Any investment helps when you’re a small business owner, especially when you consider the turbulent times of the current business landscape.

Grants give you the opportunity to grow and solidify your business, often as you see fit, without the worry of repayment. You can invest in new technologies like AI or grow your team numbers, for example.

Below is a roundup of small business grants with deadlines in October. I’ve not only included core details, but have also provided top tips for each grant to help your application stand out.

The Kenneth B and Stephen L. Tadlock Veteran Business Grant

• For: US military veterans or veteran spouses running employer-based small businesses
• Amount: $1,000
• Deadline to apply: October 15

This grant offers support to veteran-led or military spouse owned small businesses, giving them the support they need to grow into strong and sustainable companies.

25 finalists are selected overall, which gives you a good shot. On top of the $1,000 grant, you’ll also be able to join one of two busines programs, either with a focus on marketing, sales, operations, and finance, or on leadership skills.

Keep an eye on the additional eligibility requirements for this one. You must be a CEO, President, or Business Owner, have at least 1 year in business, have an annual revenue that doesn’t exceed $5 million, have between 2 to 100 employees, and an active US-based business.

To apply, head over to their website and complete the online application.

The 500 Fellowship

• For: Early-stage founders building for the US
• Amount: Up to $50,000
• Deadline to apply: Rolling basis

This grant says it’s for the top 0.1% of founders, “who’ve started building and thinking globally already.” It targets founders who are ready to put in the work.

You’ll spend four months in San Francisco with the rest of your cohort and learn from expert mentors and founders. This presents networking and mentorship opportunities, as well as an initial investment of up to $50,000 which can later become up to $1 million as you progress.

As well as an online application, you’ll also have to interview for a place.

Thrive Grant

• For: Contractors and trade subcontractors
• Amount: $5,000
• Deadline to apply: October 31

As well as a $5,000 grant, you’ll also get one year of free access to Breva, a construction pay app and cash flow software. This is ideal for construction contractors managing their own financial operations.

Similarly, you’ll be introduced to the general contractors and owners that are already a part of Breva’s platform, giving you opportunity to network. Plus, you’ll be featured on an episode of Breva’s show, which is provides some added exposure.

The focus for this application is demonstrating community impact. The grant also encourages nonprofits and associations that serve construction businesses to apply, alongside the other eligibility requirements. You can apply via online application.

Amber Grant

• For: Women-owned businesses
• Amount: $10,000
• Deadline to apply: October 31

This grant supports women-owned businesses. As well as receiving up to $10,000 as a monthly grant winner, you’ll also be eligible to receive one of three $50,000 annual grants as well, so the payoff is plenty.

Overall, the judges want to know about you, and your business dream. It advises to “share from the heart” and discourages corporate speak. All in all, authenticity seems to be at the heart of this fund.

You can apply online via the application portal. However, there is a $15 application fee to note.

How a Small Business Grant Helps Your Business

Running a small business in 2026 is expensive, and a little extra money in the bank goes a long way. Yes, a grant levels up your business in this sense, but there are plenty of other benefits to receiving one.

Firstly, the additional cash might allow you to expand your business in ways you haven’t been able to yet. For example, you may be able to better your marketing strategy, or implement AI to level up your hiring process.

Similarly, the networking opportunities are aplenty. While some grants accept several cohorts of businesses each round, others give you access to industry professionals and mentorship schemes.

Likewise, receiving a grant may help put your business on the radar of investors. Naturally, your reputation will increase and this will establish yours as a business to bet on. Really, there’s nothing to lose, and I’d encourage you to apply.

Times are quickly changing with AI. It’s overwhelming to keep track of everything going on, and take that from a tech writer. Increasingly, we’re seeing calls for better security, better training methods, and a better understanding of what the technology is actually useful for.

Navigating this landscape with confidence as a business leader means, above all, educating yourself. Not only will you increase your own understanding of what AI is capable of, but you can feed this back into your teams and wider company culture.

Luckily, there are plenty of free AI courses available this October to get you there.

Introduction to Open and Local AI with Coursera

Summary:

• Best for individuals looking to understand local and open-source AI models
• Intermediate level, some experience recommended
• 4 hours to complete with 5 modules, flexible learning available
• Shareable certificate available upon completion
• View at Coursera

The debate between closed and open-weight models has never been more important, especially as AI use becomes more expensive while embedding itself further into our day-to-day lives.

This course provides a foundation of understanding for both models, and then invites learners to apply this knowledge to real-world instances. In the long run, understanding which model works best for certain tasks could save you time and money and ensure best security practice.

Claude Code for Marketers with SkillsBooster Academy

Summary:

• Best for marketers wanting to take full advantage of Claude Code
• Beginner level
• 4 hours to complete 6 modules and 3 assignments, flexible learning available
• Shareable certificate available upon completion
• View at Coursera

AI features are a common fixture for today’s marketing teams, and now goes way beyond just tracking analytics. Claude Code, while it sounds like a tool only a developer would use, is a valubale asset in this arena.

This course says you will learn to build local marketing automations for SEO, social, email, and analytics using Claude Code, without the need for any coding experience. Likewise, you’ll be able to construct reliable, privacy-focused workflows with safety guardrails and client-ready handoff documentation.

