The AI Gold Rush: It Still Takes Teams

Teaser quotes

Sean: We’re in the age of gentleman science. They’ve deployed these models, but they don’t really have a use case. It’s kind of like they’ve deployed new instruments and now we’re learning how to play them.
Jeff: It’s not going to happen overnight. And how is that change going to be proliferated into your organization? How are you going to leverage this new technology? It’s teams that develop products, not just individuals.
Sean: Westinghouse would pick a hard problem to solve that was the first example of a new kind of use case. The cost reduction drives more use and then more use leads to novel structures.
Jeff: Okay, time out here. This is how this thing works. This is how we want, this is how we need to implement it going forward.
Sean: NVIDIA is not talking about laying people off. Right? NVIDIA’s probably embraced this and they’re doing some amazing stuff and they’re not saying yes. And what we hope next year is to lay off 20% of our staff.

Edited Transcript

Norbert: This a conversation with Sean Murphy and Jeff Allison, both of whom have spent decades in Silicon Valley high tech. We break down what AI is, what it’s not. And how organizations can adopt it without losing the craft of engineering. I’m Norbert Korny, let’s jump right in.

Sean: I’m really excited to be here, to be in a conversation with some other guys that actually know what they’re talking about, so that I can, by association, at least appear as if I belong in the room. I’ve worked on introducing various forms of automation, computerization, and digitalization to teams and organizations since 1980.

I think that AI provides new affordances and new capabilities. But there are a number of useful rules of thumb for innovation that will still hold. We are in the process of discovering what these new possibilities can do for us. But my focus has always been, going back to Doug Engelbart, the augmentation of human intellect, not the replacement. The trick is to augment people who are good at their jobs to help them develop and extend their expertise.

This idea of technology change, managing the introduction of new capabilities into organizations, is something that I’ve worked on for small firms and large firms for a while. In 2003, I started SKMurphy, and I now work with early-stage technology companies, helping them navigate the introduction of new products and methods to business customers.

Jeff: My name is Jeff Allison. I’m so glad that you’re pulling this together, Norbert. There are so many conversations happening around AI, and so many that need to happen.

Every day, I open my newsfeed and read that AI is going to do this, AI is going to do that, and wonderful things are going to happen. And I think they will, but the conversations need to get into more detail about the realities of AI and how it can help.

I have spent more than 30 years in the Valley. The Valley is just an innovative hotbed. It’s full of very creative people who have created fantastic technology. There are so many unsung heroes. You walk down the street and may meet someone who was involved in developing IBM’s ATM infrastructure years ago. They were able to do that because they were supported in the challenges they faced. Their organizations invested in tools and methodologies to enable what they developed individually and in teams.

I think AI is going to help push innovation to much higher levels, but it’s not because they’re using AI; it’s because of how they’re using it and how we create infrastructure to enable that. It’s not just about saving cost; that’s not the best way to look at it. It’s about how to help people do their best in a shorter period of time in a way that can be absorbed by the overall team.

Individuals come up with great ideas, but teams make products. Companies invest in teams. I have not mastered the new AI tools, but I do know how to introduce new technologies into organizations and what that entails. And it’s not as easy as, well, we’ll just bring this thing in, and people will start using it, and things will go great. There are all sorts of issues that need to be figured out. And I think being able to share some of my experiences with how difficult that has been over the years will help introduce this new technology into the development process.

“The future is already here–it’s just not evenly distributed.”

Norbert: I know you like the William Gibson quote, “The future is already here–it’s just not evenly distributed.” What do you think that quote gets right about AI in 2026?

Sean: It’s spot on. The new path forward only becomes clear in hindsight.

There’s an article going around that we’re in the age of gentleman science, where individual kind of amateurs are creating breakthroughs using chatbots and AI tools. And I think the major AI companies, they’ve deployed these models, but they don’t really have a use case. It’s kind of like they’ve deployed new instruments and now we’re learning how to play them. And so a lot of people are exploring the landscape, And I don’t think we’ve quite figured out the implications of what it means. And so I think what it gets right is that we’re back in an age of kind of individual exploration, individual tinkering. And we’ll look back and realize that something that happened six months ago or nine months ago, or maybe a year and a half ago, actually was the branch of the tree that we’re following. But right now we’re in this forest and it’s hard to tell.

It’s spot on. There’s an article going around that we’re in the age of gentleman science, where individual amateurs are creating breakthroughs using chatbots and AI tools. And I think the major AI companies have deployed these models, but they don’t really have a use case. It’s like they’ve developed new instruments and now we’re learning how to play them.

A lot of people are exploring this new landscape, but I don’t think we’ve quite figured out the implications or what it means. We’re in an age of individual exploration, individual tinkering. At some point, we’ll look back and realize that something that happened six months ago or nine months ago, or maybe a year and a half ago, actually was the branch of the tree that we’re now following. But right now we’re in this forest, and it’s hard to tell.

What’s Driving the AI Gold Rush?

Jeff: People who are doing new things or just starting out in their endeavor to create a new product or deliver a useful innovation, pick up new methods naturally. But 90% of us continue to stick with “the old way” because it’s proven, predictable, and we understand it. We need to talk about how those folks can get on board with this new product development paradigm. And I don’t think there’s enough being talked about that.

Also, the rate of change in AI tooling is still so high that new methods are evolving faster than they can be documented and debugged. So this transition from explorers and early adopters won’t happen overnight.  A lot of what’s being described is experiments, pilot projects, or proof-of-concept. There is much less about how a successful pilot is proliferating as a proven methodology in an organization. Much less about how to use AI technology to improve your methods and provide better products for customers. I think that’s where we’re at.

Norbert: Is the Gold Rush really about efficiency, innovation, or a competitive arms race?

Sean: I think our sense of a Gold Rush is driven by competition and a fear of obsolescence. I think there’s been a lot of messaging that “Everything you know is wrong” or “Everything is going to change.”

Lately, I’ve been fascinated by the introduction of electricity into both industry and daily life. In some ways, it was a struggle between Edison and Westinghouse. Edison was a tinkerer who would try many things in parallel. One reason I think Westinghouse ultimately set the direction toward an alternating current architecture was that he chose hard problems and solved them for industrial use. This was formed by his early sales to the railroad of an air braking system that had to work reliably.

