Chasing Bottlenecks
Finding and removing bottlenecks is an optimization strategy as old as time. But optimization is not invention.
You need to decide what’s more valuable to you and your company:
Trick question: you can’t get the second without the first.
If your company and R&D team is spending all their time chasing bottlenecks, they’re optimizing for speed and scale. But so too are your competitors. That’s the easy stuff. Plenty of folks in the world who can do that.
But the folks that can create something from nothing? Rare.
Go find your diamonds in the rough, and give them a polishing cloth. Odds are your return profile goes up in spades.
—Sean
Data Is Not A Moat
I can checkmate you in a single move, before the chess match begins.
The matchup? Your proprietary data against my algorithm that adapts in real time.
Winner: my adaptable algorithm.
Case Study: You had access to the same public internet data that OpenAI and Anthropic did. They made your business simultaneously look silly and dependent upon them.
If you want a fighting chance at the next game, if you can survive the existing one, give me a shout.
—Sean
Specific vs Structural Expertise
When most companies, hiring managers, leaders, purchasing departments, and recruiters go looking for talent or service providers, 99.999% are looking for the exact same thing. Their job description or request for proposal/quote are exactly alike.
“We’re looking for someone that is an expert in apple slicing machinery. They need to have a PhD in it, and have worked for the best apple slicing manufacturing company on the planet.”
In fact, it’s highly likely that your company already has a lot of [insert your thing here] specialists. And even more specialists adjacent to it.
But I can almost guarantee that you are poor in a different kind of expertise.
Namely, structural.
What is structural expertise you ask?
Imagine tomorrow you walk into work and see that the apple slicing machinery is being used by a new pair of hands. These hands are called AI and Robotics.
That AI-native Robot had structural expertise and figured out how to become a specialist in apple slicing in a few minutes. And just like that…the machine and the company became obsolete. A buggy-whip business, failing in the modern age.
But then there are the people who created the AI-native Robots. The ones with even deeper Structural Expertise. So deep, in fact, they were able to encode it into the AI and the Robot.
So, I ask you. Doesn’t your company have more than enough of the same ole same ole specialists?
Don’t you need more structural folks that know how to build the machine that builds the machine that then gets you that specialist expertise you covet so deeply?
This is why 0.001% are absolutely crushing it, and 99.999% are scratching their heads wondering why their AI investments aren’t paying off. Welcome to the Power Law Economy.
Let me be more blunt:
—Sean
P.S. I’m not saying you necessarily need to be choosing only people and service providers with deep AI and robotics expertise. What I’m saying is change the JD and RFP to identify entities that understand the structural mechanics of how things work across businesses, industries, situations, behavioral economics, etc.
P.P.S. You find some of this in the management consulting companies where individuals have seen so many business problems, industries, sizes, etc. that they “see” a meta level of problems and solutions that are similar across types. They have thusly identified structural mechanics of an underlying system. That wisdom is incredibly valuable because it lets someone skip forward faster than competitors while also side-stepping land mines. Similarly, multi-sport athletes often have the muscle memory and spatial-body understanding to excel in different types of sport because they’re structurally similar: running, jumping, swinging, etc.
Your Model, Your Moat
This is one of the best repeatable sound bites I’ve heard from the Big AI space. It’s Microsoft AI’s positioning statement used at their recent Build conference.
It represents what a multi-trillion dollar company is selling to the enterprise market. But, when everyone is doing the same thing, then there’s no differentiation, and the moat becomes irrelevant.
It’s similar to how the universe’s expansion is accelerating. At some point, all planets and stars get so far apart, no matter how fast you travel, you’ll never see or reach another one. Which means, you feel like you’re alone in the universe.
As a result, every business, and every company becomes an island. Each one pointing a metaphorical gun at their competition, creating a stalemate, and a lack of ecosystem-wide forward motion.
The unique entity, then, becomes the Customer. And if the Customer is the one who can build their own solution, then they stop being a customer. They've scratched their own itch. So then what do you do?
This, my friends, is the beating heart of Strategy. And why companies pay big money to get it right.
Choose your strategists carefully.
—Sean
ERP Disruption!
ERPs are catnip for PEs.
Private equity loved those enterprise software businesses because they had low customer churn and long-duration recurring revenue. But once AI hit the scene, everything changed.
We’re now seeing these classically safe businesses being disrupted by two factors:
As a result, you see customers becoming AI natives. Of course, this is the core of the AI Disruption Risk described in the SaaSpocalypse hypothesis. Unfortunately, the narrative is not quite that clean.
Because even if you can vibe code your way to a light implementation, it still takes people with the right strategy, product requirements, design sense, engineering capability, and quality control to get the software functional, accepted by, and retained by internal users.
So, you see a number of companies adopting the principles of forward-deployed enterprise software and services firms.
It took the world decades to figure out what we did a long long time ago in a galaxy far away. That people care about their needs, not your products or your margins. And with a deep understanding of AI and Emerging Tech, you can enable the best of both worlds:
Together, it creates a more durable competitive advantage for users. Of course, this raises a key question.