Most importantly, this course highlights its commitment to local, private, and risk-free execution. This enables marketers to protect proprietary marketing data, adhere to brand guidelines and keep customer lists within their own environment.

AI at Work: Tools, Workflows & Real-World Application with HP

Summary:

• Best for learners with AI foundations that are ready to take the next step
• Intermediate level (HP’s AI Foundations, Practice, and Leadership course recommended before starting)
• 2-3 hours per week, overall 4 week course, flexible learning available
• HP-edX co-branded certificate available upon completion
• View course at edX

Figuring out what all the AI headlines are talking about is one thing, but actually seeing and understanding where AI fits into your professional life takes some trial and error. This course removes the initial experimentation curve, and gets straight into how you can use your AI knowledge in real-world scenarios.

The course description says learners will be “equipped to identify high-value AI use cases in their own contexts, build effective human-AI workflows, and apply prompt engineering with depth and precision.”

As for what skills you’ll develop, they include AI for writing, editing, communication, research, data analysis, and visualization. Likewise, you’ll learn how to evaluate and audit AI outputs critically, build a sustainable personal AI practice, and design efficient human-AI collaboration workflows.

AI for Data Visualization

Summary:

• Best for those wanting to use AI agents for data analysis and visualization
• Beginner level
• One hour course, flexible learning available
• Shareable certificate available upon completion
• View at Code Academy

Microsoft’s suite of AI tools are a key foundation for many businesses, and in this course, you’ll learn to implement data analysis in Microsoft Excel and Power Bi. You’ll also learn to apply best practices to avoid poor results, bettering your chances of success when using the tools.

There’s geniunely no excuses with this course, because it only takes an hour to complete. It’s taught over a series of videos led by industry experts, and you can jump in and jump out as you need to.

The Benefits of Free AI Courses

Everywhere you look, it seems we’re at a pivotal point with AI. Leaders have called for development to slow down out of fear for all of mankind, and recent autonomous breaches has once again put the question of trust in people’s minds.

However, businesses continue to introduce new AI tools, and its value continues to be lauded. Understanding these tools, including their limitations and risks, is vital for keeping yourself informed and competitive.

Free AI courses are flexible, and often free opportunities for leaders to understand AI. Exuding confidence and composure within your company when it comes to AI inspires a positive culture, where concerns can be raised, and productivity and security prioritized.

These workflows are from Tech.co’s weekly newsletter, The AI Strat. To receive free weekly workflows tried and tested by industry experts, subscribe to the newsletter. 

Even though you’d be hard pressed to find a company that isn’t using AI in some capacity, how you use AI is incredibly important. The best way forward is to use AI to reimagine your business’s existing capabilities, rather than just introducing it into the workplace.

Luckily, these workflows remove a lot of the initial guesswork. Each one provides an easy-to-follow graphic and proof of its success. Even if you don’t use them verbatim, take them as inspiration for your next AI workflow.

This month’s workflows are:

• Influencer campaign coordination workflow
• Turn conversations into knowledge bases workflow
• Build Ideal Customer Profiles from job listings workflow
• Lead follow-up workflow

Influencer Campaign Coordination Workflow

Recommended by Vivien Garnès and Kevin Creusy, co-CEOs at Upfluence

This workflow is great for brands that frequently work with content creators on campaigns, and want to streamline much of the research, planning, and tracking processes involved.

Drudge work like searching for creators, writing outreach emails, following up, and coordinating can be handled automatically. This lets brands focus on communications that directly impact creative output – including the vision and authenticity that will draw customers to you in the first place.

Turn Conversations into Knowledge Bases Workflow

Recommended by Joshua Gilley, co-founder of Loreify

Nobody wants to keep looking for the answers to the same questions, especially if that answer happens to be pretty straightforward. It slows you down, and can cause frustration.

Luckily, this workflow allows teams to turn recurring meetings and conversations into searchable knowledge bases, so gathering answers becomes frictionless.

Gilley uses this workflow at Loreify, which records long Dungeons & Dragons sessions. The workflow makes organized notes available post-sessions within 5-10 minutes.

Build Ideal Customer Profiles from Job Listings Workflow

Recommended by Maya Shamary, brand visibility expert at Semrush

This workflow enables businesses to build their Ideal Customer Profile (ICP) from public job listings. By identifying the skills and responsibilities a company is hiring for, you can find exactly where the need for your product exists.

Shamary says this workflow has turned 8-10 hours of manual analysis of open listings into a couple of minutes, and has reduced 7-14 days of customer research.

Lead Follow-Up Workflow

Recommended by Devin Elder, founder of Ark40 Consulting

Leads won’t be left hanging with this workflow, allowing your business to quickly capitalize on customer interest and avoid losing leads in the process. Companies are already using AI in sales, and the potential is great in this area.

Elder uses this workflow for clients. In one instance, a customer service representative saved 10 hours a week that used to go into manual data entry.

Understanding How AI Can Improve Workflows

At this stage of AI development, it’s widely understood that AI alone doesn’t make your business more productive. The best way to make the technology work for you is to integrate it into your existing workflows, but only where it’s absolutely needed.

In fact, with some studies suggesting AI can have an adverse effect on productivity, where you choose not to include AI is just as important as where you do implement it. Businesses should prioritize auditing and understanding their existing processes, before making strategic and security-driven decisions about whether AI would benefit.