When he wanted to test his AC dynamo paired with an AC motor, he went to a remote mine in Colorado and ran it all winter, debugging problems as they came up. His approach was to solve a real problem with a satisfactory level of performance. That’s missing from much of today’s embrace of AI. Many announce tiny incremental breakthroughs, typically based on contrived benchmarks, not real-world outcomes. Everyone wants to be five minutes ahead, driven by a massive fear of missing out.

How do we Define and Adopt a New Paradigm?

Jeff: New tools, whatever they may be, can help us come up with very creative ways of developing products. We have been doing this for decades in the Valley; it’s great. The more you can automate and enable individuals to be more productive, the more wonderful it is.

The challenge is that teams develop products, not just individuals. Whenever you’re bringing in something new, you have to consider how it will work within the team.
It’s great to become more efficient in one area, but that may account for only 10% of the overall product development cycle. It’s an improvement, but if it slows down 90% of it, then it’s not really a win. It takes a team to develop today’s complex products that deliver value for customers.

Part of what I worry about, and what I want more discussion on: how do we leverage this technology so that the team gets more efficient, the team gets more competitive and the team is able to innovate beyond current constraints and not just accept them.

When we would develop algorithms or integrated circuits to perform critical tasks, we would then review results and say, “Okay, this is good, but we need to make it better.” So, if this technology allows us to make things better, I think it’s a great thing. But how do we do that at a team level, not just at an individual level?

I think the true Gold Rush is about the teams that really embrace this technology and are able to use it at the team level to collaborate and to develop product, they’re going to win. The guys that just take it and say, okay, this makes me more efficient, and they’re in a little box doing it, They’re not going to win here. They’re just going to get more efficient at what they do and it’s not going to affect the overall product.

Norbert: Yeah, and it seems like the adoption is driven by the individuals, unlike previously for enterprises, even better yet, the army before then. And it acts like a force multiplier. To be best, you need to be good at the beginning.

Jeff: Yes, you do. And we observed this when we were putting automation into the hardware development process for developed systems on chips. We got really good at doing specifications, but then we realized that we were really good at creating millions of gates, but now we have to get really good at testing them. So then we had to bring more people on and develop methodologies around that.

So I think this is what’s going to happen with this AI. AI is going to allow us to move very quickly in certain areas. But this will uncover blind spots that we will need to address. We’ve moved really quickly here and were really innovative there. But what does that mean to the overall development process? What else do we need to do to get this thing to actually realize everything that is promised?

Sean: Couple of quick thoughts. In the beginning, most technologies get adopted because of a cost reduction of some sort, or they come in as toys or kind of an artisanal tool.

I think a lot of AI has been sold to the CFO, by promising to cut costs by allowing you to lay people off. The most recent example is Block just laid off 40% of their people. I think that’s a smokescreen for years of bad decisions on their part. When you actually look at who’s getting laid off, it’s a lot of people with expertise. So I don’t buy that story.

The next step after cost reduction is more use, and then or use drives the creation of novel structures. Right now we see time savings at the individual level and thus more use.

Norbert: I agree.The story Jack Dorsey is running with is that they built smaller teams to make them more effective. Is this is the best thing we can do right now, because an individual wouldn’t make that big a difference in an enterprise? Small teams, maybe yes, but if you got 10,000 people, is it enough?

Individual Insight Can Enable Breakthroughs

Sean: I think an individual can have a significant insight that leads to an architectural or a novel combination breakthrough. The breakthrough always comes first in one person’s head, or maybe two people working in an intense collaboration. But then, to actually ship a product or deliver an industrial-strength or highly reliable processor, normally requires a cross-functional team of four to twelve to look at it from all the right angles.

If we go back to the Gold Rush, you can trace an evolution of individuals panning for gold—which is an artisanal model—to industrial-scale mining and extraction methods. Today, I think we’re still in the panning-for-gold phase. I don’t think we’ve even figured out how to facilitate a team of six to twelve.

Jeff: Well, this technology was initially developed for consumer or individual use, not industrial. I don’t see much of enterprise or industrial focus on what teams of engineers or scientists need.

Sean: It’s hard to track everything that’s going, but the Perplexity guys seem to be passing a test of giving answers with substantiation with links. As opposed to providing an answer that you have to treat as if it was delivered by an oracle. So Perplexity is at least a half-step towards deploying something that could be part of a reliable business process.

Jeff: That’s a big step from a consumer-based product to company-base. We’ve all done that. If anyone’s trying to bring technology into their organization and it works well for an individual, there is still a long process to get it adopted by one more more teams.

It requires a higher level of reliability, support, and well-defined infrastructure to enable all of that: standards, databases, defined development processes, revision control, methodologies, etc. These support infrastructure elements all take time to figure out and develop, regardless of whether they are for an outside vendor or an internally developed tool. There are many steps from a proof of concept to getting something widely deployed, making many people more productive.

Different parts of the organization may have different architectures and different cultures. An established business unit can have a big centralized architecture, but newer smaller business units may be more decentralized with small teams, some scattered all over the world. And then Different organizations have different architectures, right? They work differently and may benefit from different support and infrastructure models.

I think there’s a lot to be discussed here. And I’m happy, Norbert, that you’re doing this and trying to shine some light on some of these issues so that we can have these kinds of discussions and kind of work through it. Because I think some of these things are just not getting talked about enough.

Norbert: Is the software something where we’ve seen the biggest impact? Because from my point of view, it seems to me the software seems to be reinventing itself. We introduced the concept of shared libraries to reuse logic. AI recently introduced skills. Now you need a skill repository, and there are  “malicious skills” you need to scan for security threats. And this is a whole software development lifecycle you need to revisit. And we’ve been through this already.

Adoption Takes Months to Years

Jeff: We could draw lessons from earlier disruptive technologies for software development. How long did they take to go from individual adoption mainstream use. It wasn’t days or weeks. It was months and years.

I don’t know how long AI will take to become a reliable mainstream development environment for even small teams. But I think to Sean’s point where you’ve got people that are making decisions on this technology, they really don’t understand the technology, but they are just looking at cost savings. I can pay for an agent that can develop a product that costs X vs. 5-10X for a human being.

That’s a metric, but to me, it’s very short-sighted. It’s no way to run the business.
I think we are at a point where the business guys are running the show, but I’m hoping that the engineering and development organizations step in here and to say, “Hey, okay, time out here. This is how this thing works. This is how we need to implement it going forward, and this is what’s going to make sense for us rather than just counting the dollars.”

Sean: The business guys remind me of the early rapid early evolution of HTML. Netscape on a whim decides to add the blink tag or to implement cookies in a certain way. I think we’re going to look back on MCP and on some of these other things constructs and conclude that they should have never left the lab.

