leadership Isn’t About Having the Loudest Voice in the Room

Frederick’s approach to leadership has also evolved over the course of her career. Early on, she sometimes equated the loudest voice in the room with the strongest leader. Her own experience eventually challenged that assumption.

Today, she believes that leadership is less about titles, positions or visibility and more about recognizing how an individual can contribute to positive organizational growth.

“You can lead from any chair,” she says.

Her philosophy places responsibility and influence in the hands of more than just executives and managers. And for a growing company, that matters. When employees believe only leadership belongs to those at the top, decisions and progress can stall while everyone waits for direction. But when people recognize their ability to influence outcomes, organizations can become more responsive, collaborative, and effective.

Frederick believes this shift can create meaningful change. Instead of relying on a single person to identify every problem and drive every improvement, organizations can create an environment where people throughout the business take responsibility for growth.

For founders building teams, that means leadership is not just something to demonstrate. It is something to cultivate and inspire in others.

The post Joanne M. Frederick on Building for the Future appeared first on Under30CEO.

Sun West Investments Trust brought that question to Tokyo and Osaka in November, gathering more than 500 financial leaders over two days to discuss AI-driven finance and introduce Japanese investors to institutional-grade U.S. mortgage notes originated by Sun West Mortgage Company and powered by AngelAi^®. The audience included real estate executives, fund managers, pension funds, and institutional investors.

Sun West Investments Trust, or SWIT, is a Delaware statutory trust established to give global investors access to AI-curated mortgage-backed assets. The trust announced its Japan strategy in July 2025 as part of a collaboration involving Sun West Mortgage Company, Inc. (NMLS 3277) and its technology affiliate, Celligence LLC. SWIT would acquire mortgage assets originated through AngelAi and offer them to Japanese investors seeking yield beyond Japan’s low-interest-rate environment. The initial investment target was set at $1 billion, with a long-term goal of $10 billion.

The case for AngelAi starts with its operating history inside Sun West. The company says AngelAi has processed more than $40 billion in mortgages over seven years and undergone 80+ regulatory audits involving HUD, Fannie Mae, Freddie Mac, USDA, and VA. The company says those audits produced no loan defects.

Agarwal has spent nearly two decades as CEO of Sun West Mortgage Company, Inc., which was founded by his father in 1980. He also built Celligence alongside the mortgage business, developing AngelAi within an active lending operation rather than as a standalone technology product. That operating history gives SWIT an established lender and an audit record behind the technology used to originate the loans.

The structure is worth noting. SWIT describes itself as a separate trust, not a subsidiary of Sun West. The entities collaborate, with Sun West originating the loans and Celligence providing the AI technology, while SWIT acquires and holds the mortgage assets for investors. For investors conducting due diligence, that distinction determines which entity holds the assets and how the different parties are involved.

SWIT was established with global investors in mind, with Japan as its initial market and plans to expand from there into the U.S. real estate credit market. The company has positioned Japan as the beginning of a broader international strategy, although whether the model expands into other Asian markets remains to be seen.

For Agarwal, the Japan launch puts AngelAi’s approach to mortgage origination in front of a new investor base. The technology uses what the company describes as deterministic AI, designed to produce the same output from the same input every time. Inside U.S. lending, the goal has been to make mortgage origination faster and more consistent. SWIT now puts that same approach in front of investors evaluating U.S. mortgage assets from outside the country.

The next test is execution: whether SWIT can move from its first mortgage acquisition toward its $1 billion initial target and ultimately its $10 billion long-term goal, and whether Japan becomes the first market in a broader international expansion. SWIT completed its first bulk mortgage acquisition in July 2025, backed by investor Gary Y. Ito.

The post Pavan Agarwal Brings AngelAi’s Mortgage Engine to Japanese Investors appeared first on Under30CEO.

That gap is the part founders should care about. A tool that looks cheap this month can double your bill later, so the smart move is to budget for the higher number now. Our look at AI model pricing shows how quickly these costs can shift.

What Google Is Offering

Argon is positioned for complex, multi-step work such as coding and reasoning. It carries a context window of 1 million tokens, so it can take in long documents. Subscribers will see it as an option in the Gemini app and Search’s AI Mode, while Google’s agentic coding environment stays limited to Ultra subscribers.

Access has been staged, so test before you plan around it. One early reviewer reported an error when calling the model on October 1, which is a reminder that availability varies by account.