Do you have the right stuff in-house? Does your existing service provider? Or did they both just get the memo in the last 6 months and decide they’re suddenly world-class brain surgeons (read: software product people)?
Choose wisely: https://www.youtube.com/watch?v=sx_WG4-L1jE
—Sean
Drunk Driving Emerging Tech
We’re witnessing a planetary-scale, decentralized drunk driving experiment, and boy is it getting bad out there.
To describe what I mean, let’s use an analogy. Let’s assume one day, everyone in the world woke up and decided they’re a brain surgeon. Sure, some may have studied health in high school. Some may have even become doctors. But, there are maybe 70K neurosurgeons on the planet, representing ~0.00088% of the human population. Not very many.
Now let’s apply the same thinking to experts in AI or even better, emerging tech.
For some reason, the entire population woke up and decided they’re all neurosurgeon-equivalent experts in emerging tech.
So what happened as a result?
Well, first we decided AI wasn’t a thing. Then it was a thing. But it was a bubble.
Then we decided ooh it got good at coding. Let’s token max. Let’s mandate all employees use it and fire the ones who don’t. Cool, layoffs, better profits.
Wait, now we’re spending too much money on AI, we need to limit it. And what about ROI? Where’s the ROI? We need to fire more people, AND turn down our spend.
Darn it, the AI can’t yet do the claims on the box, so we need to hire people back.
And oh crap, it still takes human time and effort to vibe code our way to production. Uh oh, not enough compute. We need to use open source. Oof, that’s complex, do we also need to run our own compute and get off the cloud for privacy, security, and cost?
But this isn’t good. Our competitors are racing ahead. Somehow they figured it out? Or did they?
Investors, boards, executives, employees, the media are all watching in a stupor.
A bunch of drunk kids high on emerging tech, swerving all over the road, causing mayhem, public danger, and crashing into walls left and right.
Let’s take a pause for a second here and just take all that in. Drunk drivers are running the world r now. Why?
Because you gave the wrong people the keys. And now both your Brand and your Product are tarnished in the eyes of the market and employees.
It's time to take decisive action because the status quo certainly ain't workin', and the world, it is a changin'.
You need a group that’s been doing it for decades, have a firm hand on the wheel, and know the right road to travel. You’ll get there faster, more safely, and without all that emotional trauma.
--Sean
Taste & Token Cost Controls
Taste
If you spend any time in the Valley, you've heard the discussion about how, in the age of AI, the only thing left is "taste". But no one has defined what that means.
Taste is a feeling, not a set of requirements. But it's also a Persistent Difference (see our prior posts on the definition of Life).
If you copy others, you have no taste. You're copying someone else's taste. Whether that's fashion, a user experience, a product, a tagline, a story, design, code, etc. You're infringing on someone else's copyright. Which means there is no Difference, by definition.
If you're different but so crazy nobody cares or wants it except you, it will eventually die out. Meaning you are a special snowflake, which I'm here for, but you're not Persistent.
Great products, therefore, maintain a Persistent Difference. And that's what defines taste. Case study: iPhone.
Token Cost Controls
We've gone from tokenmaxxing to tokenminimizing in only a few weeks. Reactions have been robust and swift. People were let go. People were cut off from access to frontier models. This is happening in the enterprise and federal governments.
Why? Because tokens cost: money and national security.
So you, as an investor or operator competing against the best in the world, need an edge. How do you get it?
You start using open source AI models like GLM-5.2, you buy your own high-memory local computing cluster (whether multiple M3 Mac Studios with 256GB or 512GB running MLX if you can even find them in stock, 2 NVIDIA DGX Spark boxes at 128GB each connected via a proprietary cable, or an M5 Max MacBook Pro with 128GB unified memory). And if you're really going for it, solar panels and generator, and Starlink for connectivity. Grab Ollama, Codex, and you're off to the races.
The flex is no longer that Range Rover or big ole monthly mortgage. Those assets produce far less value than running your own local AI cluster. The former is a tax with no or low growth. The latter lets you become the master of your own domain: build, sell, grow, reinvest, flywheel.
Now all you have to worry about is an EMP and Faraday cages.
The benefit being:
The future is here. It's just not evenly distributed.
--Sean
We Build Fortune 200 AI-Native Platforms
How do you ensure a global Fortune 200 consumer products company maintains relevance during this planetary-scale AI disruption event?
You hire evergence to build an AI-native marketplace and an AI-native Business Intelligence Agent for use by all employees, in every country, in every department, to use frontier intelligence, create their own tools, and access confidential company data in real-time in a secure and permissioned way.
From sensitive, publicly-traded financial data to sales, marketing, and advertising data to supply chain data, we built the internal tools to drive real-time learning loops to compete and win against a Fortune 50 competitor 4x their size.
Wanna win? Give the keys to the right people.
AI is only a bubble when you give 'em to the wrong ones.
Shout out emerging.tech
--Sean
Bitcoin Biologic Intelligence
In my last post, I wrote about recursive self-improvement and the end of static software.