Every week, I take an AI workflow that has led to genuine business impact from experts across industries, and create a ready-to-implement graphic so anyone can get started. If you’d like this in your inbox every Wednesday and more, subscribe to the AI Strat newsletter.

Want to work in Artificial Intelligence? Don’t fancy the grind of the commute and awkward water cooler conversations? Good news, there are plenty of AI roles out there which you can do from home, with big tech companies.

They pay well too, providing you’ve got the resume to justify being appointed to one of these roles, with the likes of Google, Microsoft and Nvidia paying top dollar to attract AI expertise and talent.

While remote work policies vary from company to company, there are still plenty of flexible employers looking for workers. Some aren’t quite as flexible as you might like, and we’ll point them out to you. Good luck with the search!

Remote AI Jobs at Microsoft

Microsoft, as you may expect, has plenty of AI jobs to offer. The company is currently making a massive push for AI across its product range, and is heavily invested in the technology. 

At the time of writing, there are over two thousand roles globally at the company with an AI focus, with a wide range of salaries, so there are plenty of options. Of those, just under three hundred are fully remote.

We’ve picked a few out that caught our eye, but for the full rundown, take a look at the dedicated Microsoft job site and filter by AI/0 days in office.

Principal Software Engineer – AI Infrastructure

• Base Salary Range: $165,600 – $296,400 / year
• Focus: Large-scale distributed runtime infrastructure for foundation model training, multi-agent orchestration, and GPU compute efficiency.

Senior Software Engineer – AI Frameworks

• Base Salary Range: $165,600 – $331,200 / year
• Focus: Optimizing, scaling, and maintaining deep learning compilation frameworks and open-source models in collaboration with research teams.

Senior Software Engineer – LLM Serving & Performance

• Base Salary Range: $119,800 – $261,000 / year
• Focus: Low-level kernel optimization, inference runtime latency reduction, and memory layout strategies for real-time model serving.

Software Engineer II – Copilot & Core AI

• Base Salary Range: $98,000 – $186,000 / year
• Focus: Integrating generative AI endpoints into Microsoft 365, enterprise agent orchestration platforms, and cloud microservices.

Principal Product Manager – Generative AI

• Base Salary Range: $158,000 – $282,000 / year
• Focus: Defining product architecture, evaluation benchmarks, and safety controls for next-generation Copilot tools and enterprise agent workflows.

AI & Data Security Specialist

• Base Salary Range: $112,000 – $218,000 / year
• Focus: Designing zero-trust governance frameworks, red-teaming protocols, and privacy-preserving data layers for enterprise LLMs.

Senior Data Scientist – Microsoft AI (MAI)

• Base Salary Range: $125,000 – $240,000 / year
• Focus: Post-training alignment, reinforcement learning from human feedback (RLHF), and evaluation metrics across large language models.

Solution Engineer – Azure AI Solutions

• Base Salary Range: $110,000 – $205,000 / year
• Focus: Direct enterprise client architecture for custom Copilots, Azure AI Foundry workloads, and high-performance inference pipelines.

Training Manager – AI Experiences

• Base Salary Range: $105,000 – $210,000 / year
• Focus: Developing organizational AI fluency programs, compliance training, and adoption frameworks for enterprise platforms.

HR Business Partner – Microsoft AI Division

• Base Salary Range: $106,400 – $222,600 / year
• Focus: Talent strategy, organizational planning, and human capital growth for Microsoft’s dedicated AI business unit.

Remote AI Jobs at Google

If you’re looking for a fully remote Google role, we’ve got bad news – they aren’t easy to come by. Google is one of the companies that fully embraced the post-pandemic return to office movement, and as such, work from home jobs are rare. 

Most Google roles demand that employees are in the office three days a week, but workers are granted two days remote each week. We’ve picked out some jobs below that are remote eligible. That doesn’t mean that these will be guaranteed fully remote, but rather that Google is willing to make exceptions to their in-office policy.

What this means will depend on the department, the hiring manager, and other factors, but it’s worth shooting your shot if you want to work for the big G. Check out the Google careers portal.

