Gennaro Cuofano's Blog, page 68

Salesforce AI Transition

A few days back, Salesforce announced the cutting of a thousand roles while at the same time going all in on its AI product line.

In short, Salesforce is in the midst of the most critical transition.

The “Catalyst quadrant” framework, where in parallel, you have to defend-attack while you also transform.

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Google’s AI Transition

Google is a $350 billion a year run rate machine, which is now in the business of AI.

The key thing about Google’s search machine, is the company has managed to lower so much the cost of running a query, while at the same time making many times over it.

Google is now integrating AI on its core, in two ways:

Google Search, with interfaces like AI Overviews in Search which has just gotten expanded to 100+ countries, increasing search engagement, especially among younger users.Google Ads Machine, with Google integrating AI features, from image generation to targeting to improve the delivery and reach of these formats.

What it means, is that with this simple AI integration, one Google Search can at least try to patch things up, in the short-term, as redefining the whole search UX for AI, at Google’s scale is (almost) mission impossible.

While on the Google Ads side, the company can boost advertising budgets run through it, thus, for now translating these AI enhancements as a revenue leg up.

With the premise, that as Google Search might lose scale, advertisers might also opt for other platforms.

Yet, a key advantage for Google is it also owns YouTube, which is probably still the most impressive digital platform out there.

With the integration of AI into its Ad Machine, for instance Google has seen ain improved search performance, which has also driven the cost per click higher, thus probably making the company a few more billions a year.

Thus, as of now, Google’s core moat is the search business.

And yet, the company has to defend it, while attacking on the AI side, and transform-create on new AI-native lines of business.

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Agentic AI

In just about two and a half years, we’re assisting a further reshaping of the AI industry as we finally moved past the point where an AI needs continuous prompting to complete a task.

In what we can now call human-looped-in AI.

As in my reply to Amjad, founder of Replit, we moved from human-in-the-loop to human-looped-in.

That’s a further paradigm shift.

Or from a place where the human is instrumental in the AI to initiate a task and for the human to prompt its way through getting that complete to a place where the human only gets “looped in” by the AI (working in the background) whether to provide feedback on an intermediary work done or simply feedback on a final executed work.

From human-in-the-loop AI to human-looped-in AI

In human-in-the-loop AI, humans prompt the system to travel from point A to point B, guiding each step with explicit instructions.

In contrast, human-looped-in-AI empowers the agent to independently complete the entire cycle of tasks, with human involvement limited to providing strategic feedback or final approval.

This approach leverages the AI agent’s autonomy to manage complex operations while incorporating human oversight to guarantee quality and correctness.

Ultimately, it creates a more efficient, collaborative workflow between man and machine.

This model transforms conventional roles, ensuring AI completes tasks autonomously while humans carefully review outcomes.

Indeed, just yesterday, OpenAI released Deep Research.

This multi-step agent completes tasks, reports, and research that would have taken an analyst anywhere between a couple of hours to a few days to complete!

This is part of a much wider paradigm shift.

A platform shift is a fundamental transformation where incumbents initially leverage established distribution moats for short-term advantage in emerging markets but eventually struggle to adapt.

As new technology paradigms disrupt old business models, rapid shifts expose vulnerabilities, forcing companies to evolve or risk losing competitive dominance in dynamic cycles continuously.

Let’s review some of the key concepts that make this happen in the first place.

The Incumbent Paradox

The incumbent paradox (where the incumbent gets a leg up in the short-term, which loses effectiveness slowly, then quite suddenly) acts as a short-term distribution moat, allowing an incumbent to gain an early advantage in a newly developing market during a platform shift.

That’s a period of major confusion among the practitioners and onlookers, as it’ll prevail the narrative of “the new tech is just making incumbents richer and more dominant.”

Yet, that is only temporary. Indeed, as the platform shift happens, the tech incumbent will actually be the most challenged in making a core transition of a business model that is quite fit with the previous paradigm.

In this phase, the incumbent leverages its distribution strength to get a leg up, and it works!

Until distribution on the old paradigm, “slowly, then quite suddenly,” no longer works as a moat.

The Crossover Point

Thus, once the shift gains momentum, the gap between the old and new market widens rapidly, and the new distribution model takes control.

At that stage, the incumbent loses its competitive moat unless it has successfully adapted to dominate the new paradigm and platform.

The Platform Shift Dilemma says that moats that have worked for decades suddenly don’t.