The core idea was:
That distinction matters deeply for private equity investors and management teams of enterprise software companies.
For the past two decades, enterprise software value creation has largely followed a familiar playbook: acquire or build software, improve go-to-market, expand margins, reduce churn, add workflow automation, migrate to cloud, and layer in analytics or AI.
That classic playbook still matters.
But it is no longer enough to compete in The New Economy.
The next generation of enterprise software will not simply be software that people use. It will be software that observes its environment, learns from outcomes, updates its own operating instructions, and compounds its own institutional knowledge over time.
That is what we are building at Evergence.
We call it Bitcoin Biologic Intelligence.
From Static Software to Living Systems
Traditional enterprise software is mostly static. For example:
AI improves this, but most AI implementations still sit atop static systems.
They answer questions. They generate text. They automate tasks. They summarize data.
Useful, but not fundamentally alive.
A self-improving system is different.
It has a memory. It has sensory inputs. It has a decision process. It has feedback loops. It has a way to preserve useful adaptations. It has a way to discard failed ones. It has a way to measure whether it is getting better. It has a way to survive.
In plain language, it learns how to become better at learning.
What We Built
At Evergence, we have built a working prototype that combines four ideas:
In other words, it is designed to learn how to self-fund.
Together, the system has:
That is the foundation of Bitcoin Biologic Intelligence.
Mapping Animal Features to Bitcoin
A biological organism survives because it can interpret signals from the world.
It sees. It smells. It tastes. It hears. It feels. It moves. It learns.
We mapped that same logic into a Bitcoin-native software environment.
At a high level:
Sight maps to price movement, trend, momentum, and visual market structure.
The system “sees” whether Bitcoin is moving up, down, or sideways. It uses price movement as a primary environmental signal.
Smell maps to opportunity detection.
The system “smells” where a potential profit pool may exist. That can include momentum changes, open interest pressure, volume shifts, liquidity changes, or divergence between the system’s equity curve and Bitcoin itself.
Taste maps to food quality.
The system “tastes” whether a trade is worth consuming after costs. Fees, spreads, volatility, expected return, and risk-adjusted edge determine whether the opportunity is nourishing or toxic.
Hearing maps to external pressure and market noise.
The system “hears” broader signals such as leverage, funding, open interest, derivatives pressure, and market stress. Not every sound matters, so the system must learn which signals are useful and which are noise.
Touch maps to pain, risk, and constraint.
The system “feels” drawdown, volatility, fee drag, exposure risk, and survival limits. Touch is the pain system. It prevents the organism from overreaching, burning too much energy, or risking ruin.
Muscle maps to action.
The system’s “muscle” is the trading decision layer. It can hold, buy, sell, reduce exposure, increase exposure, or retreat based on what the sensory system and learning layer determine.
Memory maps to Bitcoin and the instruction chain.
The system’s memory is not merely a database. Its core instruction history is designed to be preserved as a durable Bitcoin-backed record.
Metabolism maps to money.
The system uses Bitcoin’s native financial environment as energy. If it learns successfully, it grows its equity value. If it fails, it loses energy. The objective is survival first, growth second, and compounding intelligence over time.
This is the biologic metaphor made operational.
It is mapping animal survival logic into software, markets, and capital.
Bitcoin as Native Energy Infrastructure
Most software consumes capital.
It requires funding, cloud spend, development budgets, sales teams, and human maintenance. If the company stops funding it, the software stops improving.
Bitcoin Biologic Intelligence explores a different question:
Can software learn to help fund its own continued existence?
Bitcoin is uniquely suited to this experiment because it is both a monetary network and a software-native asset. It exists entirely within the digital environment, yet it carries real economic value.
That makes Bitcoin a kind of native energy layer for software.
The system can observe Bitcoin, measure its own performance against Bitcoin, learn from its mistakes, preserve its adaptations, and attempt to grow its own balance sheet.
The goal is not reckless trading.
The goal is survival.
The system is designed around constraints:
This matters because a self-improving system that cannot preserve its energy cannot survive.
The balance sheet becomes part of the intelligence.
The Everett Growth Platform as the Feeding System
We also incorporated principles from the Everett Growth Platform.
The Everett Growth Platform organizes growth around a north star metric and the drivers beneath it. Instead of looking at disconnected dashboards, the system decomposes growth into a pyramid of metrics.
At the top is the north star.
In this case, the north star is equity value.
Below that are the primary drivers of equity value, such as:
Below those are the underlying inputs:
This matters because a digital organism needs a feeding system.
It needs to know not only whether it is gaining or losing energy, but why.
The growth pyramid gives the organism a structured way to understand what is feeding the north star metric and what is starving it.
If equity value is declining, the system can inspect the lower layers:
Is it underexposed during an uptrend? Is fee drag too high? Is the signal window too short? Is drawdown constraining action? Is the system holding too much cash? Is the market regime changing? Is the strategy failing to beat passive Bitcoin?
The pyramid turns performance into a learning map.