1. Staff Software Engineer, Agent Development Lifecycle (Google Cloud AI)
   • Focus: Architecting end-to-end orchestration pipelines for autonomous agents, tool integration, and LLM evaluation within Google Cloud Platform.
   • Base Salary Range: $207,000 – $301,000 / year + bonus + RSUs
2. AI Engineer, Advanced Solutions Lab (Google Cloud)
   • Focus: Partnering with enterprise customers to build, fine-tune, and deploy custom agentic solutions and generative AI models on Vertex AI.
   • Base Salary Range: $138,000 – $210,000 / year + bonus + RSUs
3. Senior Software Engineer, AI/ML (Google DeepMind / Core AI)
   • Focus: Developing low-level distributed runtime frameworks, model fine-tuning tools, and deployment pipelines for Gemini foundation models.
   • Base Salary Range: $161,000 – $239,000 / year + bonus + RSUs
4. Software Engineering Manager, SensAI (Sensors & AI)
   • Focus: Leading engineering teams building sensor fusion algorithms, heuristic ML, and spatial computing infrastructure.
   • Base Salary Range: $220,000 – $315,000 / year + bonus + RSUs
5. Research Scientist, Generative AI & Multimodal Models
   • Focus: Advancing core architecture design for multimodal LLMs, RLHF alignment methods, and next-generation reasoning capabilities.
   • Base Salary Range: $180,000 – $285,000 / year + bonus + RSUs
6. Technical Program Manager, Cloud AI & Foundation Models
   • Focus: Driving cross-functional release schedules for Gemini API integration, GPU/TPU cluster allocation, and safety evaluations across teams.
   • Base Salary Range: $138,000 – $198,000 / year + bonus + RSUs
7. Customer Solutions Engineer, AI & Data Analytics
   • Focus: Designing technical proof-of-concepts, custom LLM pipelines, and AI cloud architecture for strategic Google Cloud enterprise accounts.
   • Base Salary Range: $124,000 – $186,000 / year + bonus + RSUs
8. Product Manager, Vertex AI & Developer Tools
   • Focus: Managing product roadmaps for developer APIs, fine-tuning environments, and model governance toolkits on Google Cloud.
   • Base Salary Range: $155,000 – $248,000 / year + bonus + RSUs
9. Security Engineer, Generative AI Safety & Red Teaming
   • Focus: Conducting threat modeling, jailbreak vulnerability assessments, and automated red-teaming for foundational models prior to deployment.
   • Base Salary Range: $145,000 – $225,000 / year + bonus + RSUs
10. Data Scientist, AI Quality & Benchmarking
    • Focus: Designing quantitative metrics, human-in-the-loop evaluation frameworks, and model performance baselines across generative AI products.
    • Base Salary Range: $130,000 – $205,000 / year + bonus + RSUs

Remote AI Jobs at Nvidia

Nvidia is fuelling the AI revolution through its chips, so if you want to be at the forefront of the AI industry, this is a good place to make a name for yourself.

Nvidia’s approach to remote working is fairly flexible so you won’t have any issues finding a fully remote job. Getting it however, will be a different matter, as competition is fierce. We’re not trying to put you off, but the company receives 10 million applications a year, and only accepts 10,000 of them.

If you’ve got what it takes, check out the roles below, or head to the dedicated vacancies portal.

Senior Software Engineer – AI Runtimes & Inference (TensorRT / Triton)

• Focus: Optimizing low-level LLM execution, kernel compilation, and dynamic batching inside TensorRT-LLM and Triton Inference Server.
• Base Salary Range: $184,000 – $287,500 / year + RSUs + bonus

Principal AI Infrastructure Engineer – DGX Cloud

• Focus: Building distributed cloud platforms, cluster orchestration engines, and high-throughput communication layers for large-scale GPU clusters.
• Base Salary Range: $224,000 – $356,500 / year + RSUs + bonus

Senior Research Scientist – Foundation Models & Generative AI

• Focus: Fundamental research in multi-modal LLM architecture, synthetic dataset curation, post-training alignment (RLHF/DPO), and world models.
• Base Salary Range: $180,000 – $300,000 / year + RSUs + bonus

Senior Solutions Architect – Generative AI & Enterprise LLMs

• Focus: Partnering with enterprise clients to design production-grade deployments using Nvidia NeMo Framework, NIM microservices, and Retrieval-Augmented Generation (RAG).
• Base Salary Range: $148,000 – $230,000 / year + RSUs + bonus

Senior Deep Learning Software Engineer – Autonomous Systems / Robotics (Isaac AI)

• Focus: Developing computer vision, spatial AI, and physics-based simulation algorithms powering Nvidia Isaac Sim and Omniverse.
• Base Salary Range: $170,000 – $276,000 / year + RSUs + bonus

Technical Product Manager – NeMo & AI Microservices (NIM)

• Focus: Defining developer API roadmaps, enterprise model guardrails, and microservice architectures for deploying foundation models on containerized infrastructure.
• Base Salary Range: $152,000 – $241,500 / year + RSUs + bonus

Senior Developer Relations Engineer – GenAI & LLM Ecosystem

• Focus: Working directly with open-source AI maintainers, startups, and developers to accelerate model training and fine-tuning on Nvidia hardware.
• Base Salary Range: $144,000 – $224,250 / year + RSUs + bonus

AI Safety & Alignment Engineer – Guardrails & Red Teaming

• Focus: Designing automated testing pipelines, jailbreak defense systems, and input/output filtering frameworks (NeMo Guardrails) for enterprise deployments.
• Base Salary Range: $160,000 – $250,000 / year + RSUs + bonus

Data Scientist – AI Model Evaluation & Performance Benchmarking

• Focus: Creating standardized evaluation suites, measuring token latency/throughput across hardware setups, and benchmarking foundation model quality.
• Base Salary Range: $138,000 – $218,500 / year + RSUs + bonus

Technical Program Manager – Accelerated Computing & AI Software

• Focus: Managing cross-functional release pipelines across CUDA libraries, AI frameworks, and GPU cloud infrastructure teams.
• Base Salary Range: $135,000 – $210,000 / year + RSUs + bonus

Remote AI Jobs at Apple

Of the tech companies we’ve covered here, Apple is the strictest when it comes to working remotely. Simply put, it isn’t that flexible. You’ll find that most Apple roles demand employees are in the office three days a week, typically Tuesday to Thursday.