MCP is an interesting first effort, but it’s not a reliable, scalable protocol, and it throws away 90% of what we know about security and protocol design. I’m okay with the individual experimentation and allowing individuals to scout and report on what they were able to do. But because of this consumer focus and this fixation on replacing professionals, knowledge workers, or white collar workers, they really haven’t engaged the people who define processes, the methodologists who look at this in a structured way. And it doesn’t seem like those people are on staff at the major AI companies; they seem to hire people with different expertise.

Another interesting thing for me is that Jensen Huang at NVIDIA is not talking about laying people off. NVIDIA has deeply embraced AI and is doing some amazing stuff, but they are not saying, “Next year we hope to lay off 20% of our staff.” They realize they are in a Red Queen Race, running faster and faster just to hold on to their position in the market. They understand that the bar for acceptable performance will be continually raised.

Jeff: I think NVIDIA is airing this technology out internally and they’re probably developing methods, processes, maybe developing tool chains around this to enable them to develop their product. I think those tool chains and those methods will become more available to people over time.

This reminds me of design automation in the early days of hardware. The first Design Automation Conference was held in 1964, but it wasn’t until 1982 that they formally recognized independent vendors with an associated trade show. EDA was incubated by larger organizations for almost 20 years. IBM and other big companies developed technology internally, so they understood it.

And then, those technologies became more commercially available, creating a whole market. So I think that has to happen here too. And I think NVIDIA is probably taking the lead on it. And there may be more companies out there that are doing stuff behind the curtain, right? That, you know, they’re not talking about right now, but they’re maybe saying, okay, we’re going to develop this next generation of product or this product, you know, we’re going to use an AI model on this product and we’re going to air it out. And once we figured out how it all works, then we’re going to make it more mainstream in our organization. Maybe that’s going on. That’d be great. It’d be great to talk to people that are doing that. The AI automation gurus of the future, right? Where are they and what are they doing?

Sean: It seems like there’s two tracks running here. So, I agree it’s very possible that there are organizations that are getting value and deploying this to make internal processes or design processes more efficient, and they’re getting big advantages that they’re not disclosing. Because most of the stories that are being told seem to be told from the perspective of, “Don’t worry, we’re not falling behind. Here’s some examples of how we’re using AI.” But when you dig into most of those stories, it puffery.

We are still in the early stages. The Design Automation Conference was a community of practice that allowed competitors to compare notes and learn. People would not give away everything they were working on, but there was still value in solving common problems.

I remember watching a YCombinator LightCone podcast where they essentially concluded, “We think we can solve electronic design kick the currently EDA industry in the ass in a year from now.” Not so much. The semiconductor industry and the EDA guys have been running a Red Queen Race ever since they passed Moore’s Law. They are incorporating this technology. We haven’t so far seen anyone come out who’s made current practices obsolete. I think the current vendors are obsoleting their current tools, but that’s different from some outsider using this to destabilize the industry. All the majors now have a VP of AI. Everyone on the semiconductor side, the systems houses, and the design tools are all looking at this. I think it will be like electricity: It’s going to raise the level for everyone.

Jeff: VP of AI? Good.

Sean: You asked where we’re seeing the biggest impact. I think right now it seems like it’s amongst experienced developers working on greenfield designs. That to me is kind of a splinter or a scouting activity. There’s some changeover point where you’ve got a working proof of concept and now you actually have to harden it and deploy it and support it. And that’s not going to be normally just one person. I don’t know that we’ve got the models for that yet.

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

Sean: That’s absolutely going on.

Related Blog Posts

• Mark Bennett on Using Claude Code for Application Development (Q&A)
• AI in Action: Practical Automation by Alex Panait (webinar)
• Matt Trifiro on Lessons Learned using AI for Marketing (webinar)
• Andrew Shindyapin: AI’s Impact on Software Development (Q&A)
• Alex Panait on Current Trends and Possible Futures for AI (Q&A)
• Time to Market S01 E03 – How Will AI like ChatGPT Impact B2B Founders? 

References

Gentleman Science

• Sean Goedecke “We are in the “gentleman scientist” era of AI research” (Oct-18-2025) “Many scientific discoveries used to be made by amateurs.”
• Garry Tan “AI is in its Gentleman Science Era”

YCombinator LightCone Podcast: Why The Next AI Breakthroughs Will Be In Reasoning Not Scaling

“One thing that really stood out about o1 in particular—if you read one of the papers talking about it, so capabilities and potential for the future—it talks about how it does really well in chip design.[…]  At some point the AI will get good enough to just like design chips better than like humans can, and then it will just like eliminate one of its bottlenecks for like getting greater intelligence. And so it feels like that’s already kind of like we’re on the pathway to that in a way that we just weren’t before.

SKMurphy Take: AI may have a strong impact on electronic system design but it’s unlikely to enable a startup to obsolete the established players. See for example

• “How to Plan Agentic AI Deployment for Chip Design.”
• AI Growing Impact on Chip Design

Image Credit: 3 views of Antelope Canyon (c) Kevin Murphy, used with Permission

Hope for the best, prepare for the worst

Some thoughts on the value of preparing for a range of outcomes from your current course of action.  I think the most famous version of this advice comes from a song by Mel Brooks.

Hope for the best, expect the worst
The world’s a stage, we’re unrehearsed

Mel Brooks lyrics to The Twelve Chairs, “Hope for the Best, Expect the Worst” (1970)

I think the best version comes from Zig Ziglar, urging a flexible response to a range of outcomes.

“Expect the best.
Prepare for the worst.
Capitalize on what comes.”
Zig Ziglar

The oldest versions I could find come from Benjamin Disraeli and Mary M. Bell, but this may have been a proverb in circulation in the 19th century.

“Hope for the best,
prepare for the worst,
and take what God sends.”
Mary M. Bell in “Seven to Seventeen; or, Veronica Gordon” (1873)

“I am prepared for the worst, but hope for the best.”
Benjamin Disraeli in “The Wondrous Tale of Alroy” (1845)

Tony Hsieh offers a more tactical prescription in his book “Delivering Happiness” that also applies to cash management for bootstrappers:

• Always be prepared for the worst possible scenario.
• Play only with what you can afford to lose.
• Make sure your bankroll is large enough for the game you’re playing and the risks you’re taking.
• Remember it’s a long term game. You will win or lose individual sessions, but it’s what happens in the long term that matters.