Many incumbents are going through this in a very weird time window, where in the next decade, many of these might lose their core moats.

This dynamic, much slower in other industries, is instead the norm in a tech-driven business world.

Non-Linear Market Dynamics

The boundaries in the tech world are quite blurred as new markets develop continuously, and it’s tough to predict which one will be the next one that will eat them all while expanding on top.

Therefore, as a company operating in the tech industry, you must continuously transition your business model as you place bets on the future.

Indeed, the tech industry is super competitive; that’s what Andy Grove meant when, back in the late 1980s, he published “Only the Paranoid Survive.”

Look at Intel today, the market leader only 30 years ago, now an acquisition target.

Technology, which seems such a cool sector today, is also quite a wild ride, where the first-mover, in most cases, doesn’t make it to the other end of a market it opened in the first place.

It’s also a place where, in new markets, co-opetition is the rule, as technology markets operate with very complex dynamics and blurred boundaries where friend and enemy turn into “frenemies.”

When the future market gets dominated by startups turned into incumbents that benefit from winner-take-all, make the rule of the game until a new technology paradigm eats up the whole industry to go full cycle again.

That’s a key lesson to remember in the world of AI as it develops!

Recap: In This Issue

We’re looking just now at the emergence of a new AI shift (multi-step agents). Indeed, in human-in-the-loop AI, humans prompt the system to travel from point A to point B, guiding each step with explicit instructions.

In contrast, human-looped-in-AI empowers the agent to independently complete the entire cycle of tasks, with human involvement limited to providing strategic feedback or final approval.

That’s part of a wider platform shift.

Platform Shift: A platform shift is a fundamental transformation where incumbents use established distribution moats for a short-term advantage in emerging markets but eventually struggle to adapt as new technology paradigms disrupt old business models.The Incumbent Paradox:Incumbents gain an early edge by leveraging their existing distribution channels during a platform shift.This advantage is temporary—initial strength fades as the market evolves, leaving incumbents vulnerable when the old distribution model no longer serves as an effective moat.The Crossover Point:As the shift gains momentum, the gap between old and new market models widens rapidly.Incumbents lose their competitive edge unless they successfully transition to and dominate the new paradigm, marking the critical crossover point.Platform Shift Dilemma:Moats that have protected incumbents for decades can suddenly vanish in the face of disruptive changes.This leaves companies in a challenging situation where adaptation is essential or they risk losing their market dominance.Non-Linear Market Dynamics:In the tech world, market boundaries are constantly shifting as new markets emerge.Competition is highly unpredictable, and co-opetition—where competitors both collaborate and compete—is the norm.Rapid, non-linear shifts mean that today’s first movers often don’t survive in the long run, with startups turning into dominant players until another disruptive cycle begins.Dynamic Cycles of Dominance:The tech industry undergoes continuous cycles where new paradigms disrupt the status quo, forcing incumbents to evolve or be overtaken.This creates an environment where constant evolution is required to maintain competitive dominance.

With massive  Gennaro Cuofano, The Business Engineer

This is part of an Enterprise AI series to tackle many of the day-to-day challenges you might face as a professional, executive, founder, or investor in the current AI landscape.

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Meta’s AR Transition

​Entrepreneurs and executives must balance out the short and long term. At times, short and long-term do converge. At other times, they seem to diverge.

A business model is always in transition, as it operates in a dynamic context, which might be more or less stable depending on a set of external factors.

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For instance, as we speak, many incumbents (Google, Meta, Apple, Microsoft, Salesforce) are all transitioning their business models to fit the new developing context.

The interesting take is that no one knows yet what that context will look like, yet everyone needs to be carefully betting on it, with a “four-pronged approach” that combines defense-attack on the one end and transform-create on the other end.

One example is Meta, which has just come out with its financials for 2024.

While numbers look impressive, when you drill down into the Reality Labs segment, its losses amounted to over $17 billion in 2024.

I’ve argued that in the previous years, that money was “wasted” on a flawed vision, which in Zuckerberg’s mind was all about VR.

That money is well spent, as the company has announced a complete reorg around AR.

In other words, Meta is just undergoing a massive transition of its core.

And it’s not an easy one.

This is all coming together as part of the Transitional Market Dilemma.

The Transitional Market Dilemma

If you’ve been operating long enough in the business world, you might have reconciled yourself with the seemingly conflictual relationship between the short-term (survival) and long-term (vision).