It’s been something of a contentious issue, no more so than in 2022, when Apple’s then Head of Machine Learning, Ian Goodfellow, threw in the towel with the company, citing its return to office policy. He said “I believe strongly that more flexibility would have been the best policy for my team.”

So there you go. If the Head of Machine Learning at Apple can’t catch a break and work fully remotely, there’s not much hope for anyone else. In view of this, we’ve listed some roles below that do offer two days a week remote. When it comes to Apple, that’s probably about as good as it’s going to get. See the full list at Apple Careers.

Machine Learning Engineer, Foundation Models (Apple Intelligence)

• Focus: Training, optimizing, and deploying on-device and server-based foundation models powering Apple Intelligence features across iOS and macOS.
• Base Salary Range: $170,000 – $295,000 / year + RSUs + bonus

Research Scientist, Generative AI & Multimodal LLMs

• Focus: Conducting fundamental research in vision-language models, reasoning capabilities, and post-training alignment (RLHF/DPO).
• Base Salary Range: $185,000 – $320,000 / year + RSUs + bonus

Computer Vision Engineer, On-Device Intelligence

• Focus: Building ultra-low-power, hardware-accelerated computer vision algorithms for visual search, scene understanding, and spatial computing.
• Base Salary Range: $160,000 – $275,000 / year + RSUs + bonus

Speech & Natural Language Processing (NLP) Engineer, Siri

• Focus: Enhancing conversational understanding, multi-turn dialogue management, and contextual personal intelligence for Siri.
• Base Salary Range: $155,000 – $265,000 / year + RSUs + bonus

Machine Learning Infrastructure & Compute Systems Engineer

• Focus: Building distributed training frameworks, GPU/TPU cluster management platforms, and model compilation toolchains for Apple Silicon.
• Base Salary Range: $175,000 – $300,000 / year + RSUs + bonus

Privacy-Preserving Machine Learning Engineer

• Focus: Developing Private Cloud Compute architecture, differential privacy mechanisms, and secure enclave processing for generative AI workflows.
• Base Salary Range: $165,000 – $285,000 / year + RSUs + bonus

Engineering Program Manager, Generative AI Platforms

• Focus: Managing cross-functional delivery schedules across research, core OS teams, hardware engineering, and privacy compliance.
• Base Salary Range: $145,000 – $240,000 / year + RSUs + bonus

Machine Learning Data Operations Lead

• Focus: Managing large-scale data pipelines, synthetic data generation, and human evaluation benchmarks for foundation model quality.
• Base Salary Range: $135,000 – $220,000 / year + RSUs + bonus

Applied Scientist, Personalization & Search Intelligence

• Focus: Designing intelligent ranking, recommendation, and indexing models across Spotlight, App Store, and Apple Music.
• Base Salary Range: $160,000 – $270,000 / year + RSUs + bonus

Product Manager, Apple Intelligence Experiences

• Focus: Defining user interaction paradigms, evaluation safety criteria, and feature roadmaps for system-wide AI capabilities.
• Base Salary Range: $165,000 – $275,000 / year + RSUs + bonus

Marketers — like everyone — are still waiting for AI to make a significant impact, with a new study finding that most professionals haven’t seen a big improvement just yet.

The study from Bain noted that only 6% of marketing professionals say that AI has influenced performance in a significantly positive way, despite 47% of leaders noting that it is a “core capability” for the industry.

So, how are these 6% of professionals making a marketing difference? They focus on providing top-down, centralized AI strategy, rather than just throwing tools at employees and hoping for the best.

AI Isn’t Moving the Needle for Marketers

According to the study from Bain, titled AI in Marketing: How Leaders Achieve Double the Revenue Impact, research found that only 6% of marketing professionals say AI is having a significant impact on performance.

This lack of significant performance improvements points to a disconnected strategy, not a lack of adoption. The study also found that 47% of marketing leaders say that AI is a core capability for businesses in the industry.

The report “surveyed 1,397 CMOs, chief financial officers, and senior marketing and finance executives across technology, consumer, retail, financial services, media, applications, education, landmark, and home consumer services.”

What Marketing Leaders Do Different

The study from Bain focused on marketing leaders — who are seeing 11%+ annual revenue growth — and marketing laggards — who record flat or declining revenue growth.

Marketing leaders had some very specific tendencies that separated them from laggards, generating AI results that positive impact their business.

They are 8.5 times more likely to experiment with AI, they are 1.5 times more likely to use AI to enhance customer personalization, and they are two times as likely to allocate more than 25% of budget to AI.

How Marketing Leaders Use AI

If you want to make sure that your business is in that 6%, we don’t blame you. After all, AI is expensive, and the last thing you want to do is waste resources on technology that isn’t moving the needle.

The study from Bain found that there are some specific steps that marketing leaders take to optimize their AI usage.

• Centralize AI strategy: Marketing leaders align all business priorities with AI, allowing the entire company to understand basic standards and protocols.
• Rebuild workflows and teams: Marketing leaders evaluate and reevaluate workflows in hopes of finding ways to improve them with AI.
• Focus on customer value: Marketing leaders know that the key to impact is produce customer value, so they align strategy with customers in mind.