Tony Hsieh in his book “Delivering Happiness” in the chapter on “Poker”

I used this in Texas Hold’Em as a Model for Technology Startups.

“What you prepare for, what you hope for, and what you expect are often three different things.”
John D. Cook (@JohnDCook)

Make affordable loss bets

“Look at the Underside First: Legions of people are paid large sums to promote the positive aspects of commercially available products. Very few people earn their daily bread by pointing out malfunctions, bugs, screw-ups, design failures, side-effects and the whole sad galaxy of trade-offs and failings that are inherent in any technological artifact. To counteract this gross social imbalance, a wise designer and a wise critic will make it a matter of principle to look at the underside first.”  Bruce Sterling in Viridian Design Principles (1998)

There is much more at the Viridian Design Site. In “Constructive Pessimism,” I observed that you have to be willing to acknowledge the possibility of problems and look for them to be able to prevent or at least manage them. Many entrepreneurs who are naturally optimistic make a serious mistake in discouraging pessimistic thinking instead of putting it to good use. The clever utilization of constructive pessimism is one of the keys to success.

Related Blog Posts

• Constructive Pessimism
• SKMurphy Perspective Counterbalances Excess Pessimism or Optimism
• Managing Recurring Problems In Your Startup
• Startup Uncertainty At The Very Beginning
• Making Business Decisions in Uncertain Times
• Q: How Do I Plan for Pivots or Even Shutting Down My Startup?

Quotes for Entrepreneurs Curated in March 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 data, stories, and evidence.

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“Research means that you don’t know, but are willing to find out.”
Charles Kettering

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“ABCD – Always Be Collecting Data”

I originally used “always be collecting data” on the SKMurphy Blog on Nov-29-2010 in “Keeping Your Customer’s Trust” Under Law 6 in Weinberg’s Laws of Trust. “Always trust your client–and cut the cards.” My comment was “Always be collecting data. Always be collecting multiple perspectives.” I then used it as the title for a blog post.

I recall seeing this phrase in a presentation as “ABCD – Always Be Collecting Data,” in the late 1980s or early 1990s. It may been a riff by the presenter on the Glengarry Glen Ross movie speech by Alec Baldwin where he writes “ABC – Always Be Closing” on the chalkboard during a briefing for the sales team.

I traced the phrase to an answer to a 1925 letter to the editor of Printers’ Ink; but I suspect the phrase is older.

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“A wise man proportions his belief to the evidence.”
David Hume

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“The first principle is that you must not fool yourself – and you are the easiest person to fool.”
Richard P. Feynman

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“No research without action, no action without research.”
Kurt Lewin

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“Without data, you’re just another person with an opinion.”
W. Edwards Deming

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People Manage People, Tools Manage Data

This was a principle for systems design suggested in a talk I heard in the late 80s or early 90s. I can no longer remember the speaker’s name but I remember that he was in the disk drive business. Google has proven unavailing in sourcing it so it was probably an original insight with this engineer that hasn’t gained wider currency. Used as the title for a blog post.

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“Do something today that reflects who you are, what you are capable of, what you care about. Whether it is at work, at home, for pay or for free, do something that shows you. We need to see evidence of our abilities; we need to see evidence of our relevance. Give yourself plenty of evidence of what you can do, and you will not doubt your abilities to do anything.”

David Niven, PhD in “The Simple Secrets for Becoming Healthy, Wealthy, and Wise“

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“Founders have infinite time horizons and concentrated ownership; professional managers have quarterly time horizons and zero ownership.”

“A founder treats the company as a tool for solving a problem; a CEO treats the company as the thing to be preserved.”
Mark Atwood (@_Mark_Atwood)

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“No data yet. It is a capital mistake to theorize before you have all the evidence. It biases the judgment.”
“There is nothing like first hand evidence, as a matter of fact, my mind is entirely made up upon the case, but still we may as well learn all that is to be learned.”
Two quotes by Sherlock Holmes in “A Study in Scarlet”  by Arthur Conan Doyle

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“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”
Jim Barksdale

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“Do not seek for information of which you cannot make use.”
Anna C. Brackett in “The Technique of Rest” (1892).

Especially true for entrepreneurs when interviewing prospects. There is a corollary: calculate the expected value of perfect information; don’t spend more on gathering data than the impact it will have on your plans. A reasonable probability is normally the best you can achieve: you can only be certain of missed opportunities, not the ones that are available to you. More context:

“If the train stops, don’t ask a hundred questions, which don’t concern you, as to the cause of the delay. Do not seek for information of which you can make no use. When the steamer goes slowly because of fog, do not attack the captain every time he appears on deck with your inquiries as to whether he thinks he will run into an iceberg or another vessel, or whether there is always fog in that part of the ocean, and a hundred others, so various as to leave no doubt in the mind of anyone who listens to them of the great power of invention of their propounder.”
Anna C. Brackett in “The Technique of Rest” (1892).

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“When action grows unprofitable, gather information; when information grows unprofitable, sleep.”

Ursula K. Le Guin  in “The Left Hand of Darkness” (1969).

Originally curated in March 2012

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“There is much evidence that the proper study of programming is done at the level of the programming social unit. Not that the individual level is unimportant, but we might start by asking why, if the average programmer spends two-thirds of his time working with other people rather than working alone (yes, it’s true!), that 99 percent of the studies have been on individual programmers.

One answer, of course, is that if studying programmers is expensive, studying groups of programmers is extravagantly so. Moreover, not just any groups of programmers will do–not, for example, a collection of trainees put into a “team.” Putting a bunch of people to work on the same problem doesn’t make them a team. […] Even studying teams as they are constituted today may not be sufficient, for these are teams that have grown up in an environment pervaded by the myth that programming is the last bastion of individuality.”

Gerald Weinberg in “The Psychology of Computer Programming” (Silver anniversary edition) | 1988

This also applies to gaining an understanding of other kinds of knowledge work.

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“The warning signs are flashing bright red that the venture market has never been more consensus-driven. We believe that the consequences of continued concentration will be catastrophic for venture capital and the broader innovation economy. ”
Omar El-Ayat and Nic Poulos in  “We Have Met the Enemy an He is Us“

This impacts bootstrappers indirectly as a widespread consensus builds on the “right way” to build a startups and filters into our niches in the entrepreneurial ecosystem.

