Bill Grossso on Startups after AI

Over the past 25 years, Bill Grosso has been the CEO of 4 separate software businesses (2 venture-backed startups, 2 bootstrapped consulting businesses) and has been a C-level executive at 4 other companies. During that time, the cloud emerged, the idea of analytics and machine-learning at scale emerged, open-source software completely altered both the development and deployment landscapes. And now, of course, Generative AI is changing everything yet again.

In this video, Bill draws on his experiences over the past quarter century and talk about how startups have changed, how startup customers have changed, and how we build software has changed, leading into a discussion of the role of startups in the emerging business ecosystem.

Artificial intelligence is transforming software development, but perhaps not in the way most people think.

In this talk, entrepreneur and software executive Bill Grosso argues that AI is best understood not as a revolutionary new form of intelligence, but as the latest in a long line of productivity-enhancing technologies. Like compilers or garbage collection before it, AI dramatically increases what skilled engineers can accomplish. The real disruption comes from how this changes the economics of startups.

Coase’s Theory of the Firm

Grosso begins with an idea from Nobel Prize-winning economist Ronald Coase’s The Nature of the Firm: companies exist because they reduce the cost of coordinating work. As organizations grow, however, they also become more risk-averse. Preserving existing products and revenue streams often takes priority over pursuing uncertain opportunities.

The Role of Startups

That’s where startups fit into the ecosystem.

Rather than viewing startups as future unicorns, Grosso suggests thinking of them as outsourced innovation engines. They tackle problems that large companies recognize but cannot easily solve within their own organizational structure. In many cases, acquisition—not an IPO—is the natural outcome.

AI changes this equation by making software development dramatically more productive. Teams that once required 20 to 30 engineers can now accomplish similar work with four to six highly skilled people using AI-assisted development. Smaller teams mean lower capital requirements, faster product development, and shorter startup lifecycles.

How AI Changes Startups

At the same time, AI enables large enterprises to build many internal tools themselves.

Previously, developing an experimental application might have required a dedicated innovation team. Today, a single engineer equipped with AI can often build a working prototype. Because coordinating with outside vendors remains expensive, many projects that once would have become startups will instead stay inside the enterprise.

This shift has important implications for entrepreneurs.

1. Software alone is becoming a weaker competitive advantage. AI makes it easier not only to build products but also to copy them. Traditional software moats are shrinking, meaning startups must compete on customer understanding, execution, relationships, and speed rather than code alone.
2. Startup funding will adapt and evolve. Faster product development puts competitive pressure on lengthy fundraising cycles. Early-stage investors will increasingly finance rapid experimentation, while larger follow-on rounds will focus on helping companies establish market leadership before competitors catch up.

Grosso also emphasizes that AI does not eliminate the need for disciplined engineering. His own teams rely on detailed specifications, acceptance criteria, automated testing, multiple AI models reviewing each other’s work, and experienced engineers providing architectural guidance. AI accelerates implementation, but good engineering practices remain essential.

Finding Startup Opportunities

Perhaps his most practical advice concerns finding startup opportunities. Instead of inventing technology first and searching for customers later, entrepreneurs should ask large companies a simple question:

“What important problem do you already understand but cannot solve because your organization gets in its own way?”

Those answers often reveal the best startup opportunities.

The age of AI doesn’t eliminate startups. Instead, it changes their purpose. Success will belong to founders who identify valuable customer problems, leverage AI to move faster than ever before, and build companies designed for rapid validation, sustainable growth, and—quite possibly—an earlier, more profitable exit.

Advice for Entrepreneurs

For aspiring entrepreneurs, Grosso offers practical advice. Be skeptical of startup mythology, especially advice from venture capitalists who naturally promote high-risk strategies. Focus on solving problems that large companies already understand but cannot address internally because of organizational constraints. Use AI extensively for market research to validate ideas before investing significant time. Finally, recognize that startup success increasingly depends less on writing code—which AI commoditizes—and more on identifying valuable problems, understanding customers, and executing rapidly before competitors catch up.

Key Takeaways

1. Advice for Entrepreneurs
   • Be skeptical of startup mythology
   • Question the incentives behind startup advice
   • Consider smaller, earlier exits instead of pursuing unicorn status
   • Think of entrepreneurship as an iterative process rather than a one-time bet
2. Other Observations
   • AI changes the economics of startups more than the purpose of startups.
   • Small, highly productive teams can build sophisticated software.
   • Success increasingly depends on identifying valuable customer problems rather than simply writing code.
   • The winners will combine AI-enabled execution with deep customer understanding and rapid market validation.

Other Resources

• View slides [PDF]
• Ronald Coarse’s The Nature of the Firm [PDF]

Related Blog Posts

• Andrew Shindyapin: AI’s Impact on Software Development
• The AI Gold Rush: It Still Takes Teams
• The Hidden Cost of Replacing Junior Engineers with AI
• Mark Bennett on Using Claude Code for Application Development
• Mark Bennett: Using Claude Code in Teams
• AI in Action: Practical Automation by Alex Panait 
• Matt Trifiro on Lessons Learned using AI for Marketing
• Time to Market S01 E03: How Will AI like ChatGPT Impact B2B Founders?
• Alex Panait on Current Trends and Possible Futures for AI

The Hidden Cost of
Replacing Junior Engineers with AI

Summary: Organizations need fresh talent and long-term investment to stay innovative. Apprentices challenge outdated assumptions, while experienced teams build reliable practices. AI can assist routine work, but lasting competitive advantage comes from learning organizations, shared knowledge, sound engineering judgment, and continuous capability development.

This conversation is a continuation of “The AI Gold Rush: It Still Takes Teams” where we explore other aspects of AI tools on engineering processes, organizational design, product development. Also the ill-conceived management trend of replacing junior engineers.