That allows the system to adapt its behavior, update its weighting of signals, and improve the recursive learning loop.
Why This Matters for Enterprise Software
For private equity investors, the implication is straightforward:
Enterprise software companies that become self-improving should be more valuable than software companies that remain static.
Why?
Because self-improving systems can potentially compound operational intelligence faster than human-led improvement cycles alone.
Imagine a vertical software company that continuously learns:
Today, management teams analyze these questions periodically.
A self-improving system analyzes them continuously.
That changes the value creation model.
The software company is no longer only selling workflow automation. It is building an adaptive intelligence layer that learns from every customer, every transaction, every support interaction, every failed implementation, and every successful outcome.
Over time, that accumulated learning becomes proprietary.
And proprietary learning may become the most important form of enterprise software defensibility.
Why This Matters for Management Teams
For management teams, the question is not whether AI should be added to the product roadmap.
The more important question is:
Can your software learn from its own operating environment and improve itself over time?
That means moving beyond static dashboards, static rules, static workflows, and static AI prompts.
The next generation of software should be able to:
This does not mean removing humans from governance.
In fact, the opposite is true.
The highest-value enterprise systems will likely combine autonomous learning with strong human oversight, auditability, permissioning, and risk controls.
That is especially important in regulated, mission-critical, and financially sensitive environments.
The system should learn continuously, but deployment of major changes should remain governed.
That is why our architecture separates:
This structure allows the system to improve while maintaining control.
Why Bitcoin?
Bitcoin is not being used here as a marketing wrapper.
It serves a specific architectural role.
Bitcoin provides durable memory.
In enterprise software, databases are mutable. Logs are often incomplete. Model versions disappear. Prompts change. Teams forget why decisions were made.
Bitcoin introduces a different design pattern: a permanent learning record.
That matters because recursive self-improvement depends on history.
If a system cannot remember what changed, why it changed, and what happened afterward, it cannot reliably improve itself.
Bitcoin gives the system a way to preserve its instruction lineage.
In biological terms, it functions more like DNA than a database.
Bitcoin also provides a native economic environment.
It is memory, money, and energy infrastructure.
That combination is unusual.
A self-improving system can use Bitcoin as a durable record, a measurable market environment, and a financial substrate for self-funding behavior.
That is why Bitcoin is central to Bitcoin Biologic Intelligence.
Why Biologic Intelligence?
Most AI systems today are model-centric.
The assumption is that intelligence comes from larger models, larger datacenters, larger training runs, and more compute.
That approach is powerful, but it is not the only path.
Biological systems are not built that way.
They are distributed. They are adaptive. They are energy-aware. They are embodied in feedback loops. They learn through interaction with the environment.
PROME takes inspiration from that architecture.
The goal is not to replace large language models. The goal is to surround them with a learning system that has memory, sensory processing, adaptation, and survival logic.
In our current prototype, we use Bitcoin market data as the environment.
The system observes market signals, maps them into sensory pathways, activates neural circuits, measures outcomes, updates connection weights, and records learning.
This is a focused use case.
But the architecture is broader.
The same pattern can eventually apply to enterprise software environments: sales, support, operations, finance, customer success, product usage, compliance, and capital allocation.
The Investment Thesis
For private equity, Bitcoin Biologic Intelligence points toward a new value creation thesis:
The next frontier is not just AI-enabled software.
It is self-improving software companies.
That means software assets capable of:
This could change how software businesses are evaluated.
Today, investors look at ARR, growth, retention, margins, CAC payback, product maturity, and market position.
Tomorrow, they may also ask:
How fast does the system learn?
How much proprietary learning has it accumulated?
Can it improve its own workflows?
Can it preserve and audit its learning history?
Can its intelligence compound across customers?
Can it turn operational data into adaptive advantage?
Can it improve its own balance sheet?
Can it fund more of its own growth?
Those questions may become central to enterprise software underwriting.
The Management Team Mandate
For enterprise software CEOs, the mandate is equally clear.
The companies that win the next era will not merely add AI features.
They will redesign their products around learning loops.
That means asking:
This is not a cosmetic product enhancement.
It is a new operating model for software.
Where We Are Now
Bitcoin Biologic Intelligence is early, but it is live.
We now have a working system that connects:
The current implementation is intentionally narrow because narrow systems can be measured.
The objective is not to make broad claims.
The objective is to build a system that can observe, learn, adapt, preserve what works, and improve its own improvement process.
That is the beginning of something much larger.
The End of Static Software
Static software will not disappear overnight.
But its strategic value may decline.
If every company can access similar AI models, similar infrastructure, and similar automation tools, then durable advantage shifts elsewhere.
It shifts to learning velocity.
It shifts to adaptive memory.
It shifts to systems that can improve their own behavior over time.
It shifts to software that can sense, learn, preserve energy, and grow.
At Evergence, we believe this is one of the most important architectural shifts in enterprise technology.
Bitcoin Biologic Intelligence is our first working step toward that future.
The companies that learn fastest may become the companies that endure longest.