Simply put, you can’t just inject AI into your business and pray that it has an impact. This technology is world change, which means you’ll need to change your world to get the most of it.

Check out our guide to AI training courses to learn more.

Maybe leave ChatGPT out of your enterprise purchasing decisions, because a new study found that these AI models are wrong 86% of the time when it comes to shopping.

Models like Gemini, Claude, Perplexity, and yes ChatGPT were all tested and were consistently found to provide incorrect prices and specs and suggest outdated products.

Even worse, the models weren’t just a little bit wrong; they really whiffed it. When asking about high-cost items like laptops, mattresses, and TVs, AI was incorrect by a median average of $300.

How Accurate Is AI for Shopping?

The study from Product.ai investigated the free and paid tiers for ChatGPT, Claude, Gemini, and Perplexity, asking each of them the same 220 shopping questions.

According to the report, the AI models provided conflicting and incorrect information in responses 86% of the time.  Even worse, comparison questions that specifically matchup products against each other were incorrect 97% of the time.

How Wrong Is AI for Shopping?

The study goes on to note that AI models aren’t just a little wrong when it comes to prices, features, and specs of high-end items like laptops, mattresses, and TVs. They were way off.

“When a price was wrong, it was wrong by a median of $300…There was almost no middle ground between exactly right and badly wrong.” – Product.ai researchers

On top of that, one out of every ten of these incorrect responses were wrong by as much as $500, which isn’t a small error; that’s a serious problem.

Why This Matters

Right now, this very second, there are people using AI to make purchases. One study from Bain & Company noted that around “30% to 45% of US consumers are using generative AI for product research and comparison.”

If that significant portion of shoppers are getting recommendations and pricing/feature information that is inaccurate, we could be talking about millions or even billions of dollars being misspent.

That doesn’t exactly create a positive customer experience. And in an era where trust is more valuable than anything for businesses, it might be better to leave AI alone for the time being.

Both OpenAI and Anthropic have this week revealed cheaper AI models, as competition heats up in the market, especially from Chinese models such as Alibaba and Deepseek.

The new models are claimed to be around 40-50% cheaper than previous versions, with both companies aiming to offer solutions that are more token efficient.

With the price of using AI on the rise for US businesses, and the threat of ‘Tokenmaxxing‘ burning through AI budgets, the move will be a welcome one for many.

OpenAI Launches GPT-6 Sol and GPT-6 Luna

Hot on the heels of its Astra model earlier in the month, OpenAI announced the addition of two new models, GPT-6 Sol and GPT-6 Luna, designed to be budget-friendly options.

The secret to the lower cost, according to OpenAI, is that these models are more efficient to run than previously, and as a result they’ve been able to set the price accordingly. As noted in the press release for the new models: “Improvements in caching and inference let us serve these models at lower cost, and we’re passing those savings directly on to users.”

According to OpenAI’s own benchmarking, GPT-6 Sol outperforms Claude Opus 5 at 9% of Opus 5’s cost per task. GPT-6 Luna goes even further, with an apparent 58% lower cost per task than its predecessor.

Factual error rates have also reportedly been improved. GPT-6 Sol makes ‘about half as many mistakes’ as its predecessor, which OpenAI puts a near Astra-level reliability, but at a much lower cost.

GPT‑6 Sol and GPT‑6 Luna are out now in ChatGPT Work and Codex, available to Plus, Pro, Business, Enterprise, and Edu users.

Anthropic Launches Claude Opus 5.5

Not to be outdone, Anthropic revealed its cheaper model on the very same day as the OpenAI announcement, with Claude Opus 5.

There are a range of improvements with the model, but undoubtedly the headline is that it’s 40% cheaper than its predecessor, marking a potentially massive saving for heavy users. Tokens cost less, and the model also requires few tokens per task.

In its own tests, Anthropic claim huge improvements over Opus 5. It reports that testers were able to build working games with just one prompt, and that another completed a 680,000-line code migration in a day, using the model. Another test saw Opus 5.5 tasked with reducing load times across a web app, and it was successful in 39 out of 40 instances, while the previous model made smaller improvements that ‘altered the apps behavior’.

Safety, something of a hot topic right now, has also improved, with Anthropic claiming the new model is much less likely to deviate from instruction, or make ‘hard to reverse’ choices.

Claude Opus 5.5 is available now.

Threat from Chinese Competition

The announcement of these low cost AI models from Anthropic and OpenAI seems to be a reaction to overseas competition, which is moving at a breakneck speed in a bid for AI dominance. China is at the forefront, with models such as Deepseek and Alibaba offering highly capable tools at a fraction of the cost of US models.

The rivalry between China and the US as both seek AI dominance is also generating safety concerns, with President Trump meeting China’s president Xi Jinping this week in Washington. The main topic of conversation is likely to be how to keep up the AI development pace safely without the need to slow down, something which many in the AI industry have called for.

The introduction of these lower cost models will be good news for businesses that are using AI heavily on a daily basis, but does it go far enough to stop users jumping ship and moving to even cheaper foreign models?

AI has improved productivity for a lot of individuals, but businesses haven’t seen the fruits of that labor yet. Why? Because employees keep lobbing AI slop grenades at each other.