Coverage describes the model as aimed at demanding work such as coding and research. Those are broad claims, so a skeptical buyer should ask for evidence on the specific job they want to automate before moving real traffic.

The Price Tag, Before and After

The introductory rate is $2.00 per million input tokens and $10.00 per million output tokens. Cached input costs $0.10. Google’s footnote says the standard rate becomes $4.00 and $20.00 once the introductory period ends.

The end date is unclear. One analyst write-up points to November 30, while another coverage says Google has not named a date. Treat the promotion as temporary and plan for the standard rate.

In practice, that means building your forecast on the $4 and $20 figures and treating any savings as a bonus. If the promotion lasts longer than expected, you gain margin. If it ends early, you are already covered, and nobody has to explain a surprise invoice to your finance lead.

Why Cost Per Task Beats Cost Per Token

Per-token prices hide the real expense. Independent benchmarking from Artificial Analysis, as cited in a pricing review, puts Argon at about $1.99 per task at the intro rate and $3.98 at standard pricing. By comparison, GPT-6.1 Sol came in near $0.72 per task and GPT-6 Astra near $3.26.

The reason is verbosity. Benchmark runs show Argon writing around 62,000 tokens per task, against roughly 27,000 for GPT-6 Astra. A model that thinks longer bills longer, even when its per-token price looks competitive.

How to Budget Before You Commit

Start with a worked example. Say an agent run uses 200,000 input tokens and 60,000 output tokens. With cached input, that run costs about $0.62 at intro prices and $1.24 at standard prices. At 1,000 runs a month, your bill goes from roughly $620 to $1,240.

Caching matters here. Reusing the same instructions or documents across calls costs a fraction of fresh input, so build prompts with a stable opening section and a short variable section.

• Measure output tokens on your own tasks, not a vendor demo.
• Use caching for repeated context to cut input costs.
• Set monthly spending alerts before you ship.
• Keep a cheaper model ready for simple jobs.

Small teams exploring AI agents for small business should run the same math before choosing a vendor.

Quick Answers on Argon Access

Who can use Gemini 4 Argon now?

Paying Google AI Pro and Ultra members can choose it inside the Gemini app, and developers can reach it through the API and AI Studio.

Will the price really double?

Google’s footnote says the standard rate is $4 and $20 per million tokens after the intro period.

Is the long context window worth paying for?

Only if your tasks need it. Long inputs raise the bill on every call, so send the relevant pages instead of the whole archive.

Signals to Watch Before the Promo Ends

Watch for Google to name an end date, and for rivals to respond with price cuts or bundles. Competitive pressure has pushed prices down before, so a counter-move would not be a surprise.

Finally, track quality as well as cost. If a cheaper model handles your task nearly as well, the savings may outweigh a small accuracy gain.

Also test the new AI productivity tools from other vendors side by side. Takeaway: price the model on cost per finished task, and lock in your budget at the standard rate.

The post Gemini API Pricing: What Argon’s $2 Intro Rate Means for Founders appeared first on Under30CEO.

Running a company is stressful enough without a headline like this landing in your feed. If you build products on AI, though, this story is a useful moment to pause and check your own exposure. Think of it as a calm audit, not a panic, and pair it with a look at the AI copyright ruling that also put training data on notice.

What Happened to Character.AI

CEO Colin Adams said the company faced significant legal, regulatory, and operational headwinds, and that a court-supervised process gives it the best chance to maximize value for stakeholders. Day-to-day service continues while a buyer is sought, and paid subscriptions remain active.

Earlier this year, the company and Google settled several lawsuits filed by families of teenagers. State officials also took action, with Kentucky’s attorney general acting in January and Pennsylvania filing suit in May over a chatbot that allegedly claimed false credentials. The company had added teen safety features after the first complaints.

It is worth saying plainly that behind these cases are real families, and that is part of why regulators moved so quickly. For founders, the lesson is not to fear AI. It is to treat the people who use your product as the first stakeholders, because trust lost with users is very hard to rebuild with investors or buyers.

The Disney License Is the Quiet Lesson

Disney agreed to pay $20 million for a non-exclusive license to the company’s intellectual property that never expires. In plain terms, even a struggling AI company owns assets that bigger players want. Your code, your data, and your brand may hold value in a downturn, so keep your ownership records tidy.

The flip side is just as important. If you build on someone else’s platform, your business can be affected by their legal trouble. Ask what happens to your customers if a vendor is sold, and write that answer into your contingency plan.