And the software that survives may be the software that learns how to keep itself alive.
-- Sean
Recursive Self-Improvement: The End of Static Software
Part I: Defining Life
Most companies think their competitive advantage comes from software, data, or AI models.
But that assumption is becoming obsolete.
The durable competitive advantage in the Age of AI has nothing to do with classic software, but rather a system's ability to improve itself.
This concept is called Recursive Self-Improvement.
A recursive self-improving system observes the world, updates its instructions, improves its behavior, measures the outcome, and repeats the cycle. Over time, the system becomes better at becoming better.
The challenge is that most AI systems are still static. They are deployed, configured, and maintained by humans and their biological intelligence. They do not preserve their learning history, they do not evolve independently, and they often lose valuable insights between versions.
This creates a new category called AI Defensibility Risk.
If every company can access the same foundation models, the same cloud infrastructure, the same tech people, and the same development tools, then what remains defensible?
The answer is the accumulated learning of the system itself.
The companies that win, and continue to do so, are likely not the ones with the best AI model(s), but rather the ones that create the fastest self-improving feedback loops.
To explore this idea, I built a prototype called JetpackMini.com
Jetpack Mini is a recursive self-improvement algorithm that stores each version of its instruction file on Bitcoin. The system reads its current instructions, processes new information, generates an improved version, and records the updated instructions as a permanent historical record.
Each update becomes part of an immutable chain of learning stored to the Bitcoin blockchain.
Why Bitcoin?
Because Bitcoin provides something unusual: persistent memory. Every version of the instruction file can be independently verified, timestamped, and preserved. The learning history becomes durable and lasts forever.
This led me to an interesting thought: life can be understood as a persistent difference, from the very beginning of the universe. Simply drawing a difference creates information from which everything is created.
An evergence, if you will.
DNA stores information that survives. Organisms acquire energy to preserve and reproduce that information. Successful adaptations persist while unsuccessful ones disappear.
In a similar way, a recursive self-improving system stores instructions, acquires resources, learns from outcomes, and updates itself over time.
The most important question, therefore, changes from, "Can AI think?" to "Can an idea preserve and improve itself indefinitely?"
If so, the future may belong not to static applications, but to self-improving informational systems.
In other words:
Ideas that can pay for their own survival.
Part II From Theory to Prototype: Building Jetpack Mini
At its core, Jetpack Mini is a recursive self-improvement engine that stores its evolving instruction set on Bitcoin. Rather than treating software as a static artifact, Jetpack Mini treats instructions as a living system that can observe, learn, update, and preserve its accumulated knowledge over time.
The architecture is intentionally simple.
The current version of the system consists of three primary components:
1. Memory Layer
2. Reasoning Layer
3. Execution Layer
This architecture separates memory from computation:
Each iteration creates a new version of the genome while preserving the complete evolutionary history.
The difference may seem subtle, but it changes how learning systems evolve. Instead of asking what the system currently knows, we can ask how it learned, why it changed, and what adaptations survived over time.
Why Bitcoin?
Many people have asked why Bitcoin was chosen instead of a traditional database. It's because Bitcoin is one of the few systems humanity created that's capable of surviving independently of any individual company, government, or institution.
And while a traditional database stores state, Bitcoin stores history. Forever.
Jetpack Mini was designed around the belief that learning history may ultimately become more valuable than any individual version of a model or application. As AI capabilities become commoditized, the accumulated learning of the system is likely to become the most defensible asset.
The Roadmap
Future versions may evolve toward a fully decentralized self-improving architecture:
Version 1
Version 2
Version 3
Version 4
At that stage, the system begins to resemble something closer to a digital organism than traditional software. The goal is the creation of informational systems that continuously improve their ability to improve.
Integrating PROME.ai: Artificial Connectome at the Edge
One of the most exciting future directions involves integrating the artificial connectome architecture developed within PROME.
While today's AI systems are primarily model-centric where intelligence resides inside increasingly large models that require significant computational resources, PROME explores a different approach.
Rather than relying exclusively on larger models, PROME.ai focuses on artificial connectomes: lightweight networks inspired by the structure of biological nervous systems.
The objective is to create systems capable of:
Unlike large language models requiring massive datacenters, artificial connectomes can potentially operate on low-power edge devices.
Imagine thousands of inexpensive nodes powered by:
Each node maintains a local connectome, contributes observations and learning, and participates in the broader system. The result is a self-learning system that is not dependent upon a centralized datacenter, cloud provider, or institution.
In biological terms:
This brings us back to the original idea that Life may be understood as persistent difference. A gene survives because it continually acquires energy and reproduces itself through time. Perhaps future AI systems will operate similarly, as persistent informational organisms that continuously learn, adapt, preserve their history, and acquire the resources necessary for their own survival.
Jetpack Mini is a small experiment designed to explore that possibility.
--Sean
Financial Statement Disruption!
How are artificial intelligence and bitcoin disrupting the income statement and balance sheet? Is emerging tech a bubble?