Edited Transcript

Norbert Korny: There’s the rush for AI to replace humans, and especially the juniors. Do you see the impact on the job market in Silicon Valley?

Sean Murphy: That’s absolutely going on. I think that’s ill-conceived for a couple of reasons. I don’t think it’s going to work that way. And if you don’t bring on any novices or apprentices, how do you get journeymen and masters 10 years from now? In other words, that’s a recipe for going out of business.

Jeff  Allison: Well, it’s the same thing that we did with outsourcing, right? And then all of a sudden, you know, you outsource everything, and and then you go, okay, I need a power guy or I need a signal integrity guy, and you can’t find those guys. They’ve gone somewhere else.

Sean: You didn’t plant them five years ago to let them get better and work their way up.

Jeff: Right. You always assumed that someone else was going to do it, and there’s always going to be an unlimited supply of the talent you need. Sean, you’re right: bringing in young talent should be part of the culture of an organization if it wants to get better over time. We all get stuck in our ways, lapsing into “This is the way we’ve always done it, and it’s always worked.” Some bright kids come in, look around, and say, “Why are you doing it that way?” Which is great. It’s refreshing and gives you a lot of energy.  If you don’t bring in new talent, it may look great on the damn Excel spreadsheet for expenses, but fast-forward five years–maybe only three years–it’ll be a very different picture.

The numbers guys look at engineering today as a collection of costs and assume we can develop whatever we need when we need it. But view it as critical that they drive costs down. Well, that’s not necessarily true. Sometimes you have to make a huge investment early on to get a new technology under your belt so that you can reap the benefit in future generations of product. You can’t just add a significant new capability in one step. Trying to cut the cost right at the beginning doesn’t make any sense to me, but I’m not a CFO.

Newcomers bring fresh perspectives
that challenge established practice

Sean: I think you made a good point. Newcomers look at things with a fresh perspective  and they challenge established practice. Now, perhaps 80% of the time there are good reasons for traditional solutions, and they persist. But about 20% of the time people say, “How about that? The assumptions we made when we put this practice in place no longer hold, and we didn’t notice.” I don’t think that you can get an LLM to challenge you and say, “Hey, it seems like some of the assumptions implicit in your request or plan are incorrect, I think you need to check your premises or the basic assumptions behind your design decisions. Whereas a new person will go, “This doesn’t make any sense to me.” Now, that’s can be irritating, but sometimes it’s a grain of sand you can use to make a pearl.

Jeff: I was thinking about when we used to bring in new guys. They’d gone through the universities and were familiar with new tools and new methodologies. My worry here is that if these guys are learning these AI tools when they get their degrees, it may limit their ability to think outside the box and innovate. We need to be careful not to limit ourselves to what AI tells us. I think we need to watch out for that.

Sean: I agree, but the universities have a challenge there as well. The clock cycle on changes is running very fast right now. If I am teaching first-year calculus, differential equations, or another body of knowledge that’s been debugged over decades, the college-university system works great. But in this situation, where methods are being rapidly obsoleted, I think this argues more for an apprentice model. How do we attach juniors and others to the guys who are breaking the trail? How do we create an apprenticeship system and a community-of-practice model that encourages shared exploration, not just individual tinkering, to create a more effective learning dynamic?

Instead, the labs are selling a “tall thin designer” paradigm where one guy puts on a red cape and blue tights and does it all, encouraging the CFO to believe they can cut costs by letting go of a large chunk of their engineering or customer support staff. But the strange thing is that it’s the older, more experienced engineers who are getting better results. They can spot not only basic errors in generated code but also poor architectural choices the tools make. It’s led to the first example of reverse age discrimination I’ve seen in Silicon Valley in 40 years. Normally they tell the old engineer, “You’re too expensive, we can get a junior guy with twice the energy to do the job for half the salary.” Now we are replacing junior engineers and keeping the seniors

I’m not quite sure how to make the apprenticeship model work. We’ve had this long period of a kind of zero-interest-rate (ZIRP) paradigm, where we got used to things working a certain way. There are learning organizations.  NVIDIA seems like one, and I am sure there are many others. I think organizations oriented around learning will be able to digest these new technologies and move faster with them.

I don’t think we’re paying enough attention to how to build on initial breakthroughs. It’s less about the guy who gets the first 10x productivity bonus and more about reducing his insights and methods into an established practice. A learning organization is also a teaching organization, about fostering team-level and organizational practices that are more effective. To your point, Jeff, there’s a tension between encouraging people to compare notes and share what they have learned, encouraging wider adoption and additional refinements and improvements, and measuring just relative personal productivity. I don’t want to share what I’ve learned if I am facing a 20-30% layoff, because I’d be reducing my competitive advantage over my peers. Management is creating perverse incentives for shared exploration and shared learning.

Questions for Planning for a Paradigm Shift

Jeff:  Well, I think all of these issues are common to anything that’s early on a paradigm shift.  It’s easy if I’m starting from scratch, but if I’ve got a current roadmap, do I intercept what’s going on right now with this new technology, or do I look at something new, get my legs under that, and drive new skills back into other programs? And what’s the timing behind that? And who does that? Companies and organizations have to manage bringing on new talent, providing training, and developing new processes. Management asks questions like:

• What’s this do for my current roadmap and product portfolio?
• What does this mean for our longer-term strategy?
• What new  products should I start?
• How do I make this technology scalable over time?
• How does this affect my architecture?
• How does it affect my organizational structure?
• How should I evaluate our internal skills inventory?
• What mass training  do we need to offer to get people up to speed?

There’s just a whole list of things people need to kind of get their heads around. It’s not just about AI and how it’s going to make us magically more productive overnight. I think individuals may get an immediate boost, but figuring out how to make a product development organization more effective will take a while.

Sean: So Norbert, what do you see going on? You’re in a larger organization. How do you see this getting digested?

Norbert: Well, you need to have a deep understanding of systems and system design already,  because the tools don’t know how to build better systems.