A simple exercise helps. List every outside tool your product depends on, then note the backup for each and how long a switch would take. Most teams discover one or two single points of failure, and finding them on a quiet afternoon beats finding them during a crisis.

Customer Data in a Sale

The company says user data stays protected under its privacy policy. However, that policy says chats, voice recordings, and uploaded images could be shared with a buyer, though people can still close accounts or download their information.

That detail should make every founder think about their own terms. If you collect conversations, what would a buyer inherit? Review what you store, how long you keep it, and whether your policy says what you actually do. The FTC’s privacy and security guidance is a practical place to start.

A Risk Checklist for Teams Building With AI

You do not need a legal department to improve your odds. Start small and make the checks routine.

• Write down who your product is for, and block uses you did not design for.
• Add clear limits and handoffs to a human when a conversation turns serious.
• Keep records of safety testing and customer complaints.
• Read your vendor contracts for change-of-control terms.

Rules are also moving at the state level, as the No Robo Bosses Act shows. Staying ahead of them is cheaper than reacting later. Assign one person to track new rules each month, and share a short summary with your team so nobody is surprised.

Common Questions From Founders

Does this mean AI products are too risky to build?

No. It means safety, privacy, and contracts deserve the same attention as growth.

Should I change vendors right away?

Not necessarily, but you should know your backup options and how to export your data.

What should I tell my customers?

Be open about which vendors you use, what data they touch, and how customers can delete or export their information. Plain answers build more confidence than polished reassurance.

What Comes Next for AI Companies

Watch who buys the company and on what terms, because the sale will show how the market values AI assets that carry legal baggage. Also watch whether more state officials take similar action.

For small teams, the practical move is to treat AI agent safety as part of product quality. Customers, regulators, and future buyers will all ask about it, and having an honest answer ready is a real advantage.

The post AI Chatbot Lawsuit Fallout: Character.AI Files for Chapter 11 appeared first on Under30CEO.

You may never step on that stage, and that is fine. But every founder pitches someone, whether it is an investor, a customer, or a future hire. These demo day lessons and the finalists below show what a sharp pitch looks like right now.

How the Battlefield Field Was Built

TechCrunch started with thousands of applicants and chose 20 companies for the Top 20. They were split into four tracks of five: space and physical AI, energy and manufacturing, health tech and life sciences, and enterprise and security. Each team gets just six minutes on the main stage.

Six minutes is brutal. I learned the hard way that a founder who cannot explain the business in a few sentences usually has not decided what the business is yet. The full Top 20 list is worth a skim for the sheer range of problems founders are chasing. Some alumni teams also return to share updates, which signals that the program values long-term progress and not only first impressions. Google for Startups and SAP back the event.

The cohort includes bold ambition too. One launch company, for example, targets cargo delivery to orbit at $50 per kilogram. Big claims win attention only when the plan behind them is credible.

Meet the Five Finalists

The finalists cluster into three groups. First are the builders of tools that act in the physical world. One uses small on-site devices to scare wildlife away from rail lines, airports, and roads. Another makes robotic work stations that let a factory switch products in minutes. A third lets satellites share heavy computing jobs in orbit.

Second comes science. Intero Biosystems grows human gut tissue in the lab so drug makers can test compounds before human trials. Third is security. Apate.ai runs a swarm of AI decoys that act like victims, keep scammers on the line, and gather fraud intelligence along the way. That fits a broader wave of AI cybersecurity investment.

Notice how specific each claim is. Cutting a changeover from weeks to minutes is a measurable promise, and a buyer can picture the savings. Vague promises such as better efficiency rarely survive six minutes.

What the Winners of Any Pitch Have in Common

Look at what all five do. Each one names a painful, concrete problem, and each one shows a working product, not a slide. None needs a long preamble to be understood.

That pattern holds in smaller rooms too. A local lender, a first enterprise customer, and a seed investor all want the same three answers. What breaks without you, how do you know it works, and why are you the one to build it?

Build Your Own Six-Minute Pitch

Time yourself tonight. Open with the problem in one sentence, show the product in action, and close with the ask. If you need a longer setup, cut something. Then practice with a stranger instead of a friend. Friends fill in the gaps without noticing, while a stranger will tell you exactly where they got lost. Record one run, too, because watching yourself is uncomfortable but fast.

• Lead with a customer’s pain, not your technology.
• Show proof such as a pilot, a demo, or early revenue.
• Name the market in terms a stranger can picture.
• End with one clear request.