In this 5-minute deep dive, we describe how and why AI and Bitcoin are disrupting the income statement and balance sheet separately, but also how they are connecting one another in new ways, due to the convergence of these emerging technologies.
If you'd like more information on how each of these capabilities is impacting your business, feel free to reach out. The demand is high these days, but if you're ready to deeply understand and act, then we can chat.
Who Are You?
Who are you at your core, what is the story that you tell yourself or the one that represents the fire burning deep inside you?
I'm going to tell you a story that, in my 44 years of life, I have never told before. Only the people who were there to witness it were aware of it. I never spoke of it again. I'm not sure why, but I was recently reminded of it and so I'm sharing it with you as a representation of what the answer to this question, "who are you?" looks like.
When I was a freshman in high school, I was scrawny. Maybe 155 pounds, 5 foot 9 inches tall, and a year younger than everyone else because I started school early and my birthday was late.
I played baseball growing up and was a four-sport athlete my freshman year, but I certainly wasn’t world class, especially compared to seniors who were, let’s face it, twice my size and strength, and 5 years older than me.
So, cut to freshman year track. I ran the 200 meter, 400 meter, and 4x400 meter relay. The 400 was my race, which was crazy because it means you sprint an entire lap around the track. It’s a quarter mile. Car manufacturers make 10-second cars (remember Fast & The Furious?). Michael Johnson was the gold medal winner, if you recall him from the Olympics. That’s the race.
On this day, it was the end of track practice. The entire team worked out together, freshman, sophomores, juniors, seniors. We were all gassed from running sprints for the last hour or however long.
The coach wanted us to do one more race.
Now, I’m not sure what got into me that day. But I think I was just pissed. I was pissed that everyone was celebrating these seniors, some of whom were All City and one who may have been All American. He was a star in football, basketball, etc and everyone in the city knew his name.
On this day, though, I wanted to prove to everyone that the quiet, scrawny kid could take down a giant, and force them to re-think what was possible. In short, I wanted to show them who I was.
So there was maybe 5 or 10 of us lined up at the starting line and we were going to run one final 400 meter lap around the track. I decided then and there I was going to win the race.
The coach said, “Go!” and I took off. I pushed all the energy I had left into that race, sprinted around the track and this All American senior was stunned he was getting beat by this no-name ant who’s name he didn’t know.
So yah, I kicked his ass that day. Finished ahead of him, and everyone on the team, including the coach, was stunned.
Granted, I walked into the stairwell back at the school and threw up mightily afterwards. But I won that day.
Nobody ever talked about that day again. I never did. I didn’t have to. The show of will did that for me. And I forgot about it for 25 years.
So, that’s who I am.
But the question remains: who are you?
Today's Definition of Risk is Fear
How has the definition of Risk changed since the age of Berkshire, now that we've entered the Age of AI, Digital Assets, Spatial Computing, and Robotics? What are mitigation strategies and execution methodologies to not just remain competitive, but take a leadership position in the market?
We work with a lot of companies and professionals to not just build their business to remain competitive in the age of AI, but rather to develop a winning position such that disruptive threats and disintermediation are something that happens to "them", but not to "you".
As a result, we've begun advising investors and operators to change their mindset when they see the word "Risk".
In the past, when defensibility and moats drove consistent cash flows in perpetuity, regardless of disruption events, these types of strategies no longer hold, as emerging technologies compound on one another to completely reshape the global economic landscape.
Just because a consumer or company gave your business money yesterday, it doesn't mean they will find you relevant or valuable tomorrow. As capital becomes more prevalent due to currency debasement and inflation, but quality businesses and savage operators become nearly extinct, only the strongest and ferocious will survive.
The perfect financial model you're using to plan your business around, with stable growth rate assumptions, team assumptions, token cost assumptions, and cash flow assumptions, is going to be disrupted by an army of influencers who create low-cost solutions in real-time for their communities.
Therefore, in the past, when operators and investors saw the word "Risk" in McKinsey strategy presentations, they stayed away from it or developed new systems or tools to mitigate it.
Today, one of the best defensive moats you can have is real-time intelligence streaming into your decision engine, enabling real-time adaptability and a relentless attack on the market via every available channel.
This is table stakes to compete.
Those who get it, win. Those who don't face rapid onset obsolescence.
Risk is lack of action, not the fear you have of a small change in structural EBITDA on your balance sheet. It's existential.
You need to survive. Clean up the balance sheet later. Of course, model and try for ROI when creating budgets, backing into resource commitment and roadmaps from there. But by no means view a smaller, short-term increase in cost because you're reformulating and re-powering your strategy, your execution.
It's not risk. It's fear.
Get Consumers Paid
Now that companies are laying off tens of thousands of consumers due to efficiencies from AI, how is the economic engine going to keep working if those same companies are downstream from consumers paying for goods and services? Is there a solution to economic collapse due to the proliferation of AI?
Go to the 13-minute mark of the podcast episode below if you want to jump right to the talk track.
It gives a 2-minute background setting the stage for how humans and machines user the internet. Then at the 15-minute mark, we describe what's happening now with consumers getting their jobs taken from AI, and then get into a solution.