Jeff:  That’s true. Tools don’t develop products, people develop products. They may enable you to develop more easily but you are still doing the engineering. Even with better tools you have to know what you’re doing.

Norbert: There’s a thin line between apprenticeship and blindly copying the response of the ChatGPT into the code. With this rush to embrace AI we have lost some of that and I don’t know if we are encouraging juniors to understand and make improvements on the ChatGPT output. Because at some point we just run out of seniors.

Sean: Early in the development of a new field or discipline information is fragmentary and contradictory. We are working on this hazy frontier, which we think is jagged  because of our ignorance, and we need to be organized in translating our exploration and tinkering into new methods that are reliable. You’ve got fragments of understanding you need to verify to untangle the contradictions, mark the blind alleys, and remix to find a reasonably reliable approach.

That’s not something that an LLM is going to help you with because there’s not a knowledge base you can train it on. If you want to teach somebody an established body of knowledge that’s been debugged, something like algebra, then I can use an LLM. But in a rapidly changing field where the frontier is mutating rapidly, there is not a lot of reliable information that’s written down. There are firsthand accounts, but many will contain errors and oversights despite the best efforts of the people involved. You cannot really Google for information that’s not well structured and expressed in common terminology. This is where apprenticeships and community-of-practice models are more effective, because much of the new information is shared only in conversations, emails, and informal briefings and presentations. Or it’s the result of watching someone else do something and gaining insight.

Jeff: So I give an agent a design challenge, and they write a piece of code for me. Part of the problems we used to have in engineering was the lack of reuse, right? So, someone would develop some really smart piece of code or smart circuitry, right? Then we would ask, “Why aren’t other people using it in their products? Why don’t they take what’s been debugged and put into production and use it to save time and effort?”

Well, part of the problem was that it wasn’t packaged in a way they could use. So they’d have to talk to the engineer and quiz them on some critical details they neglected to write down because they were trying to hit a ship date, not develop a reusable design element. And the developer may have worked on this design block two or three years ago and they are on a new project with new deadlines and they are not measured on helping out a different project. Sometimes new test cases have to be developed for a slightly different use case the block will now be applied to, and this involves more questions and perhaps some debugging of why it’s not working.

If the AI tools can not only generate the code but generate accurate documentation that matches it and test cases that enable reuse then it may have something to offer. Do you think AI will help with reuse?

Norbert:  It might.

Jeff: How do we have conversations around design reuse and standardization? How do we introduce methods for libraries and standard practices that align with our culture? How do we manage the security issues: both in terms of leakage of important intellectual property outside of the organization and risks introduced by subtle defects in the code that is generated?

Moving from Breakthrough to Beaten Path

Sean: I think the challenge we are talking about is moving from breakthrough to beaten path, from an artisanal proof of concept or prototype to an industrial-strength process. You have people exploring in many different directions, making unique discoveries and gaining distinct insights. But they are not necessarily compatible because they proceed from different perspectives and can diverge further over time.

What I’ve seen is that shared methods are typically negotiated outcomes. You need to get it documented, and then people have to look at that and say, “Okay, here are the strengths and weaknesses of this approach. Here are the strengths and weaknesses of that approach.” You can support multiple methods depending on the problem domain you’re working in.

Norbert: Right, but if AI can help facilitate that, it’s a good thing. The goal is better methods that yield better products faster, not smaller teams.

Jeff: Business guys are going to drive the businesses. They’re going to give engineering the amount of dollars they think engineering needs, which is rarely enough. So the engineers say, “Well, we can do this now, but it’s going to take longer. Or we won’t come out with full functionality in six months, we can only hit 60% at this level of funding.”

But those conversations always happen. What I’m trying to figure out is how does engineering become more efficient using this technology? My other concern is if you have something generating lots of code, how do you test all of it? Because you don’t want AI developing test programs for the code that it generated; that’s basic engineering practice. If someone develops a circuit block or a piece of code, you can have them write a basic unit test, but you need others also to write tests. So having AI test its own code doesn’t make sense.  So I don’t know how all of this will work out: a lot of code is being developed very quickly now. How do you develop a test process around that to verify that all these new modules work together? They may work on their own, but what about at the system level?

Norbert, say a build fails at 1 a.m. because someone checked in a piece of code that broke something, or you have all these conflicts. Now someone has to wake up, go through all the logs, look at the code, and untangle it.  Will AI help there, or make it worse, or how are people going to struggle with that?

AI is Tireless Diligent Intelligence

Sean: I think we should look at “AI” more as a tireless or diligent intelligence; I think in the break-the-build case, a simple set of rules could be applied consistently to catch basic mistakes. You attack the bottom 20-30% of the problem with AI instead of trying to achieve a breakthrough.  Ask it to do the bookkeeping and basic blocking and tackling.

My fear with the labor cost savings focus is that I have never worked in an engineering organization where the leader gathers everyone together and says, “This is a great team. We set very high standards, so I want to let you know that a year from now, only 60% of you will be here.”  Salespeople are used to that. When you bring in a new sales force, it’s kind of implicit that if they don’t make their numbers, they’re gone. But engineers are not normally managed that way because they’re working in teams, and you can’t always trace who contributed what. There are ways to assess contribution, but it’s more at the team level. I worry that this focus on cost reduction is going to prove extremely corrosive to leveraging the breakthroughs the pioneers make, using them to build on the early guys, and actually raising the standard of performance.

Jeff:  Don’t you think this will work it’s way out over the next nine to eighteen months? We’ve seen people come in with a new business model they claim will dramatically cut costs. At some point they realize it’s not going to work. It’s going to be painful, right?

Sean: I think it’s going to take a couple years and it’s going to be very bad in the interim.