How do you make sure that the short-term positively converges with the long-term vision of your business?

A concept I developed as I dived into various business models over the years is “transitional business models.”

To me, that has been a transformative concept, as it has removed the apparent conflict between the short and long term as a whole.

This concept changed my view of the business world.

The Transitional Business Model

The Transitional Business Model is a “temporary market hook” that works for the first phase of market validation and scalability to create options to scale, creating a much larger market opportunity.

It comprises three phases:

Phase 1: Initial Hook (Market Validation)The product or technology enters a small, limited market for early adoption.Companies focus on proof of concept, traction, and generating initial revenue to validate feasibility.Phase 2: Market Expansion (Building Scalability)Growth accelerates as the business refines its model and crosses the chasm to reach a broader audience.Companies focus on scalability, optimizing operations, and expanding market share.Phase 3: Scale (Full Vision Realized)The technology or business reaches large-scale adoption, high scalability, and sustainable growth.It transitions into a foundational business model with mass-market appeal.

Key transition zones are:

Market Entry Zone : Testing viability with a niche market. Growth Zone : Scaling efforts to reach mainstream users while finding footing in a “foundational business model.” Scale Zone : Achieving long-term growth as the transition is completed.

This is part of an Enterprise AI series to tackle many of the day-to-day challenges you might face as a professional, executive, founder, or investor in the current AI landscape.

How?

The transitional business model in a nutshell

When companies like Netlifx, Meta, and Tesla start rolling out their business models, they go through a phase called the “transitional business model.”

A transitional business model is used to gain traction in a market that is not necessarily big or initially scalable.

If we break down ​business strategy​ into three core parts:

​Market entry​ (or go-to-market​ ) requires initial traction (also in a niche market). ​Growth and market share acquisition , requiring expansion (from niche to broader).And business model renewal requires integration , consolidation, or innovation​ (you either acquire, merge, or place bets).

For any company, fully rolling out this strategy (depending on the industry) might take at least a decade.

A transitional business model is a model that will serve the purpose of gaining initial traction and market validation.

Therefore, it will help with market entry and shape the long-term vision as rolled out.

A transitional business model might seem obsolete in hindsight, yet that is the same model that proves the idea’s viability while keeping it alive.

A transitional business model might not be scalable.

Yet, that model will help create an initial positioning and get the funding (revenues or capital) needed to roll out the scalable business model.

A transitional business model might not have a long-term vision, yet it will help shape it.

Thus, a transitional business model works in the short term to validate the market and enable the technology and its ecosystem to mature while still having a reality check.

This is the core premise of a renewed business playbook that doesn’t just rely on growth capital.

It moves by (also) securing growth capital and then validates the market step by step.

There are plenty of examples of transitional business models:

​Facebook’s initial transition to scale, a former college social network, would open up to anyone just later on as it gained substantial traction.​Netflix’s initial transition to scale moved from a DVD rental company to a streaming platform only much later.​Google’s initial transition to scale, before building the most powerful advertising machine ever built, sold advertising through its salespeople.What is a transitional market?

The basic premise of the transitional business model is that it will sit on a temporary market that will serve as a launch ramp and transform it into a scaled-up version.

Yet, as technologies mature often in parallel with others, as a company, you can’t control that, and it’s hard to predict when that “fundamental shift” will happen.

For the sake of it, the transitional business model will help keep a hook with the current market context, finance the business model in short, and keep it alive long enough to also have the financial resources to experiment with the fundamental paradigm shift as it becomes viable.

I’ll tackle the transitional market in the upcoming issue.

Why Is Meta Underoing This New Transition?

As I’ve explained in previous issues, the convergence of AI with many other technologies will create a paradigm shift, which is just starting to show its potential.

A business model is always in transition, as it operates in a dynamic context, which might be more or less stable depending on a set of external factors.

For instance, as we speak, many incumbents (Google, Meta, Apple, Microsoft, Salesforce) are all transitioning their business models to fit the new developing context.

Thus, again the convergence of AI with other technologies will create a paradigm shift that will work to create a whole new market in the coming years.

Now, the thing is, this transition will start from the outer layer of the business ecosystem (the last to develop), which is the web.

That will create massive pressure on the incumbents that have dominated it, which, while in the short-term might seem to dominate this paradigm (“incumbent paradox”), in the longer term, if not embracing it, these will be threatened to their core.