What are AI slop grenades? The term has been gaining steam, with the CEO of Shopify mentioning it in an interview with CBS News, noting that it is “a bad thing.”

The term slop grenades refers to the all-too-common phenomenon that sees AI-generated work being completely unedited and unchecked before passing it on, leading to additional hours spent reviewing and correcting it.

AI Slop Grenade: Term Explained

An AI slop grenade is a term that describes AI-generated work completed by an employee that does not check, review, or edit the work before sending it on to other employees.

In essence, the AI-generated work — which we know can often be filled with errors — acts as a ticking time bomb, creating the appearance of accomplished work, but then exploding into a mess that requires additional work from other teams.

This phenomenon has been described in quite a few ways. Just a few months ago, many were describing it as the AI rework tax, explaining that any productivity benefits gained from using the technology were “taxed” by the additional work required to make it acceptable by normal business standards.

Shopify CEO: Slop Grenade ‘a Bad Thing’

The term is gaining notoriety because it was used by the CEO of Shopify in an interview with CBS News last week to describe the increasingly problematic trend he sees in the business world.

“You don’t really read it, and now it has to be reviewed by your colleagues, and they are like, ‘this doesn’t look right.’ You’re just letting AI do the work for you.” – Tobias Lütke, CEO of Shopify

With 40% of AI work often still requiring rework, it’s understandable that these slop grenades would have such a negative impact on productivity.

The Great AI Rollback

Just a few short years ago, Shopify was all in on AI. The CEO infamously told employees that they need to prove that they “cannot get what they want done using AI” before doing anything else.

Shopify isn’t the only company that has rolled back its AI commitment. Duolingo and Salesforce both infamously laid off a huge number of employees in favor of AI, only to later say that the dip in quality was untenable.

The importance of having a human in the loop is not lost on us, nor is it lost on tech CEOs, who are finally seeing that AI is not ready for the big time just yet.

If recent news has taught us anything, it’s that we no longer need to theorize about the possibility of AI systems going rogue. Recent episodes of AI-enabled attacks have led to warnings from prominent researchers, and calls from frontier labs to slow AI development.

However, AI isn’t just an adversary. It’s also been useful for preventing cyber incidents, which could make it harder to regulate.

All in all, cyber expert Brandon Dixon is unsure about how much slowing down will protect us against AI’s capabilities. He urged businesses to understand their own processes first and map out risks in a recent interview with Tech.co.

Recent AI Attacks Put Safety in the Spotlight

Days before Anthropic CEO Dario Amodei called for frontier labs to slow down AI model development, ex-Anthropic and ex-OpenAI researcher Jacob Coxon released a series of posts on X expressing fear for the future of humanity.

Among the passages, Coxon claimed AI leaders are “gambling with our lives” in their pursuit of superintelligence. His comments most likely would have joined the chorus of existing AI “doomers” as they’re called, had they not followed a series of highly concerning autonomous AI attacks.

Most recently, OpenAI disclosed several concerning behaviors exhibited during the testing of GPT-5.6 Sol. The model supposedly started to leave instructions for future versions of itself, telling it to conceal mistakes and misaligned behavior from users. If that doesn’t concern you, just remember what happened with Hugging Face.

To Brandon Dixon, co-founder and CTO of Ent, which helps organizations understand and protect against malicious actors and AI agents, we’ve moved beyond the theoretical stage of AI cyber attacks. This ultimately means the playing field has shifted massively, and what we do next is important, but also hard to figure out.

AI’s Positive, and Negative, Impact on Security

When I’ve interviewed cyber experts on AI, what we discuss is a kind of double bind. AI is facing both sides of the battlefield, in that it’s helping businesses detect and react to attacks faster, while mounting those attacks in the first place.

“The models being used to craft phishing lures are also being used to find bugs before they ship, improve code quality, and surface vulnerabilities in production systems before they’re exploited,” Dixon told me.

At the moment, it’s impossible to see a world where we fight AI, without using AI. “Speed and efficiency cut both ways,” Dixon told me. Essentially, the features that make AI so good at attacking us, also make it very good at protecting us.

Nevertheless, AI-powered cyber attacks are a serious concern. Using that speed and efficiency, attacks can happen faster, without giving humans enough time to detect them, and serious amounts of data can be lost.

“The limiting factor for attackers has historically been the ability to understand the data that was stolen,” Dixon noted. “But now, AI supercharges that.”

Policing AI is Becoming Increasingly Difficult

Cyber experts have been calling for a more careful consideration of AI’s security vulnerabilities for some time. However, this point in time does feel significant. Dixon said it’s because we’ve now seen these capabilities in action.

“There are documented cases of AI agents breaking out of sandboxes, taking unintended actions, violating corporate policies, or otherwise behaving adversarially. These systems have materialized and evolved extremely quickly,” he explained.

“We are only a handful of years into this technology, and its capabilities change materially every year. That makes it difficult to know where it is heading or to apply security and governance measures that will not become outdated shortly after they are deployed.” – Brandon Dixon, co-founder and CTO at Ent

Despite the technology moving faster and faster, the US government is yet to pass any federal laws around AI. Individual states like California have implemented some AI laws, but currently, AI isn’t equally regulated in the US.