Cost reduction thesis  has no basis in reality

Jeff: I’ve been through headcount reductions, not because we brought them on ourselves but because of things that happen  in the market or the national economy. It’s very difficult when you commit a program to develop a product that you promise to customers or a market, you need to size it correctly up front. When you don’t, you are in for a lot of pain through the whole development process. My concern is people have been advancing this cost reduction thesis that has no basis in reality.

Sean: There’s a category of algorithm that’s called a greedy algorithm: essentially, you look at the choices that are immediately available and choose the best one. Typically it’s the lowest cost or shortest distance. You don’t look ahead multiple moves or consider the consequences; you pick the best move and keep picking it at each turn. For certain problems, these algorithms are optimal. But in general, you cannot cost-cut your way to greatness, which seems to be what some firms are trying to do. We are also starting to realize the implications of a code base that’s been auto-generated, but I don’t think we’ve seen the full consequences of that yet.

Jeff: It’s going to take time to play out. I hope most companies are starting to put this technology in play internally on smaller projects or a subset of projects to get it ironed out. Then they’ll understand the cost structures and what’s needed to use AI in their mainstream product line.

Sean: I don’t know how much controlled experimentation is being done. It does not seem like many companies are used to acting as learning organizations. It’s certainly not the forte of most IT organizations, who are more comfortable with vendor-supplied cookbooks and predefined migrations. The challenge many companies now face is that the rate of change in the industry has increased by at least an order of magnitude, and their internal control and planning structures aren’t set up for that.

Norbert: As far as testing goes, you can make another AI model to test the codebase just to have a different point of view, but the bigger problem is that humans need to review that code.

Most of us are just not wired for that. We are wired for writing code, and now suddenly we need to read a lot of it.

Jeff: Yes, read it and understand it.

Hallucinations and Psychosis

Norbert: Another challenge is that a few hallucinations, sometimes more than a few, are mixed into all of this generated code. There is a strong temptation to skip climbing the mountain and get there in one jump, but it rarely works out. Hallucinations are intrinsic to LLMs.

I think LLM-induced psychosis is going to be recognized as a problem. Mr Dunning and Mr. Krueger will be excited because sometimes the LLM hallucinates exactly what you would expect, just to validate your worldview. You can get caught in a feedback cycle where your false beliefs are reinforced, further distorting your thinking..

Jeff: Do you think so? Aren’t companies going to create their own standards and their own learning modules? First, for security reasons; second, for proprietary information; and third, in the belief that we’ve created it so we know what’s in it.

Norbert Security concerns for malicious code will be a big part of this. Another risk that’s getting more attention: how do you protect your source code when you build using a mainstream AI model, how do you prevent them from stealing it? From what I’ve read, this has already happened multiple times. You just can’t rely on some AI provider.

Jeff: But if I were running a company and had to invest in creating my own LLM model that’s going to take some headcount and a serious investment, won’t it?

Edge or On Premises LLM Models

Norbert: You may not need to build your own model. You may be able to protect against data and IP loss by running a model on premises. We moved everything to the cloud on the theory that data was stored safely and redundantly for disaster recovery. But the AI firms may face different incentives than the SaaS vendors.

Jeff:  Reminds me how social media firms told us everything we posted remained our data and no one else could use it. Now we find out the AI companies were strip mining all that content plus all of the books and artwork for their generative models.

Sean: I wonder if there’s a pattern match to the web server code that’s now essentially all open source for Internet-facing stuff for a variety of reasons. I think the other thing that happened was the big providers had a good enough business, and it would have been obvious if they were stealing from their customers. So, for the most part, as far as we know, they didn’t steal our data or code: Amazon hasn’t launched 40 other applications that look a lot like those that were formerly hosted on AWS. But the incentive set facing the large model guys, I think, is very different, and you’re going to see more people running open-source models locally for a variety of reasons.

Jeff: But if they run numbers on what it would take to do this internally, I’m sure that’s more than just a handful of people, right?

Sean:  So the basic model can be a barn raising, a group collective project, and then you feed it your own data privately to extend it. And if I use an open source model and then extend it, then that may not cost a billion dollars. There’s a lot to play out here, and I think we have at least one round of bankruptcies or reorganizations here before 2030 or so.

Jeff: But it’s the same thing though. At the end of the day, this technology needs libraries and a whole stack of supporting tooling and data. We know that historically companies created software libraries, hybrid libraries, corporate standards, and everything else. I mean, that’s a huge investment, right?

Norbert: Basically, it’s libraries of the skills, and anyone can publish a skill, some may be malicious but they get included by default.

Jeff: Another thing, I want to ask: let’s take Company A and Company B who are competitors. They’re competitors, and they’re using the same toolset. Where’s the competitive edge? How does Company A become more innovative than Company B if they are both using the same libraries, the same tools. The outputs are, in my mind, probably going to be the same, not so different because it’s all based on the same thing. So where’s the innovation, and where do I get competitive advantage?

Norbert: Well, this is a great question because what’s stopping you from building something like Facebook when you have all the tooling at your hands?

Sean:  But it turns out that building the Facebook application is not the barrier to the next Facebook. It’s the fact that they’ve got a massive installed base.

Related Blog Posts

• The AI Gold Rush: It Still Takes Teams
• Expertise Acquisition Light Speed Barrier
• Organizing Your Experiment Log
• How To Run Experiments That Improve Your Business
• Jeff Allison How to Drive Innovation and Meet Commitments
• Experiments vs. Commitments
• Good Post-Mortem Questions Spark Learning
• Six Activities for Learning New Skills and Tools
• Impatience For Success Works Against Learning
• Buying a Map vs. Learning to Explore
• Entrepreneurs Need To See With Newcomer’s Eyes And Ask Stupid Questions

Image Credits:

• Overview Graphic generated with ChatGPT by Theresa Shafer.
• Antelope Canyon (c) Kevin Murphy, used with permission.