“Venture capital has long celebrated itself as the business of contrarianism. The best investors prided themselves on spotting what others missed: the non-consensus founder, market, or product. The venture model was designed to harness outlier outcomes — but it relied on individual conviction, not herd behavior, to find them.”
Omar El-Ayat and Nic Poulos in “We Have Met the Enemy an He is Us

I think this still applies for bootstrappers: leverage your unique experience, perspective, and skills to find a niche you can serve exceptionally well.

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“When I look at my grandchildren or I hold them, I can feel that it’s only my individual strength that is subsiding. The strength in the family, in the species, and in the whole beating heart of the universe hasn’t subsided at all.”
David Milch reflecting on his Alzheimer’s and his spirituality in his memoir “Life’s Work“

I am a huge fan of David Milch’s writing in “Hill Street Blues”, “NYPD Blue”, “Deadwood”, “John from Cincinnati”, and “Luck.” I read his “True Blue: The Real Stories Behind NYPD Blue” he co-wrote with Bill Clark and enjoyed it immensely. I thought this might be similar to Somerset Maugham’s “The Summing Up” or Sid Meier’s “Memoir” where creative people offered assessments of what they learned and some insights into their process. This is a very bleak book that recounts some terrible childhood experiences that shapes his life as well as his gambling addiction, drug addiction, and alcoholism. Part of the problem is that by the time he started on the memoir his Alzheimer’s had progressed to a point that he is relying on transcripts of writing sessions and older notes but he has very little memory of events except those that are incredibly painful emotionally.

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“There’s a fundamental connection between seeming and being. We all become what we pretend to be. Everyone tells a story about themselves inside their own head. Always. All the time. That story makes you what you are. We build ourselves out of that story.”
Patrick Rothfuss in The Name of the Wind (2007)

This works at a founding team-level as well. This reminds me of two older quotes:

“We are what we pretend to be, so we must be careful about what we pretend to be.”
Kurt Vonnegut in “Mother Night” (1962)

I think our beliefs, dreams, and visions allow us to act our way into expertise. This is not “fake it till you make it,” it’s “acting as if” to allow you to become what you aspire to be.

“No man for any considerable period can wear one face to himself, and another to the multitude, without finally getting bewildered as to which may be the true.”
Nathaniel Hawthorne, The Scarlet Letter, 1850.

+ + +

“Everything you read in the newspapers is absolutely true except for the rare story of which you happen to have first-hand knowledge.”
Erwin Knoll

Known as “Knoll’s Law of Media Accuracy” in the 80s. Two decades later Michael Crichton labelled it “Murray Gell-Mann Amnesia effect.”

“Media carries with it a credibility that is totally undeserved. You have all experienced this, in what I call the Murray Gell-Mann Amnesia effect. (I call it by this name because I once discussed it with Murray Gell-Mann, and by dropping a famous name I imply greater importance to myself, and to the effect, than it would otherwise have.)

Briefly stated, the Gell-Mann Amnesia effect works as follows. You open the newspaper to an article on some subject you know well. In Murray’s case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward-reversing cause and effect. I call these the “wet streets cause rain” stories. Paper’s full of them.

In any case, you read with exasperation or amusement the multiple errors in a story-and then turn the page to national or international affairs, and read with renewed interest as if the rest of the newspaper was somehow more accurate about far-off Palestine than it was about the story you just read. You turn the page, and forget what you know.

That is the Gell-Mann Amnesia effect. I’d point out it does not operate in other arenas of life. In ordinary life, if somebody consistently exaggerates or lies to you, you soon discount everything they say. In court, there is the legal doctrine of falsus in uno, falsus in omnibus, which means untruthful in one part, untruthful in all.

But when it comes to the media, we believe against evidence that it is probably worth our time to read other parts of the paper. When, in fact, it almost certainly isn’t. The only possible explanation for our behavior is amnesia.”

Michael Crichton in “Why Speculate” a talk at International Leadership Forum, La Jolla (26 April 2002)

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“Honor and shame from no condition rise;
Act well your part, there all the honor lies.
Fortune in men has some small difference made,
[…]
Worth makes the man, and want of it, the fellow.”

Alexander Pope in Epistle V in his “An Essay on Man” (1891)

English has drifted a little since Pope wrote this almost 140 years ago so I will offer my interpretation of these few lines: what matters is how well you play the hand you’ve been dealt. Circumstances, wealth, and social status make little difference, it’s how you manage the situations you find yourself in that allow you to distinguish yourself. This is the mindset that helps entrepreneurs to thrive.

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“A story has no beginning or end: arbitrarily one chooses that moment of experience from which to look back or from which to look ahead.”
Graham Greene “The End of the Affair”

The story of your entrepreneurial journey begins much earlier than you may realize; if you inquire and research, you can find antecedents in the lives of your grandparents and, likely, your great-grandparents. There are lessons in the lives of your ancestors that apply to and can inform your journey. For better and for worse, strengths, tendencies, flows, and choices, good and bad, echo in your character and capabilities. And the story doesn’t end with the failure of any one business you start or any one success.

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“Sit down before fact as a little child, be prepared to give up every preconceived notion, follow humbly wherever and to whatever abysses nature leads, or you shall learn nothing. I have only begun to learn content and peace of mind since I have resolved at all risks to do this.”
Thomas Huxley

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“In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”
Herbert Simon in “Designing Organizations for an Information-Rich World”  collected in “Computers, Communications and the Public Interest”

On of the risks that imaginative entrepreneurs face is apophenia: seeing patterns and connections that are not there.

+ + +

“Science, my boy, is made up of mistakes, but they are mistakes which it is useful to make, because they lead little by little to the truth.”
Jules Verne in “Journey to the Center of the Earth”

This reminds me of

The Road to Wisdom
The road to wisdom? Well, it’s plain
And simple to express:
Err
and err
and err again,
but less
and less
and less.”

Piet Hein

+ + +

“Pure data. You don’t believe data–you test data.” He grimaced. “If I could put my finger on the moment we genuinely fucked ourselves, it was the moment we decided that data was something you could use words like believe or disbelieve around.”
Paolo Bacigalupi in “The Water Knife“

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image source: 123rf.com/profile_alrika 186083413

“We built something people really liked, but they didn’t need it.” – Suchita Kaundin

Discovery and Customer Validation Lessons

Suchita Kaundin concluded that better discovery could have revealed the problem earlier. Important questions she would now ask include:

• What happens if this problem is not solved?
• How do customers solve it today?
• Who owns the budget?
• Is there urgency to solve it this quarter?

Testing enthusiasm alone is not enough; founders must test urgency and willingness to pay.