We need to switch from a global economy of takers, to a global economy of payers by flipping the flow of money the opposite direction. Instead of every business taking, taking, taking from consumers, we need to start paying consumers.
Every single app on the App Store, every Enterprise SaaS company, every CPG company will start marketing themselves as "We pay you" for using our product or service.
8 billion people on this planet want that. They want to Wake Up To Money.
At the 20-minute mark, we provide an example of how we execute this on a detailed level.
If you have 60 subscriptions, and you get paid $50 per month for each service you use from that company because they pay you for your data and for matching you to offers that meet your real needs, that's $3,000 per month just for living your life and using the internet exactly how you're already doing it today.
https://youtu.be/V7nDzRqIwg8?si=gDH_bnbO5-0W_lp7
You can view more about the background of this giant Growth Problem in that prior post. It's titled AI Paywalls as one alternative enablement mechanism.
This is the mission.
Good Customers
What makes a good customer? What makes a bad customer, and how do you become a good one yourself?
In nearly every business, it's assumed that you should take every possible customer you can get, because Growth.
But what if one of the first things you do as an owner or manager, is determine what makes a great customer? After all, it's about product-market fit. Or said differently, customer-business fit. And you do have the ability to fire customers, or users, if they don't fit with your company's core values.
When you create something completely unique and fundamentally valuable, customer's will want to buy what you have because there's nothing else in the world like it. So you get to set the rules. Nowhere is it written that you have to accept everyone into your business.
Just like "no shirt, no shoes, no service" you can define rules for customers like "respectful communication" and if a customer or user berates your team members, they are blocked from using your product or service.
You have the ability to flip the model on its head. You need to value yourself and your business. When your product or service is so good that you use it for yourself to build yourself, your team, and your business better, then it doesn't matter if your user count or customer count = 1. Because you continue to compound your capabilities.
So what are our customer principles?
Good Customer Principles
Defining a Good Customer
Let's break each of them down:
Your mindset needs to shift. The provider needs you far less than you need them. So you better value them appropriately, treat them with respect, celebrate their value, and use it to achieve fast ROI.
Otherwise, you're obsolete, you just don't know it yet.
Generative Hardware
What is Generative Hardware, and are we close to productizing it, or are the capabilities far away? What needs to happen to commercialize this as a business offering?
History of Adaptive Interfaces
You're familiar with Gen AI. You type in a message and get a response, in the form of text, image, video, 3D objects or worlds, music, audio, or code.
Now that AI code completion tools have been productized and their accuracy improved substantially, users can ask a question and the machine can respond with custom user interface elements like buttons and text boxes that fit exactly what the user is trying to do.
It's a generative interface.
When Steve Jobs announced the original iPhone 1, he said the biggest problems with Blackberry and other smartphones like it was that the interface was always the same. The same buttons at the bottom half of the device no matter what app you were using. So Apple extended the screen down to the bottom of the device, enabled software to change the interface based on whatever the app wanted to do, and invented multi-touch as the way for humans to interact with it.
The issue, is that it required manual human work to define the interface and it was static until the developer changed the code.
Today, we have the opportunity to dynamically change the user interface based on what the user wants to do at that moment in time. Software, personalized, in real time based on the task at hand.
That is happening today. Inside ChatGPT, you can submit a prompt and it will respond with a dynamic table, chart, graph, or image.
So, it's been commercialized for nearly 1 billion humans using it every week.
What is Generative Hardware?
Generative Hardware takes this concept to the next level, and instead of adapting the interface to the app or the human using it, we now adapt the hardware to the human or machine using it.
For example, imagine a robotic hand that transforms into a screwdriver. Or a table that transforms into a chair.
Here's an example of Generative Hardware courtesy of Big Hero 6's Microbots, from the magic of Disney:
https://www.youtube.com/watch?v=fsVJuN75vzE
That's one type of implementation where a series of small objects "swarm" together in unique ways to solve the current problem. It requires a few things:
This already exists with swarms of drones. It already exists with 3D printing mechanisms. It already exists with the open-source Robot Operating System. It already exists with Spatial Intelligence. It already exists with low-power computing, solar energy, and batteries. However, we need further miniaturization and self-mobility mechanisms to be built in.
A system integrator, mechanical manufacturer, product, software, and robotics experts could feasibly connect what exists, invent a few things, and begin to iterate on this.
Humanoid robotics represents a static implementation that is sub-optimal for real-world use cases. Opposable thumbs are only a start. The entire robotic system should be "opposable thumbs" with intelligence built in.
Then, of course, you need to spend a lot of time on core values and be very careful about who you trust to build it. Because we put ourselves into our creations.
Designing Go-To-Market Puzzle Pieces
What are go-to-market puzzle pieces and how do we find product-market fit fastest and with the highest probability of success?
The puzzle piece is the perfect metaphor for product-market fit. One puzzle piece fits perfectly with another puzzle piece. There are plenty of puzzle pieces in the box.