This post was republished on LinkedIn as https://www.linkedin.com/pulse/hidden-cost-replacing-junior-engineers-ai-sean-murphy-lig3c/

Expecting Too Much Too Soon

One mistake entrepreneurs make is expecting too much too soon.

In your mind, the product works. The business model is clear. Prospects immediately understand the demo, see the value, and money starts coming in.

But a useful rule of thumb is: “Nothing new ever works.”

Even getting prospects to pay attention requires iteration. Before someone agrees to watch a demo or evaluate your product, they first consume your message, positioning, or product description. If that does not resonate, the demo never happens.

And if getting attention is hard, getting people to pay money is ten to a hundred times harder.

Early-stage success requires target practice: experimentation, tinkering, careful listening, and steady improvement. You have to pay attention not only to what prospects say, but also to what they avoid saying.

A good way to protect yourself is to develop Plan B and Plan C before you start Plan A. If you talk to 20 people who fit your target customer profile and none react positively or offer constructive feedback, it is time to shift.

One of the hardest parts of entrepreneurship is that many things you feel confident will work generate no response at all. You have to learn to persevere in the face of a 90% failure rate.

Related Blog Posts

• Why is it so hard to get your first ten customers?
  • We like to use the analogy of a combination lock to describe the early startup learning process: there are four key aspects of your product that need to be in sync: Need, Impact, Customer Segment, and Message. Fundamentally, this startup combination lock requires four correct settings to open. To keep it open as your product and customer needs evolves they need to stay in sync.
• Ryan Waggoner: Maybe Startups Are So Hard Because We’re Doing Them Wrong
  • Investors naturally gravitate towards founders who either hit a billion dollars in a few years, or die trying (sometimes literally), and then investors and founders both are incentivized to craft this story that they only way to win is to win big, fast, and with all your chips on the line.
• New Market Exploration
  • New market exploration is one of the most difficult customer development challenges that entrepreneurs face. It requires strong interview and sense making skills. It also requires a commitment to carefully observe and document customer behavior–with a willingness not only to “go and see” but to have the imagination to ask “what if” to explore what could happen if you would remove one or more constraints imposed by the status quo. Entrepreneurs must see underneath the surface behavior of prospects and their self-reported problem descriptions to understand the cognitive model for a task or job to be done by your product. Developing a first prototype–or even what you think may be your first product–does not mean that the need for further interviews and imagination goes away, if anything it’s just the next basecamp for further exploration. New market exploration requires not only the confidence to form a vision of a solution but the willingness to be surprised and to admit where your mental map does not match the actual territory, integrating new “ground truth” to improve your product and business model.”
• Marshall McLuhan: How To See The Future
  • “To be a futurist, in pursuit of improving reality, is not to have your face continually turned upstream, waiting for the future to come. To improve reality is to see clearly where you are, and then wonder how to make that better.”
    Warren Ellis “How To See The Future” (Sep-7-2012)
  • “I have gradually come to appreciate that the really important predictions are about the present. What is happening right now, and what is its significance?” Robert Lucky
• Ten Mistakes Early Stage Bootstrappers Often Make
  • #7 Expecting Too Much Too Soon: Not Planning for “Target Practice”, Iteration, and Improvement

Sign up for an Office Hours Session
To Make Sense of What You Have Seen and Heard.

An early market can be like a combination lock, you may have discovered one or two of the keys to opening it, but it’s hard to assess partial progress. Exploring a market it’s like listening to 15-20 second snippets from songs you have never heard before, you have to make sense of what a number of folks are telling you: little sounds familiar and it’s hard work. If you are having trouble making sense of what you have learned from your initial early market conversations, please sign up for an no cost no obligation office hours session and we can help you make sense of what you have seen and heard. If you are preparing to scout a new market and would like a briefing for a descent into chaos, you are also welcome to sign up for an office hours session.

Image Credit: (c) Bondars (Licensed from 123RF Image ID : 33650284)

The post was republished on LinkedIn as https://www.linkedin.com/pulse/entrepreneurs-often-expect-too-much-soon-sean-murphy-t9bic/

July 4, Independence Day 2026

“History by apprising them of the past will enable them to judge of the future; it will avail them of the experience of other times and other nations; it will qualify them as judges of the actions and designs of men; it will enable them to know ambition under every disguise it may assume; and knowing it, to defeat its views. In every government on earth is some trace of human weakness, some germ of corruption and degeneracy, which cunning will discover, and wickedness insensibly open, cultivate, and improve. Every government degenerates when trusted to the rulers of the people alone. The people themselves therefore are its only safe depositories. And to render even them safe their minds must be improved to a certain degree.”  Thomas Jefferson in “Securing the Republic” (1784)

Jefferson quote suggested by DataRepublican in “Our 250th Anniversary” who highlights “Every government degenerates when trusted to the rulers of the people alone,” as a core responsibility for all citizens.

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John Adams: I have come to the conclusion that one useless man is called a disgrace; that two are called a law firm, and that three or more become a Congress!

While some quote collections ascribe this Adams, it’s actually from the movie “1776” screenplay by Peter Stone. I watched 1776 earlier this year, it’s available on YouTube, and was surprised how historically accurate it was. The concept of a “law firm” in Revolutionary times was not really how lawyers practiced, they were normally solo practitioners with a clerk and perhaps an understudy/apprentice (there were no written bar exams until mid-1800s). Even my grandfather, born in 1888, learned law by apprenticing himself to a practicing attorney. The ABA did not successfully lobby legislatures to require law degrees until 1930s.

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One Mistake Entrepreneurs Make: A Great Product Will Sell Itself

One mistake entrepreneurs frequently make is believing that a great product will sell itself.

Many technical founders assume that if they build something innovative, customers will naturally discover it, recognize its value, and buy it.

In my experience, even excellent products need deliberate sales, marketing, and customer education efforts to succeed.