In the full video, we unpack the realities of shutting down an early-stage AI company: unclear documentation, unpaid pilots, weak product-market fit, and painful pivots. Then we explore how those experiences strengthen the next startup: sharper customer discovery, disciplined validation, better legal and equity foundations, and clearer go-to-market focus. Suchita Kaundin offers practical insights for founders navigating transitions, turning hard-won lessons into strategic advantages for her next AI venture.

Key Takeaways

• We built something people really liked, but they didn’t need it.
• During the discovery phase it is important not just to test enthusiasm but also urgency.
• If they do not pay for a pilot, they probably will not pay later
• Building the product is actually one of the easiest parts… getting people to buy it is the hard part

About Suchita Kaundin

Suchita Kaundin is a Product Manager at Guestrix with deep expertise in embedded systems, hardware–software integration, and platform technologies. She has worked across startups and multinational companies, building and scaling products in IoT, data storage, firmware, and software platforms. With a strong foundation in engineering, she transitioned into product management and excels at the intersection of technology, business, and customer needs. Suchita specializes in embedded software, ensuring seamless hardware–software interactions. She enjoys solving complex technical challenges, collaborating closely with firmware and hardware teams, and defining scalable, high-performance platforms that maximize efficiency and long-term product value.

Related Content on Customer Validation

• Where Do Lean Startup Methods Help Most?
• Customer Development: Scouting A New Market
• Steve Blank on Customer Development Process for Startups

Scaling Requires Building a Repeatable Process

Scale = turning “what I do” into “what we do.” The hard transition is extracting your tacit know-how into a repeatable method other people can deliver.

Lead with Results

Sell outcomes with proof, not self-description. Parents (and most buyers) care about results: testimonials, concrete examples, timelines, and a clear process—not “I have X degrees / Y years.”

Biggest Mistake

Ask for testimonials early—within two to three months—once you’ve delivered results, instead of waiting years. Timely requests dramatically increase volume and growth. If clients are satisfied, capture proof quickly; strong testimonials build trust, credibility, and accelerate business traction.

Growth Requires Investment and Experimentation

You need a “play budget” for marketing tests (PPC, ads, website tweaks). Most experiments won’t work; a few winners can drive disproportionate growth.

Too Much on Your Plate, What to Delegate?

When tasks become inefficient and draining, like invoicing, recognize it quickly and delegate, freeing time to focus on higher-value work and growth.

Full video

https://vimeo.com/manage/videos/1170101132/player

About Amadeus Ciok

Amadeus Ciok is founder of Learn Vibrant Math Tutoring, a high-performance tutoring company focused on delivering measurable academic results for students in competitive environments. Starting as a solo tutor in the Bay Area, he has grown the business 7× into a multi-tutor team serving dozens of students locally and nationwide.

With over 15 years of experience in competitive mathematics and tutoring, Amadeus developed a structured, conversational teaching methodology that emphasizes deep understanding, confidence-building, and rapid grade improvement. His team specializes in helping students achieve top academic outcomes, including admission to leading universities such as Stanford, UC Berkeley, and Dartmouth.

Known for his disciplined approach to hiring, training, and quality control, Amadeus has built a scalable services business rooted in clear processes, continuous coaching, and strong client relationships. His work reflects a results-driven philosophy: focus on what clients value, deliver consistently, and use testimonials and outcomes to fuel growth.

Quotes for Entrepreneurs Curated in February 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 iteration and successive refinements.

+ + +

“In my opinion dividing work into small, but different, batches (eating elephant one bite at a time) should not be called iteration. My reference case for iteration is Newton’s method: improving your solution by repeating an operation on improved approximation of answer.”
Donald Reinertsen (@DReinertsen)

originally Quotes For Entrepreneurs–April 2013

+ + +

“In Ireland the inevitable never happens and the unexpected constantly occurs.”

John Pentland Mahaffy

+ + +

“Everything we know is only some kind of approximation, because we know that we do not know all the laws yet. Therefore, things must be learned only to be unlearned again or, more likely, to be corrected.”
Richard P. Feynman

+ + +

“All models are approximations. Essentially, all models are wrong, but some are useful. However, the approximate nature of the model must always be borne in mind.”
George E. P. Box In “Response Surfaces, Mixtures, and Ridge Analyses”

+ + +

“Alas! we know that ideals can never be completely embodied in practice. Ideals must ever lie a great way off–and we will thankfully content ourselves with any not intolerable approximation”.
Thomas Carlyle in “On Heroes, Hero-worship and the Heroic in History” (1840) [Gutenberg]

+ + +

“It’s not easy taking my problems one at a time when they refuse to get in line.”
Ashleigh Brilliant

Some problems come with strict time limits and are not amenable to iteration. Your judgement can improve over a series of related problems, provided you survive the challenge each problem presents.

+ + +

“Historians approach their subject from the moving platform of their own times, with the result that the past changes shape continually Anyone who lives to reread his own work long afterwards must therefore expect to recognize signs and hallmarks of the inevitable displacement that time brings to historical understanding.”
William H. McNeill in “‘The Rise of the West’ After Twenty-Five Years.“

This is from his retrospective preface to the 1991 edition of “Rise of the West,” originally published in 1963. McNeill offers a perspective on iteration: as we learn more we revisit older narratives and the assumptions that underlie them to refine our understanding.

In addition to “Rise of the West,” I have read three other books by McNeill: “Plagues and Peoples,” “Venice: Hinge of Europe,” and “The Pursuit of Power.” I appreciate his systems perspective on the forces at work in historical contexts.

+ + +

“The scientist explores the world of phenomena by successive approximations. He knows that his data are not precise and that his theories must always be tested. It is quite natural that he tends to develop healthy skepticism, suspended judgment, and disciplined imagination.
Edwin Powell Hubble in “Commencement Address, California Institute of Technology (10 Jun 1938), ‘Experiment and Experience.’

+ + +

“In doing we learn.”
George Herbert  in  Jacula Prudentum (1651).

+ + +

“The scientist is a practical man with a practical aim: the next approximation. The scientist builds slowly, if he is dissatisfied with any of his work, even near the foundation, he can replace it without damage to the remainder.

The theory that there is an ultimate truth, although very generally held by mankind, does not seem useful to science except in the sense of a horizon toward which we may proceed, rather than a point which may be reached.”
Gilbert Newton Lewis in “The Anatomy of Science” (1926), 6-7.

condensed from:

“I have no patience with attempts to identify science with measurement, which is but one of its tools, or with any definition of the scientist which would exclude a Darwin, a Pasteur or a Kekulé.