That’s where we are!

If Meta doesn’t figure out what the next major platform shift is happening, with AI emerging, that might completely threaten its core advertising business model.

Therefore, it will be critical for Meta, just like other incumbents, to go through a “transitional business model” again, sitting on top of their core one to find a new mass scalable market on top of which they’ll build the new core.

Meta’s New Transition

Meta is embarking on a massive AI-driven transformation, introducing a wave of AI-based hardware products while integrating AI deeply into its ecosystem.

Among the highlights are Oakley-branded smart glasses for athletes, slated for 2025, alongside high-end glasses with built-in displays that showcase advanced technology.

Meta is also revisiting its smartwatch project and developing camera-equipped earbuds as an AirPods rival.

The company’s first AR glasses, codenamed “Artemis,” represent a leap into true augmented reality, with a planned release in 2027.

Meta’s Orion is still a prototype. Can the company make it compact enough to fit into an AR glass? Source: Meta [image error] Meta’s Orion in action

These initiatives reflect Reality Labs’ leadership and Meta’s dedicated division for hardware innovation, signaling the company’s ambition to dominate the AI and wearable technology markets.

AI integration will be central to all these devices, transforming their functionality and user experience.

With this pivot, Meta appears poised for a potential reorganization, similar to shifts seen at Google and Microsoft, as it aligns more closely with its AI strategy and hardware ambitions.

This move is, therefore, part of a broader transformation to fit into the new business landscape.

Will that succeed?

Recap: In This Issue!Meta’s Business ShiftMeta is undergoing a major transition from a VR-focused vision to augmented reality (AR).Reality Labs reported over $17 billion in losses in 2024, but the company sees AR as the future.Meta is launching AI-driven products, including smart glasses, AR glasses (codename “Artemis”), and AI-integrated wearables.The Transitional Business ModelA transitional business model is a temporary market strategy that helps companies gain initial traction before scaling.It consists of three phases: Market Validation, Market Expansion, and Full Vision Realized.This model helps businesses navigate industry shifts, securing short-term survival while preparing for long-term growth.Even incumbents, when there’s a paradigm shift, need to use a beginner’s approach to tackle again a new market that develops.Examples of Transitional MarketsFacebook: Started as a college social network before expanding globally.Netflix: Transitioned from DVD rentals to a streaming giant.Google: Initially sold ads through a sales team before automating ad placements.The Transitional Market DilemmaBusinesses must align short-term success with long-term vision.Major tech incumbents (Google, Meta, Apple, Microsoft, Salesforce) are adapting to new AI-driven markets.The challenge is predicting when foundational technology shifts occur.Meta’s Future DirectionMeta is restructuring around AI and AR to compete in a changing landscape.AI-powered personalization will drive future Meta products.The company’s success depends on navigating this transition effectively.

Ciao!

With massive Gennaro Cuofano, The Business Engineer

This is part of an Enterprise AI series to tackle many of the day-to-day challenges you might face as a professional, executive, founder, or investor in the current AI landscape.

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Tesla’s Robotics Transition

Tesla is undergoing a critical business model transition, which represents another significant change for the company: to expand the market on top of which it operates.

A few years back, in 2020, when I looked at Tesla, I asked, “Is Tesla a car company?”

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And already back then, my answer was the following:

In my piece about “non-linear competition,” I argued back then how the boundaries in the tech world are quite blurred as new markets develop continuously, and it’s tough to predict which one will be the next one that will eat them all while expanding on top.

Therefore, as a company operating in the tech industry, you must continuously transition your business model as you place bets on the future.

Indeed, the tech industry is super competitive; that’s what Andy Grove meant when, back in the late 1980s, he published “Only the Paranoid Survive.”

Look at Intel today, the market leader only 30 years ago, now an acquisition target.

Technology, which seems such a cool sector today, is also quite a wild ride, where the first-mover, in most cases, doesn’t make it to the other end of a market it opened in the first place.

It’s also a place where, in new markets, co-opetition is the rule, as technology markets operate with very complex dynamics and blurred boundaries where friend and enemy turn into “frenemies.”

When the future market gets dominated by startups turned into incumbents that benefit from winner-take-all, make the rule of the game until a new technology paradigm eats up the whole industry to go full cycle again.

That’s the wildness of the tech industry.

Indeed, in this context, as I’ll show you, Tesla is starting to become a general-purpose robotic company, and cars are only the start of this journey.