Customers are just like you: busy and focused on their own priorities. It may be not immediately obvious to them what problem the product solves, how it differs from alternatives, or why they should change their current behavior to use it.

Entrepreneurs must actively engage prospects, listen to their needs, explain the value of their solution, and provide measurable proof of value.

Early sales conversations provide important feedback that helps refine both the product and the messaging. Many successful startups did not grow because their products were perfect: they grew because founders spent significant time talking to customers, learning from their suggestions and concerns, and adapting their offerings accordingly.

It’s essential that your product provides value to a customer, but value still needs to be communicated, demonstrated, and delivered.

Don’t believe “If you build it they will come.”

Entrepreneurs who combine product development with customer development, marketing, and sales are far more likely to achieve sustainable growth.

Three Very Dangerous Assumptions

1. Prospects are willing to explore your website for more than a minute even if they cannot see something that they can use.
2. Because your team finds your product concept compelling it must be the case that what you have is so intrinsically compelling that you don’t have to develop a real model for the value that you offer–and to set a price that reflects that value.
3. People will pay you for an abstract or fuzzy set of benefits.

Related Blog Posts

• Vision Is Critical But Avoid The “Field of Dreams” 
• How Do I Make Sure I Understand the Customer’s Problem and Present a Vision of a Solution?
• Balancing Engineering Vision vs. Customer Expectation
• Introducing Emerging Technology
• Micah Boster on Market Insertion
• Chalk Talk: Sarasvathy’s Effectuation Model for Startups
• How Founders Can Learn Sales on the Job

Quotes for Entrepreneurs Curated in June 2026

I curate these quotes for entrepreneurs from a variety of sources and tweet them on @skmurphy about once a day where you can get them hot off the mojo wire. At the end of each month I curate them in a blog post that adds commentary and may contain a longer passage from the same source for context.

My theme for this month’s “Quotes for Entrepreneurs” is initiative and effective action.

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image source: 123rf.com/profile_gannvector 90869038

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2. Don’t fire all of your ammunition at once.
8. You can’t be a plan with no plan.
28. The test of moral ideas is moral results.
41. “In moments of crisis, the initiative passes to those who are best prepared”

Morton Blackwell’s “Laws of the Public Policy Process“

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Self-Responsibility
I am responsible for

• my existence.
• the achievement of my desires.
• my choices and actions.
• the level of consciousness I bring to my work and my relationships.
• my behavior with other people–co-workers, associates, customers, spouse, children, and friends.
• how I prioritize my time.
• the quality of my communications.
• my personal happiness.

I accept that no one is coming to make my life right, or save me, or redeem my childhood, or rescue me from the consequences of my choices and actions. In specific issues, people may help me, but no one can take over primary responsibility for my existence. Just as no one else can breathe for me, no one else can take over any of my other basic life functions, such as earning the experience of self-efficacy and self-respect.

The need for self-responsibility is natural; I do not view it as a tragedy.

Nathaniel Branden “The Six Pillars of Self-Esteem” (1994) [Archive (PDF)]

I think entrepreneurs commit to self-efficacy.  They help others and accept help from others, they commit to earning a living based on an exchange of value with others, but they reflect on outcomes–satisfactory and unsatisfactory, wins and losses, failures and successes–in order to learn from past mistakes to avoid them in future and to build on demonstrated strengths and capabilities. Branden defines the six pillars as:

1. Living consciously
2. Self-Acceptance
3. Self-responsibility
4. Self-assertiveness
5. Living with a purpose
6. Personal integrity

I originally curated this in July 2025; someone retweeted it today and I realized it apply equally to initiative and personal agency.

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“Be the hero of your own movie. If your life was a movie and it started now, what would the hero of your life’s movie do right now? Do that. ”

Joe Rogan “Be The Hero of Your Own Movie”

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“It will never rain roses: when we want to have more roses, we must plant more trees.”
George Eliot in “The Spanish Gypsy”

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“No man’s life, liberty or property are safe while the Legislature is in session.”
Gideon Tucker in “Final accounting in the Estate of A.B. (1866)”

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“Organizational success is downstream of a culture of urgency that considers a problem important to solve and is willing to work hard on solving it.”
Brian Potter (@_BrianPotter) in “How Long Does It Take to Plan a Bridge?“

Potter is a good analyst and tackles a number of construction-related questions on his substack. One approach he favors consistently, which entrepreneurs can learn from, is to look at an individual problem as part of a population of related or similar problems. He then hunts for the characteristics that drive a variance in outcomes.

Mark Twain observed, “History never repeats itself, but the Kaleidoscopic combinations of the pictured present often seem to be constructed out of the broken fragments of antique legends.” Which time has simplified to “History does not repeat itself, but it does rhyme.” Look for problems that rhyme with the one you are trying to solve.

“The solution to a problem that rhymes with yours will often spark new thinking on your part, enlarging the set of viable options you can consider.”

Sean Murphy in  “Making Business Decisions in Uncertain Times“

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“This happens more often than you might realize — not just in mercenary bands or other military contexts, but also in businesses large and small, in churches and in amateur sports leagues and even in individual family units. Somebody wakes up with the realization that the whole thing is headed towards shipwreck, and says to themselves, “Well, I better take it over.” At the moment we wake up, most of us don’t realize the dreadful truth: once we take charge, there is no walking away from it with honor intact, and when the outcome is finally determined we will own it for good or for ill.”
John Psmith (@PsmithBooks) [pseudonym] in a “Review of Xenephon’s The Anabasis”

I used a passage from Xenephon‘s Anabasis in “Burn Your Boats But Not Your Bridges.”