The scientist is a practical man and his are practical aims. He does not seek the ultimate but the proximate. He does not speak of the last analysis but rather of the next approximation. His are not those beautiful structures so delicately designed that a single flaw may cause the collapse of the whole. The scientist builds slowly and with a gross but solid kind of masonry. If dissatisfied with any of his work, even if it be near the very foundations, he can replace that part without damage to the remainder. On the whole, he is satisfied with his work, for while science may never be wholly right it certainly is never wholly wrong; and it seems to be improving from decade to decade.

The theory that there is an ultimate truth, although very generally held by mankind, does not seem useful to science except in the sense of a horizon toward which we may proceed, rather than a point which may be reached.”

Gilbert Newton Lewis in “The Anatomy of Science” (1926), 6-7.

+ + +

“Science does not aim at establishing immutable truths and eternal dogmas; its aim is to approach the truth by successive approximations, without claiming that at any stage final and complete accuracy has been achieved.”

Bertrand Russell in “Is The Universe Finite” collected in “The ABC of Relativity”  (1925) [Archive]

+ + +

“The closest you can get to perfection is constant improvement.”
Brendan Brazier

+ + +

“For the theory-practice iteration to work, the scientist must be mentally ambidextrous; fascinated equally on the one hand by possible meanings, theories, and tentative models to be induced from data and the practical reality of the real world, and on the other with the factual implications deducible from tentative theories, models and hypotheses.

George E. P. Box in “Science and Statistics” (1976)

+ + +

“The hallmarks of Scottish medicine were close clinical observation, hands-on diagnosis, and thinking of objects such as the human body as a system–not so different from the practical approach of engineers such as James Watt. In fact, science and medicine were probably more closely linked in Scotland than any other European country. Together with mathematics, they formed the triangular base of the Scottish practical mind.”

Arthur Herman in “How the Scots Invented the Modern World“

Careful observation, diagnosis, and a systems perspective are key elements of the entrepreneur’s mindset.

+ + +

“People misunderstand what built Silicon Valley. It wasn’t just intelligence. It was the stamina to endure embarrassment, the courage to diverge from the ‘ideal path,’ and the sheer will to keep going.”
Bassel Ojjeh in “Is YC for Cowards?”

 

+ + +

“Sometimes you have to push past the failures and doubt and no’s.
Sometimes those are the signals that you’re on the wrong path and you have to pivot or stop. How do you know which is which?”
Jason Cohan Dec-20-2024 tweet

It’s a hard problem. While there are search aspects to early market it’s as much a design challenge as a discovery challenge. It’s not a puzzle with pieces that fit only one way, its a collection of LEGO you can combine in many ways–and you don’t need to use all of them. Two related articles by Jason Cohan worth reading: “Perseverance” and “Predict the Future.”

+ + +

“There are two possible outcomes: if the result confirms the hypothesis, then you’ve made a measurement. If the result is contrary to the hypothesis, then you’ve made a discovery.”
Enrico Fermi

+ + +

“Iteration, not ideation, is the most important part of early stage entrepreneurship. You have to have a lot of ideas, a lot of bad ideas, if you want to end up with a good one. You have to make a lot of prototypes and put share them with prospects who can give you feedback. And revise or abandon them if necessary.”

Marc Randolph in “Your Idea Sucks, But That Doesn’t Matter“

+ + +

“You can only iterate on something after it’s been released. Prior to release, you’re just making the thing. Even if you change it, you’re just making it. Iterating is when you change/improve after it’s out. So if you want to iterate, SHIP.”
Jason Fried

+ + +

“We are all apprentices in a craft where no one ever becomes a master.”
Ernest Hemingway in New York Journal-American (11 July 1961)

This reminds me of an aphorism of Hippocrates:

“Life is short,
and craft long,
opportunity fleeting,
experimentation perilous,
and judgment difficult.”
Hippocrates

+ + +

“The fastest way to iterate is to learn from others.

• Read good books
• Talk to people who have done it before
• Soak up the lessons of the past

Learn from the experiments history has already run and you can start the race halfway finished.”

James Clear

+ + +

“Iteration requires two distinct skills that work in collaboration with one another. First, the curating skill, which is able to realize and harness seeds of potential in ideas that are incomplete. This skill allows the feedback loop to push the work in completely new directions. The second is the proofing skill, which can earmark weak points that need improving. This is polish and refinement.

If you’re good at either one of these skills, you’re going to have people showing up at your door asking you to look at their work. Want to know which you’re good at? When do people show you what they’re working on? If it’s towards the beginning, you’re stronger at curation. If it’s towards the end of their process, you’re probably more of a proofer. But ideally, you should try to have both skills. There’s a lot of waste in gold mining. You want to have sharp eyes that can spot glimpses of gold, and then be able to polish them into something special.”

Frank Chimero in “On Iteration“

+ + +

“I think basically zero layoffs are happening because of AI. It’s just a plausible excuse for corporate reputation management.

AI does reduce hiring rates not because people believe it can do roles, but due to general uncertainty, chaos, and the sense that team gaps can be managed longer.”
Tom Goodwin in a Feb-5-2026 tweet

Which was replied to by Victor Okefie (@LucidTheEagle) “AI is the perfect scapegoat, technologically plausible and morally neutral. The real cause is often a strategic reallocation of capital or a correction from over-hiring. The ‘uncertainty’ it creates is a force multiplier for austerity; it lets management freeze roles under the guise of futurism, not failure. The gap isn’t filled by AI, but by stretching the remaining team thinner under the banner of ‘waiting to see.'”