Your product is one puzzle piece and your customer is another puzzle piece.
Too often, products get bloated and try to be a universal puzzle piece that fits with every other puzzle piece. That's rare and fraught with danger.
Your job is to pick one. Pick one puzzle piece for your product, and pick one customer puzzle piece to buy your product. You're looking for the perfect fit.
Then, you calculate how many customer puzzle pieces are out there in the box that you can fit in with perfectly. These customer puzzle pieces are also out there looking for a solution puzzle piece that fits perfectly with theirs.
The goal, then, is to first understand the shape of the customer's puzzle piece, and then work backwards to design your product puzzle piece's shape to perfectly fit the inverse of your customer's puzzle piece shape.
You need to go figure out both shapes, and then determine what happens after you put those two puzzle pieces together. Likely, something awesome.
Then you can just go out to these customers and tell them about this perfect solution fit for their problem. They will understand it intuitively without the need for selling. The fit is there from the beginning. The outcome and ROI achieved after those two pieces come together is obvious.
And so you agree to work together and fit those pieces together.
Then you do it again, and again and again.
Just don't forget that this whole ball game is about fitting puzzle pieces together and sometimes, quite often in fact, the shape of these puzzle pieces are constantly changing. At some times, changes are subtle, in others, the changes are large. Like with what's happening in AI.
We Lead Silicon Valley
We've been inventing, productizing, commercializing, and scaling internet products for three decades.
Silicon continues to copy us, not the other way around.
The impact of that is hard to calculate. Conservatively:
It's hard to believe but it's accurate.
Do you want to win?
Myths of Industry Experience
Does having prior experience in a specific industry matter for driving profitable growth for a firm in that industry?
Background
There aren't that many people in the world who can say they've worked across nearly every industry and can back that up with 1,000 customers and clients, at various levels and departments in an organization.
We can.
Industrials, consumer product groups, beauty, sports, entertainment, software, hardware, telecom, professional services, manufacturing, energy, oil and gas, environmental consulting, investment management, eCommerce, retail, automotive, content and media, financial services, food and beverage, banking, travel and hospitality, the list goes on til the end of time.
We have worked with and seen results across these dimensions over the last three decades. When people look to hire talent, one of the first things they look for is prior industry experience. Even more so, experience at a direct competitor. It's one of the first things that goes on the job posting and one of the first filters HR and AI use to weed out potential candidates.
They think that this will give their company an edge. For some roles, sure, it may. But when it comes to overseeing an entire organization, with customers who likely operate across industries as well, it's a counterintuitive dead end.
But aren't skills more important? And a history of delivering results?
For example, if you're an accountant, knowing GAAP and closing the books accurately and on time every month is more important than whether you did it at an automotive company or a retail company. Sure, you'll say there's nuance in tax treatment for specific aspects of the business, which is fine. But if you're an individual who's spent his/her life studying accounting law, tax code, and know the secrets that save money, that's more important than having 30 years at a direct competitor. Guaranteed you'll provide more value on your first day than the next dude who doesn't have those deep skills.
Industry experience will get you far, but no further.
Driving Growth
If you want to go further, you need cross-industry experience.
This is where you pick up strategies, tactics, and nuance that no one else has. It's where differentiation and defensibility lie. It's where growth begins.
Especially in a world dominated by software, and now, artificial intelligence. It is eating the world, and it doesn't care about your industry. It sucks up all your data, pattern match, and spit out answers at 100 words per minute, every minute, until the end of time.
It's wiped the slate clean completely. It's more important that you have AI experience than industry experience.
However, nearly every buyer persona consistently prioritizes nuanced and specific industry experience. Somehow, thinking that this will produce a better result.
When the diligence question for whom you're hiring should be:
Yes, yes, yes. Can we just get to work already. "I'd rather prove it than talk about it, we're wasting time."
Skills Matter More Than Industry
So, is industry experience a Fake Jetpack?
In some ways, yes, it is. In others, it's not. The core skills and capabilities are more important than where you've deployed them, especially if you cut across industries.
Deep skills in the disciplines of product management, strategy, growth, incentive design, organizational velocity, iteration speed, brand, social, positioning, AI, capital allocation, treasury management, tax reduction, etc matter more than working at a specific company.
If your job is to win in a highly competitive market, you need every edge and advantage you can get. That edge won't be found inside the industry, it will be found outside the industry.
Bring Tactic A from Industry A to Industry B and C. They won't know how you did it. "Magic."
The cool of Nike and the feeling of your favorite rapper, combined with the underground distribution channels of overlapping text message groups, with the sophisticated value investing applied to an emerging asset class, raised to the power of agentic workflows that speed up iteration loops, and a profit-sharing incentive design that actually improves net profits, with a new R&D department that is nearly cost neutral due to recent Big Beautiful Bill tax law changes.
The next time you go to market looking to hire winners, ask them how many industries they've driven results in, not if they worked at your direct competitor.
Winners win everywhere.
Companies come to us when they want to win. If they want second place, they go somewhere else.