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“People always ask me, “What’s the secret to being a successful CEO?” Sadly, there is no secret, but if there is one skill that stands out, it’s the ability to focus and make the best move when there are no good moves. It’s the moments where you feel most like hiding or dying that you can make the biggest difference as a CEO.”
Ben Horowitz in “The Hard Thing About the Hard Thing”

Sometimes you have to make a move because standing still is less and less tenable. I originally curated this quote by Edwin Louis Cole in April 2012:

“You don’t drown by falling in the water; you drown by staying there.”
Edwin Louis Cole

I blogged about “The Best Bad Plan” using this scene from the movie Argo as a point of departure.

Tony Mendez: “There are only bad options. It’s about finding the best one.”
Warren Christopher: “You don’t have a better bad idea than this?”
Jack O’Donnell: “This is the best bad idea we have, sir, by far.”

Reconciling yourself to the “best bad plan” is a key skill for entrepreneurs.

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“Planning is important, but the most important part of every plan is what to do when things don’t go according to plan. A future filled with unknowns is everyone’s reality. A good plan has a margin of safety with funds in reserve and a flexible timeline that enable intelligent reaction and improvisation when needed.”

Condensed from a much longer section that begins with “2. Planning is important…” by Morgan Housel in “the Psychology of Money”

I have blogged about the value of maintaining a firm grasp on contingencies in “Constructive Pessimism” and “Hope for the Best, Prepare for the Worst.”

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“Actions have consequences. So does inaction.”
Frank Sonnenberg in “When Silence Says it All” (collected in  “The Path to a Meaningful Life“)

A longer excerpt

“Actions have consequences. So does inaction. Restoring honor and integrity begins by standing up for your beliefs, holding people accountable for their actions, and — above all — leading by example. Virtue isn’t about demanding more from others; it’s about expecting more of yourself. It takes courage to speak the truth when silence is easier, honesty to do what’s right even when it’s difficult, and compassion to help those who can’t help themselves. That’s how we rebuild trust, restore decency, and remind the world that character still matters.”

Frank Sonnenberg in “When Silence Says it All” (collected in  “The Path to a Meaningful Life“)

Three related quotes

• “If you choose not to decide, you still have made a choice.”
  Neil Peart “Freewill”
• “A decision unmade is a decision made to stay the same.”
  Mignon McLaughlin
• “When you have to make a choice and don’t make it, that in itself is a choice.”
  William James “The Principles of Psychology”

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“When there’s a big disappointment, we don’t know if that’s the end of the story. It may be just the beginning of a great adventure.”
Pema Chodron in “When Things Fall Apart: Heart Advice for Difficult Times“

Next time you think of giving up, remember this photo of Elon in 2008.
Szymanski tweet

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“Tom Peters: These Playmobil toys of yours… they do amazingly well, all over the world. So what’s their secret? What do they do that’s so interesting?

Horst Brandstätter: It’s not what the toy does that’s interesting. It’s what the child does with the toy that’s interesting.”

Recounted by Hugh Macleod in “The Product Does not get to be Kick Ass Until the User Kicks Ass” and his ebook  “Authenticity is the New Bullshit” (2013)

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“You have to frame the world in order to interact with it.

Chaos is what you see when you don’t know what you are looking at.”

Jordan Peterson in “2017 Personality 15: Biology/Traits: The Limbic System (minute 42)“

It’s hard to take the initiative when all you see is chaos. Agency is enabled by your ability to diagnose a situation and identify opportunities for improvement or at least mitigation and cutting of likely losses.

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“Creativity is the ability to come up with original, useful ideas. Good ideas have lonely childhoods. You can’t plan for creativity. You can only plan to do the work.”

Hugh Macleod in  “Authenticity is the New Bullshit” (2013)

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“It’s easy to educate for the routine, and hard to educate for the novel.”
Jonathan Rosenberg

Quoted by Jeff Jarvis in  “TEDxNYed: This is Bullshit” originally curated in March 2010, it reminds me of this quote by James Carse:

“To be prepared against surprise is to be trained.
To be prepared for surprise is to be educated.”
James Carse in “Finite and Infinite Games“

I used this in “Planting Trees: Finite and Infinite Entrepreneurship.”

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“The question is not ‘How do we survive until things return to normal?’ Instead it’s ‘How do we build a stronger company in this environment?’ You should be asking, ‘what do we prioritize?’ not ‘What can we cut?'”

Dave Kellogg in “Emerging Stronger From The Downturn Than You Went In.” (Jan-24-2023) [Slide 5]

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“Enthusiasm is the electricity of life. How do you get it? You act enthusiastic until you make it a habit. Enthusiasm is natural; it is being alive, taking the initiative, seeing the importance of what you do, giving it dignity and making what you do important to yourself and to others.”
probably not Gordon Parks

I love the quote, which is widely attributed to Parks, but it does not match his writing style and the earliest cite is to a year after his death in an anonymous posting on Sermon Central. Still, it’s a good insight into the interaction between enthusiasm and initiative.

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“You can analyze the past, but you need to design the future.”
Edward de Bono

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“Most success you see is the result of being able to do the right thing long enough.”
Alex Hillman

One aspect of initiative is that you have to sustain an action you have started long enough to see it bear fruit.

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“The art of teaching is the art of assisting discovery.”
Mark van Doren

True for sales as well

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“I’m lost, but I am making record time.”
Allan Lamport

I think this captures the risk of confusing effort with results. Initiative does not mean taking action without a clear impact in mind. Quoted in “Metro’s Goldwyn Mayor: The Complete Malapropisms of Allan Lamport” (1995) compiled by John Robert Colombo.

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“There was no doubt about it. This bastard was a serious king-hell Crazy. He had that rare weird electricity about him–that extremely wild & heavy presence that you only see in a person who has abandoned all hope of ever behaving “normally.”
Hunter S. Thompson “Fear and Loathing: On the Campaign Trail ’73“

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“If you climb in the saddle be ready for the ride.”
Cowboy proverb

This seems appropriate for a month devotes to quotes about seizing the initiative and taking action. Be prepared for the consequences–good and bad.