Yesterday, I highlighted the one that Meta is going through.

A quick reminder about Tesla’s several transitions before we jump to its next one!

The First Tesla: From an electric sports car to a general electric car

When companies like ​Tesla​ start rolling out their business models, they go through a phase called the “transitional business model.”

That model works in the short term to validate the market and enable the technology and its ecosystem to mature while still having a reality check.

Therefore, launching products will allow the company to test larger and larger segments of that same market.

You might want to keep an ambitious, long-term vision, but you have built-in reality checks in the short term.

As I always try to keep in mind, strategies take years to roll out, and they seem obvious, but only in hindsight.

Therefore, the transitional business model is a “market hook” that creates enough traction to keep financing your ambitious long-term goal.

A few years back, when Tesla was still going through a pretty rocky time, it was clear that Tesla’s strategy was fully rolling out via a transitional business model lens:

The Second Tesla: From electric cars to self-driving leader

It was 2006 when Tesla, with his co-founder Martin Eberhard, launched a sports car that broke down the trade-off between high performance and fuel efficiency.

Tesla, which had been building up an electric sports car that was ready to be marketed for a few years, finally pulled it off.

As Elon Musk explained Back in 2012:

In 2006, our plan was to build an electric sports car followed by an affordable electric sedan, and reduce our dependence on oil…delivering Model S is a key part of that plan and represents Tesla’s transition to a mass-production automaker and the most compelling car company of the 21st century.

Tesla had to find an effective market entry strategy that would enable it to validate the market.

That has worked out quite well!

Tesla has now reached an interesting point in its business model story, where it has become the most important EV player.

While automotive revenue decreased, its revenue decline is largely driven by price reductions, which significantly lowered the average selling price (ASP) despite a +2% YoY increase in deliveries.

Automotive revenue fell (-8% YoY in Q4, -6% YoY for FY 2024), but regulatory credits (+60% YoY) helped soften the impact.

However, Tesla saw strong growth in energy generation (+113% YoY) and services revenue (+31% YoY), which partially offset automotive losses.

The key takeaway?

Price cuts maintained market share but hurt revenue, while energy and services are becoming crucial growth drivers. Expect Tesla to keep refining its pricing and revenue mix going forward.

Yet, the company has reached the point of maximum efficiency, as it reduced its “COGS per Vehicle” to less than $35K.

Yet the key transition happening right now is in robotics, and seld-driving cars are only the start of this transition.

And cars are only the start!

The Third Tesla: From Self-driving Cars to general-purpose Robotics

If you think about it, cars, once they’re relying on self-driving, will be pretty much robots on the road.

That’s how you need to look at Tesla’s robotics business model transition.

Self-driving cars are the “market hook” to transition into something else.

In the meantime, Tesla is pushing an aggressive self-driving schedule, as Tesla made major AI and robotics advancements in 2024, strengthening its computing infrastructure with a 50k H100 cluster and 400% more AI training power.

This fueled FSD V13 (Supervised), trained on 4.2x more data. Safety improved, with FSD covering 5.94M miles between accidents—8.5x safer than the U.S. average.

Tesla plans to launch unsupervised FSD and a robotaxi service in 2025, alongside expanding FSD in Europe and China. Optimus Robotics also saw progress and moved toward pilot production.

The takeaway?

Tesla is pushing AI-driven automation hard, with 2025 set to be a pivotal year.

That will help Tesla build a new core defining the company in the next 20-30 years.

Will it pull it off? This is what the new transition looks like.

Musk’s true long-term ambition is to make “the machine that builds the machine.”