Possibly related:

AI winter is experiencing global warming.
Andrew Shindyapin (@ph0rque)

+ + +

“Good engineering is characterized by gradual, stepwise refinement of products that yields increased performance under given constraints and with given resources. […]

The most difficult design task is to find the most appropriate decomposition of the whole into a module hierarchy, minimizing function and code duplications. […]

Communication problems grow as the size of the design team grows. Whether they are obvious or not, when communication problems predominate, the team and the project are both in deep trouble. […]

Reducing complexity and size must be the goal in every step—in system specification, design, and in detailed programming.”
Niklaus Wirth in “A Plea for Lean Software” (1995)

+ + +

“Few ideas work on the first try. Iteration is key to innovation. […]

I have learned that innovation requires a clear vision, many iterations, and a willingness to learn and improve. ”
Sebastian Thrun Update on our Summer Pilot

+ + +

The goal of a customer interview is to uncover the truth, not to sell. If you don’t come away knowing something new and actionable, you’ve wasted the interviewee’s time and yours. Your mindset should be: ‘What does this person know that invalidates something I thought was true?'”
Jason Cohan in “The Iterative-Hypothesis Customer Development Method“

+ + +

“Something that has always puzzled me all my life is why, when I am in special need of help, the good deed is usually done by somebody on whom I have no claim.”
William Feather

I am a big fan of William Feather and have blog about him several times:

• Recipes For Longevity in “Mutual Improvement Clubs”
• William Feather on “Perseverance Rewarded”
• William Feather on “Dead Business”
• William Feather’s “The Business of Life”
• More from William Feather’s “The Business of Life”

+ + +

“Stamina is one of the most universally useful traits you can develop. It’s a more broadly applicable advantage than things that are situationally useful, like strength, intelligence, speed, popularity, or motivation. It’s the ability to chip away at goals despite a lack of visible progress. To stay patient. To be on time. To push through difficult material. To forego momentary comfort. To follow instructions or proceed without them. To not headbutt others. To keep an open mind and be willing to renew your perspective.”
Robin Janssen in “Stamina is a Quiet Advantage“

Also collected in his “Working Wisdom.” Robin Janssen is a pen name for Robert Jones. Included because iterations require stamina and an ability to keep your mind open to new perspectives as you learn from earlier solution efforts.

+ + +

“Simulation compresses whatever it can reach. Human effort migrates to whatever it can’t.”
Will Manidis (@WillManidis) in “Rented Virtue“

Simulation is just calculation; it can be plotting a graph from an equation, using Excel to forecast or predict the behavior of a system, or a more complex model that solves a system of equations using numerical methods.  It’s a way to make your imagination visible, to project the likely consequences of a decision on a situation based on your assessment of starting conditions. It trades the time needed for calculation for the consequences of an unwittingly poor decision. It enables you to iterate by restarting with the same initial conditions and making different, and hopefully better, choices.

+ + +

“Program construction consists of a sequence of refinement steps, where a given task is broken up into a number of subtasks. Each refinement in the description of a task may be accompanied by a refinement of the description of the data which constitute the means
of communication between the subtasks. Refinement of the description of program and data structures should proceed in parallel.

During the process of stepwise refinement, a notation which is natural to the problem in hand should be used as long as possible.

Each refinement requires a number of design decisions based upon a set of design criteria such as efficiency, storage economy, clarity, and regularity of structure. You must be conscious of these decisions and weigh the various aspects of design alternatives in the light of these criteria. You may need to revoke earlier decisions and back up, if necessary, to the start.”

Niklaus Wirth in “Program development by stepwise refinement“

+ + +

“To choose the right level of fidelity, consider the following five questions:

1. Who is the audience for this prototype?
2. What is the one most important purpose of this prototype?
3. How many iterations of this prototype are necessary?
4. How much uncertainty is there in the project at this stage?
5. What tools can be leveraged to create the prototype?

Often prototyping is most useful when a sequence of prototypes can be done in rapid succession. Each builds on the learning and discoveries from the previous iteration. When choosing a fidelity, consider the benefit of doing more iterations at lower fidelity.

Bob Moll What is the Right Fidelity of Prototype?

+ + +

“Technological advance is an inherently iterative process. One does not simply take sand from the beach and produce a Dataprobe. We use crude tools to fashion better tools, and then our better tools to fashion more precise tools, and so on. Each minor refinement is a step in the process, and all of the steps must be taken.”
Chairman Sheng-ji Yang, “Looking God in the Eye” (Accompanies the Polymorphic Software technology) from a cut scene in Sid Meier’s Alpha Centauri

+ + +

“I admire Iterative People. They seek constant improvement, take direct action to learn, exhibit humility, and approach the world with a curious mind. They experiment, unafraid to share things that are a work in progress.”

Ryan Hoover in “Iterative People“

Hoover suggests some way to cultivate a mindset of constant improvement: share ideas and ask for feedback, in particular with curious, action-oriented people; follow your inspiration when it strikes by capturing them in writing and take at least one step forward.

Image source: 123rf.com/profile_jeremyarnica and 123rf.com/profile_gheoronstan

Chatter and Silence

“One of those strange instants of silence descended, as though a hundred unrelated conversations had simultaneously arrived at the same pause.”
William Gibson in Neuromancer

It’s an astounding effect when multiple concurrent conversations in the same room all pause at once. I wonder if it’s sometimes a cascade effect similar to a school of fish changing direction in response to an invisible cue: two or three conversations pause simultaneously and others start to pause in response to this subliminal cue. As more conversations pause, the silence ripples out, causing even more to pause. A similar effect sometimes happens in a crowded room as everyone starts talking louder to be hear, increasing the background noise which in turn triggers people to raise their voices. At a certain volume threshold people catch themselves and pause and the silence is suddenly as deafening as the earlier background noise was.

It’s a phase change in behavior that can also be triggered by a loud noise or other disturbance.

“A group of men had come into the Great Hall and were gathered around Clavius. they looked like a corporate body of some sort, a board of aldermen, perhaps, or the chiefs of the local board of medical ethics, for they all dressed similarly, in loose dark jackets with wide lapels and trousers with soft boots to mid-calf, and they moved in reference to each other, not simultaneously like a school of fish, but with the symbiosis of effort shown by beavers or wolves. Sober, punctilious men and women, they were anxious to get about their business, and seemed unamused by the disorder of the Clavius menage, though they were obviously used to it.”
Alexander Jablokov in “Carve the Sky“

Here Jablokov hints at different movements towards a common purpose like fingers of a hand grabbing a tool or a handhold.

“From a distance you look like an aircraft carrier, but as you get closer it becomes clear you are really a thousand canoes. ”
Rick Munden recounting a vendor’s description of TI

I think most organizations behave more like a  large school of small fish than a whale.

We had a number of aquariums when I was a boy and I was always fascinated by how a school of fish coordinated their quick darting patrol of a volume of water. They would suddenly change direction but I could never tell why or how they decided. I suspect a lot of phenomena are like that, they frolic in plain view but beyond our understanding, like the invisible ineffable cues that a school of fish use to synchronize their movements.

Related Blog Posts

• Time to Market: Tips for Strategic Networking
• Working in Silence
• Rules of a Scientist’s Life, Applied to Entrepreneurs
• Six Excerpts From “Carve the Sky”
• Revisiting Neuromancer after Three Decades