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“Effectiveness in uncertain times lies as much in the capability for sensemaking as it does in the capability for decision making. Capabilities for making sense of the unexpected get activated, organized, strengthened, and institutionalized more or less effectively depending upon how people handle failure, simplification, operations, resilience, and expertise. In compact form, the guidance implicit in these five is:

1. Scrutinize small failures.
2. Refine categories that you impose.
3. Watch what you are doing and what emerges.
4. Make do with the resources you have.
5. Listen.

As these five increase, transient organizing becomes more mindful and more responsive to the unexpected at earlier points in its unfolding.

Karl Weick “Making Sense of the Organization (Vol 2) Chapter 1

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“Inability to focus is a disease. And it’s becoming a pandemic spreading throughout the world. Cheap entertainment has made people’s minds soft. They can’t read books anymore. They can’t sit down and work for an hour straight with no breaks. Many can’t even watch TV without also being on their phone. Cure yourself of this, and you’ll have a massive advantage in the future. Give in to it, and it’ll ruin you.”
Himanshu Bakshi in “Inability to Focus is a Disease.“

Focus and follow-through are essential to taking the initiative in a way that leads to effective action.

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“Discontent is at the root of the creative process: the most gifted members of the human species are at their creative best when they cannot have their way.”
Eric Hoffer

The primary reason you don’t get your way is that methods that used to work no longer work: what got you here won’t carry you forward. Initiative that leads to effective action starts with an examination of what you have to change about your approach or your methods. But you also have to make sure you don’t break what’s working, uncovering that is at the heart of “appreciative inquiry” and “amplify positive deviance” methodologies. See these posts for more on understanding what’s still working:

• Early Customer Conversations: Use Appreciative Inquiry, Amplify Positive Deviance
• Managing Change in an Organization: An Incomplete Resource List

h/t to Brad Pierce for pointing out in a comment on LinkedIn: “When you say, ‘what got you here won’t carry you forward,’ could it sometimes be that you’ve misidentified or neglected what really got you here?” I answered: That’s a very good catch and one that I had not considered. Yes, I have seen many individuals and firms not realize some of the key things they were doing that were contributing to their success and inadvertently abandoning them. I have also observed individuals get lucky with an approach that is not really repeatable much less scalable and stick with it because the win it produced was so memorable. This led me to add the “make sure you don’t break what’s working.” 

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“The first abuse of power is not realizing that you have it.”
James Richardson in “Vectors“

On reason we don’t learn the full extent of our powers is limits that we encounter when we are young. It takes a while to figure out what we are truly capable of.

“Learning too soon our limitations, we never learn our full powers.”
Mignon McLaughlin

I like this quote by Mignon McLaughlin; I used in “Success for a Bootstrapper” on the value of perseverance and also in April 2009 and June 2018 collections.

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“We see the same basic pattern recur in conflicts as different as the American Civil War, World War II, and the Russo-Ukrainian War: an initial phase in which one or more armies are commanded by blundering incompetents, a transitional phase in which the incompetents are rapidly killed off or forced to retire, and a terminal phase in which the battlefield is controlled by dead-eyed men of ignoble origin and impressive ability. This is one reason that the board game/computer game model of attritional warfare isn’t accurate. Most armies get stronger and smarter as they take losses, until very close to total defeat.”

John Psmith (@PsmithBooks) [pseudonym] in a “Review: The Hard Thing About Hard Things, by Ben Horowitz“

Don’t Assume Everyone is a Potential Customer

Entrepreneurs often assume everyone is a potential customer; they pursue too many opportunities instead of focusing on those with the strongest need.

One thought experiment many entrepreneurs have found helpful is to imagine they have 100 prospects lined up outside their door.

I ask them to come up three to six questions that have a yes, no, or number answer,
that would enable them to sort these prospects by who is in the most pain and therefore most likely to pay for the product.

For B2B markets, the best questions are factual, not opinion-based, and probe the prospect’s current operating reality:

• Role and responsibilities
• Specific tasks or decisions they face regularly
• Business outcomes that are currently at risk

The key question is not whether a prospect has a problem, but whether the pain, cost, or risk is severe enough that they will pay to solve it.

“Anything that won’t sell, I don’t want to invent. Its sale is proof of utility, and utility is success.”
Thomas Edison

Some Implications

Another way to use the three-to-six-question filter is to consider how much you would pay to have a conversation with someone who meets your criteria. In this case, the answers normally have to be gleaned from publicly available information or from a referral or recommendation. This is, in fact, a real challenge you will face as you scale: investing effort and ultimately money to reach likely prospects.

You must also consider if each question is one they are willing to answer, and answer truthfully, to a stranger. Some important questions will only be answered, or answered truthfully, after a certain amount of trust has been established, and cannot be used early in a conversation or potential business relationship.

Even a product that at first seems like it would be general-purpose may benefit from a focus on a narrow set of needs and the features to support them. For example, a butcher, a scientist, and a cook may all be able to use a scale to measure the weight of a material, but the level of accuracy, the need for independent verification and visibility of result, and the amount of material the scale needs to support may vary widely between the three use cases.

Use the “Three Minute Rule” to Clarify Needs

I like Anthony Tjan’s “Three Minute Rule”

• What happens 3 minutes before they run the report?
• What do they do 3 minutes after reading–and understanding–the report?

Use this to learn the customer’s context–the larger workflow they are likely operating within–identify adjacent opportunities, and understand how the customer may value them.

Related Blog Posts

• Ten Mistakes Early Stage Bootstrappers Often Make
• Early Customer Conversations: Use Appreciative Inquiry, Amplify Positive Deviance
• I have written about the “Three Question Test” in more detail in “The Three Question Test at the Bootstrappers Breakfast.” We often use it to help someone still wrestling with who to serve or where to focus their offering.

Image Credit: “People Waiting in Line” copyright SKMurphy, Inc.