Recap: In This Issue!Tesla’s Shift to a New Business ModelTesla is undergoing a critical transition beyond EVs, evolving into a general-purpose robotics company.Cars are just the starting point—robotics and AI automation will define Tesla’s next 20–30 years.The Concept Behind Tesla’s StrategyThe Power of Transitional Business ModelsCompanies like Tesla start with a transitional business model before reaching their ultimate vision.This approach allows Tesla to:Validate market demand in stages.Refine technology and its ecosystem before full-scale expansion.Finance long-term innovation while maintaining real-world viability.Example: Tesla’s first step was launching a luxury electric sports car (Tesla Roadster) before moving to mass-market EVs (Model S, Model 3, etc.).Non-Linear Competition & Market EvolutionThe tech industry operates on non-linear competition, where market boundaries shift unpredictably.First movers often pioneer markets but don’t always dominate them in the long run.Co-opetition (cooperation + competition) is the norm—tech rivals compete and collaborate simultaneously.The industry follows a winner-take-all cycle, disrupted when a new technological paradigm emerges.Example: Intel dominated semiconductors 30 years ago, but is now struggling to adapt.Tesla’s Business TransitionsFirst Tesla: From Electric Sports Car to Mass-Market EVsTesla started as an electric sports car company (Roadster, 2006) but always aimed for mass-market EVs.2012: Model S launch marked Tesla’s transition into a mass-production automaker.The strategy: Create high-performance luxury EVs first → Use profits & learnings to scale down to affordable EVs.Second Tesla: From EVs to Self-DrivingTesla became the dominant EV player, but recent price cuts hurt revenue despite higher deliveries.2024 Financials:Automotive revenue fell (-8% YoY in Q4, -6% YoY for 2024) due to price reductions.Regulatory credits (+60% YoY) helped soften the impact.Energy generation (+113% YoY) and services revenue (+31% YoY) are becoming crucial growth drivers.Key Takeaway: Tesla is refining its revenue mix, relying more on services and energy.Third Tesla: From Self-Driving Cars to General-Purpose RoboticsTesla doesn’t just see cars as vehicles—once self-driving is perfected, cars become road-based robots.Self-driving is Tesla’s “market hook” to transition into AI-driven robotics & automation.2024 AI & Robotics Advancements:50k H100 cluster & 400% more AI training power.FSD V13 (Supervised) trained on 4.2x more data → 8.5x safer than U.S. average.Tesla plans to launch Unsupervised FSD & Robotaxi service in 2025.Optimus Robotics moved toward pilot production, signaling a broader robotics vision.Key Takeaway: Tesla is aggressively pushing AI-driven automation, with 2025 set to be a pivotal year.What’s Next for Tesla?Tesla is laying the foundation for an AI & robotics-powered future.The next 2–3 years will determine if Tesla successfully transitions beyond cars into robotics and automation.Can Tesla navigate this shift before new competitors emerge?

With massive Gennaro Cuofano, The Business Engineer

This is part of an Enterprise AI series to tackle many of the day-to-day challenges you might face as a professional, executive, founder, or investor in the current AI landscape.

The post Tesla’s Robotics Transition appeared first on FourWeekMBA.

Meta FY2024

[image error]Strong Financial GrowthTotal Revenue: $165.0B (↑ 27% YoY from $130.3B in 2023)Operating Income: $69.4B (↑ 62% YoY from $42.8B in 2023)Net Income: $62.4B (↑ 69% YoY from $36.8B in 2023)Diluted EPS: $23.42 (↑ 69% YoY from $13.86 in 2023)Quarterly Revenue TrendSteady growth in revenue throughout the year, with a strong Q4 performance.Performance by Business SegmentFamily of Apps (FoA):Revenue: $162.3B (↑ 27% YoY)Operating Income: $87.1B (↑ 47% YoY)Reality Labs:Revenue: $2.1B (↑ 47% YoY)Operating Loss: $17.7B (↑ 18% YoY loss)Key TakeawaysMeta saw a significant increase in revenue, profitability, and earnings per share.FoA continues to be the dominant revenue driver, contributing the majority of Meta’s income.Reality Labs revenue grew by 47%, but losses widened to $17.7B, highlighting ongoing investment in the Metaverse and AR/VR.Overall, strong financial performance, with continued expansion in digital advertising and AI-driven growth.

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Building Teams in The AI Age

While many think AI will be just taking over, I argue humans in the loop will matter a lot to smooth out the worse outcomes of an AI-first organization.

Consider that, at least for the current paradigm, as AI has been training on human data, it might show the same psychological traits as us humans (in terms of outcome, not qualia).

Things like optimism, kindness, and companionship. But also stupidity, deception, and malice.

Of course, the way the AI processes these is not in a “human way” but with the same outcome; thus, from the outside, it is as if the AI had these features, simply as a result of generalizing from the given human data.

In short, an AI trained on human data with a human-like architecture (the neural net is far from being a human brain, yet the neuron-like structure of our brains inspires it).

Why would we expect this AI to showcase traits different from what we humans showcase?

Not surprisingly, recent research shows that AI has started showcasing a deception capacity.