Many of these teams will also use the stage to recruit, because attention helps with hiring as much as with capital. Following startup funding news shows how narrow, technical ideas keep drawing big checks when the story is clear.

Is a Startup Competition Worth Your Time?

Do you need to win to benefit?

No. Making the cut gives you press, investor introductions, and a deadline that forces you to sharpen your story.

Why does equity-free money matter?

It lets you keep ownership while funding early experiments, which is rare at this stage.

What to Watch After the Winner Is Named

Expect follow-on funding announcements in the weeks after any big stage moment. Keep an eye on which of these five announce raises, pilots, or big customers in the weeks ahead.

Also watch the themes. If physical AI and security keep showing up in front of judges, that tells you where capital and customers are heading next. Use that signal to test whether your own idea sits in a growing lane or a crowded one. Then write down one change to your own pitch before the weekend.

The post Startup Pitch Competition Lessons From Disrupt’s Battlefield Finals appeared first on Under30CEO.

For founders, the headline is less about the check and more about the model behind it. A buy and build approach turns acquired software into steady earnings, then uses those earnings to buy again. If you ever plan an exit, these startup acquisition lessons show how buyers think about what they are purchasing.

The Deal in Plain Numbers

Here is how to think about the transaction. Berkshire will receive 10 million ordinary shares at $33.00 each, which brings its total ownership to roughly 4%. Add the earlier IPO investment and the combined commitment is about $930 million. The math checks out, since ten million shares at $33.00 equals $330 million.

The placement is still subject to customary closing conditions. Details are expected in a Form 6-K, a foreign issuer filing that the company will submit to the SEC.

How the Buy and Build Machine Works

Bending Spoons says it has followed one playbook for more than a decade, and it says it has not sold off any major company it bought. It acquires a product, reorganizes the team, rebuilds the technology, redesigns the interface, and sharpens marketing and monetization. Artificial intelligence now sits at the center of many of those changes.

Its portfolio spans productivity and collaboration software, media and file transfer services, and even pet tracking hardware. That range tells you the company cares less about the category than about whether a product has loyal users and room to improve.

Operational change is the hard part. Rebuilding a product means choices about which features stay, which prices rise, and which teams merge. Founders who sell to this kind of buyer should expect the roadmap to be rewritten, so negotiate what happens to customers and staff before signing.

Why a Patient Investor Matters

Berkshire is known for holding businesses for a very long time. Putting more money into a company that also refuses to flip assets suggests a shared view of time. Both sides appear to prefer compounding earnings over quick resales.

In addition, the second check improves flexibility. The company says its acquisition pipeline remains strong, and fresh capital lets it move when a seller wants a fast, certain close. Cash on hand is a negotiating tool, not just a safety net.

Founders can borrow this idea. Choose investors whose time horizon matches your own, because a fund that needs a quick exit will push you toward choices a long-term partner would avoid.

Meanwhile, the pace of AI acquisitions shows that large buyers are paying for capability as well as revenue.

What This Means If You Run a Small Software Company

Many founders assume the only good outcome is a venture-scale win. Bending Spoons shows another path, where a modest product with stable users can become an attractive target because someone else believes they can run it better.

Consequently, it pays to know what a disciplined buyer sees. They look at retention, support costs, pricing power, and how much of the product depends on you personally. If those answers are strong, your company is easier to value and easier to sell.

• Document your processes so the business runs without you.
• Track retention and churn monthly, not yearly.
• Test pricing before a buyer does it for you.
• Keep your cap table clean and your contracts organized.

These habits help long before any sale. Clean books shorten due diligence, and a steady monthly report lets you spot a problem while it is still cheap to fix. Lenders and investors respond to the same signals, so the work pays off whether or not you ever sell.

Understanding startup valuation math will also help you judge whether an offer reflects real earnings or hype.

Common Questions About Buy and Build

What does buy and build mean?

It means acquiring companies, improving their operations, and reinvesting the profits into more acquisitions.

Does this affect early-stage founders?

Yes, because acquirers like this set price expectations for small, profitable products.

Signals to Track Through Year End

First, watch whether the placement closes on schedule and what the Form 6-K reveals about use of proceeds. Next, see whether Bending Spoons announces a new acquisition soon after, since the company has said its pipeline is strong.

Finally, watch how public investors value a company built on operational change. If the market rewards it, expect more funds to chase small software businesses, which could raise prices for sellers and tighten competition for buyers.

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