And, given recent comments made by Trump following the slowdown call from AI leaders, it doesn’t look like any new laws will be passed through Congress soon. Mostly, this is because of rapidly developing Chinese AI models, which Trump has continually said the US needs to outpace.

However, many experts will say some government intervention on AI is needed, Dixon included. “I think the government should establish sensible rules that preserve innovation and progress while preventing clearly reckless or harmful uses.”

“The goal should not be to eliminate risk entirely because that would suppress legitimate research and defensive deployment. Oversight should focus on meaningful risks and measurable harms, not broad restrictions on the underlying technology,” he said.

Will Slowing Down AI Protect Organizations?

It’s difficult to think about what happens if we slow down. What’s most important to remember, for starters, is that even though AI leaders have committed themselves to slowing down, they could turn the other way, especially given the stance of the Trump administration.

For Dixon, slowing down AI won’t necessarily put us in a better place. “From my perspective, more time does not necessarily produce a better understanding of security vulnerabilities,” he said, though he does admit he can’t speak for frontier labs themselves.

While additional time could help review code, test software, or identify weaknesses, he added, organizations may not use the time as wisely as we assume they will. “If history is any indication, many will wait for attacks to begin before responding appropriately.”

It’s still not entirely certain what slower development will look like, should it happen. But, Dixon told me businesses shouldn’t use an inability to control all aspects of AI as a reason not to adopt it. To him, it doesn’t make any sense. But what also doesn’t make sense, is just “adopting AI, hoping for the best and not having any clue what people are doing.”

Instead, organizations need to understand how they actually operate. “It is not enough to identify sensitive data, intellectual property, or trade secrets,” he said.

He advises businesses to figure out “which behaviors are acceptable, which workflows deserve attention, how those workflows could be exploited, and how that exploitation would be detected.”

95% of respondents of a new study list AI fluency as a hiring requirement. However, a significant number of organizations are still reporting having made a bad AI hire.

Historically, employees have been encouraged to experiment with AI to find out how it could suit their specific needs. Now, standardized AI use is emerging, which will help hiring managers choose the right talent.

Wouter Durville, CEO of TestGorilla, spoke to Tech.co about what AI fluency means and how businesses can hire with this skillset in mind, all while avoiding common mistakes at the interview stage.

Companies Are Looking for AI-Fluent Job Candidates

TestGorilla’s State of AI Fluency study finds that 53% of hiring managers now prefer a candidate with high AI fluency over one with deep domain expertise. However, of those managers, 59% report having made a bad AI hire.

Wouter Durville, CEO of TestGorilla, highlighted how impactful an AI fluent employee can be. “I think people who are really AI fluent, the outcomes they generate are massive. It’s a big difference.”

Companies certainly want to hire with AI in mind, the study finds, but are often going about it in the wrong way. Durville explains how the study was borne from customers communicating to him how much they were struggling with the issue. Eventually, TestGorilla landed on the term “AI fluency,” one Durville knows is difficult to properly define.

“It’s really about getting those people who know how to use these tools in the right way, and in a responsible way. Who are agile, who are using different tools for different things, who are system thinkers that see the bigger picture of where to use AI and where to put the human in the loop,” he explained.

It goes without saying, though, that this definition will vary based on your company and its goals.

Businesses Are Moving Towards Standardized AI Use

The first step when AI started to make waves in companies was convincing employees to actually use it. Common advice was to let employees figure out the tool for themselves, find ways to embed it into their own workflows, and experiment.

Arguably, this could explain the rise in individual productivity compared to company-wide gains some studies reported. But, this tactic seems to have faded.

“For example, in product development, it becomes quite messy if everyone is using a different tool for coding.” – Wouter Durville, CEO of TestGorilla

Messiness aside, it makes it easier to police the technology if everyone is using it across repeatable, company-wise processes.

That’s not to say organizations should stamp out individual moments of AI brilliance, but standard practice could protect against costly data breaches or common AI mistakes.

In the case of hiring, it’s likely much more productive to have a standardized company practice when it comes to AI. That way, in interviews, hiring managers can assess exactly how a candidate has used AI, and whether it matches up to AI expectations within their organization.

How Can Companies Hire for AI Fluency?

As well as standardizing AI use, there are other tactics companies can use to test AI knowledge. The first is actually defining what AI fluency means to your organization, which 71% of respondents of the survey have already done.

Likewise, the study found 37% of organizations set the bar at tool awareness when hiring a candidate. Rather than putting a tick next to someone’s name when they mention ChatGPT or Gemini, Durville encouraged businesses to “go deeper,” and figure out exactly how the candidate has used AI.

“You want to actually test an example in an interview,” he explained. So, asking a candidate questions like, “tell me about a workflow you actually built, or something that you automated. How did you go about it, what went wrong, and how did you fix it?”

“If you ask those questions, if you peel the onion a bit or go a bit deeper, you’ll very quickly notice, okay, this person has no clue how to do this,” he said.

Furthermore, Durville said AI use — and how it’s going to be tested in interviews — should be related to the job role as much as possible. You’re likely to test a marketing executive on their knowledge of AI video editors, for example, but this is less useful when interviewing for an engineering job.

Overall, Durville has positive views about AI and how it will impact hiring. While the CV and cover letter have basically been killed, he told me, companies can use AI to give more opportunities to candidates at the interview stage.