Thus, even more now, powerful AI tools in the hands of “stupid humans” can cause organizational damages that can’t be reversed. Thus, my argument here is that in the age of AI, the organizational risk of putting these tools in the hands of the stupid is much higher than in the past.

Therefore, and that’s the key take here, now more than ever, understanding human stupidity not only helps you out in managing it at the human level but also at the human-machine interaction level.

That is why you want to ensure you have “an intelligent system” to filter out, smooth out, and possibly weed out the irreversible effects of stupidity, which starts with understanding its psychological features.

I want to return to a classic on human stupidity, The Basic Laws of Human Stupidity, by Carlo M. Cipolla, and adapt it to build teams in the Age of AI.

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A Quick Intro To The Basic Laws of Human Stupidity

The central premise of The Basic Laws of Human Stupidity by Carlo M. Cipolla is a pamphlet that was supposed to circulate only in a small circle of friends.

Originally written in English by internationally renowned economist Cipolla, this book became a massive success, especially and paradoxically in Italy (Cipolla’s original work was in English).

Then, it became a massive bestseller and was translated into many languages.

The book’s key idea is addressing human stupidity in a non-trivial way, with a few basic laws to understand, the counter-intuitive ways in which stupidity is the most dangerous of the possible negative traits humans might showcase.

Indeed, this is based on five basic laws:

Always and inevitably, each of us underestimates the number of stupid individuals in circulation. The probability that a given person is stupid is independent of any other characteristic possessed by that person. A person is stupid if they cause damage to another person or group of people without experiencing personal gain, or even worse, causing damage to themselves in the process. Non-stupid people always underestimate the harmful potential of stupid people; they constantly forget that at any time, anywhere, and in any circumstance, dealing with or associating themselves with stupid individuals invariably constitutes a costly error. A stupid person is the most dangerous type of person there is.

In short:

The first law emphasizes that no matter how many stupids there are in circulation, we’ll always underestimate their ratio in a group.

The second law is also critical, highlighting how stupidity is independent of anyone’s characteristics. This means that he emphasizes how the ratio of stupidity in a group will be the same, or at times even higher, in what from the outside might seem groups that should come with more intelligent people. He mentions Nobel laureates as a prime example.

The third law is the one I’ll use to derive the matrix. This is the basis to determine the actions of a stupid person, but also the identification of four main types of person that, in a way, have to deal with, contain, filter out, or be badly influenced by the stupid: the helpless, the bandit and the intelligent.

The fourth law points out how often the effect of stupidity comes from the fact that non-stupid people tend to underestimate or not fully understand the effect of stupidity.

The fifth law makes into a corollary that stupidity is the most dangerous of human traits, which can cascade into irreversible collective damages.

The Third Basic Law & The Four Types of People

A person is stupid if they cause damage to another person or group of people without experiencing personal gain, or even worse causing damage to themselves in the process.

As I’ve explained, The Human Cooperation Matrix, derived from one of my favorite books, The Basic Laws of Human Stupidity, is an attempt to help myself weed out and avoid the stupids.

Not forgetting also how to deal with the bandits while, where possible, supporting the “helpless” to see if she/he can turn into the “intelligent” side and focus on working primarily with the “intelligent,” keeping in mind that the stupids and bandits will always be around, trying to mess things up.

Let me explain…

The Stupid-Out Matrix

As per The Basic Laws of Human Stupidity, you will deal with four types of people:

Bandit: engaging in win-lose actions. The bandit wins, and the other person and group of people loses.Helpless: engaging in lose-win actions. The helpless loses, and the other person and group of people win.Intelligent: engaging in win-win actions. The intelligent wins and the other person and group of people also win.And Stupid: engaging in lose-lose actions. The stupid loses, and the other person and group also lose.

As you’ll see, a few practical considerations come from this simple quadrant.

A key premise is as we cut the square with a diagonal, you’ll notice that both the helpless and the bandits have a part of the quadrant leaning toward the intelligent and another toward the stupid.

Therefore, as you’ll see, these two types of people will also play a key role in either filtering out or enhancing the stupid’s role in creating systemic messes.

Indeed, another key point to take into account is both helpless and bandits might be intelligent-leaning (I call it the “trainable helpless” and the “intelligent-leaning bandit”).

As Cipolla explains, stupids’ actions are very hard to predict because, by definition, the stupid doesn’t follow any particular logic. Even on the losing end, the helpless and bandit actions might be more predictable.

Thus, a few anticipations below:

We’ll use this as the basis for the analysis to build a team

The Stupid Quadrant

Provided that as per The Human Laws of Human Stupidity, no matter the domain, you will always have a constant ratio of “stupids” (those engaging in lose-lose actions) you’ll have to manage somehow.

How can we minimize the impact of “stupid” on an organization?

By enabling a narrow focus on the single thing the stupid is good at and ensuring this narrow focus gets channeled into the “intelligent” side of the organization (broader win-win framework).

There is a caveat about the stupid side, but I’ll tackle it in the end.

The Helpless Quadrant

While for the “stupid” you want to contain, narrow the actions to fit into a very specific competence, useful to the organization.

You want to use the opposite approach for the helpless yet competent but with a mindset problem.

Indeed, the main issue with the helpless is if she/he keeps engaging in lose-win actions, that will cause burnout.

The focus is on transforming helpless but competent individuals into intelligent contributors by cultivating a mindset that promotes win-win actions.

This involves helping them understand the broader context of the company so they can see how their actions affect the organization as a whole.

By enhancing this awareness, they can move beyond their narrow focus and act in ways that align with the company’s goals.

However, this approach should only be applied to individuals whose helplessness stems from a fixable mindset issue and who possess high professional competence.

Unlike the “stupid” individuals, they benefit from increased contextual understanding, enabling them to make meaningful contributions.

As I’ll show you, the trainable, helpless, and intelligent-leaning bandit will help filter the stupid.

The Bandit Quadrant

The thing with the “bandit” (those engaging in win-lose actions) is that, in theory, it’s easy to say they must always be weeded out.

In practice, many of these are top performers, high achievers who desire to be the best at their work. In short, they are intelligent-leaning.

And you don’t get many of these people around.

To address the bandits, the key is determining whether their behavior is fixable and whether they are highly competent or even top performers.

Suppose they possess the potential to shift toward the intelligent side of the matrix. In that case, it might be worth engaging with them, as their transformation could bring significant value to the organization.

The challenge with bandits lies in their high individual performance, which can make them valuable despite their misalignment with the organization.

To get the best out of a high-contributing bandit, the focus should be on leveraging their skills while minimizing their potential negative impact on team dynamics and company culture.

One approach is to allow the bandit to work independently, giving them space to perform exceptionally without disrupting the team.

However, the ultimate goal is to channel their success into the organization in a structured way.

This can be achieved by capturing their accomplishments and turning their methods into replicable processes, contributing to team growth and scalability.

By proceduralizing their success, you enable the organization to benefit from their output while gradually integrating their contributions into the company’s broader framework.

Over time, this may also create an opportunity to guide them toward alignment with the intelligent side, fostering both individual and organizational success.

As I’ll show you in the end, the intelligent-leaning bandit, together with the trainable-helpless, will help filter the stupid.

The Intelligent Quadrant

For the intelligent, the focus should be on enabling their growth and ensuring they remain engaged and aligned with the organization’s long-term goals.

This involves addressing three key areas:

Sustaining Growth: The intelligent must have clear opportunities for continuous learning and development. For those with a narrow focus, a structured, linear career growth path is essential to keep them motivated and ensure they feel valued within the organization. For those with broader capabilities and managerial potential, a more flexible career trajectory should be designed, allowing them to take on diverse challenges and responsibilities to maintain engagement and prevent attrition.Protecting Against Burnout: The actions of the stupid, helpless, and bandit must not overburden or frustrate the intelligent. This requires creating a supportive environment where inefficiencies or misaligned behaviors from others are managed effectively, ensuring the intelligent can focus on their strengths without being drained by others.Promoting Collaboration for Organizational Growth: While the intelligent contribute significantly on an individual level, their potential for driving broader organizational success depends on their ability to collaborate and empower others. Encouraging cooperation and a mindset of shared success can help ensure that their impact extends beyond their immediate role or scope. For those with broader managerial potential, developing leadership skills and involving them in cross-functional projects can help embed their contributions into the organization’s culture and processes.

By tailoring career paths to their mindset and potential, which are linear for narrow-focused individuals and flexible for broader-minded ones, the company can ensure that the intelligent remain engaged, thrive long-term, and contribute to sustained organizational growth.

The presence of the intelligent and the ability to create an intelligent system, able to channel the trainable-helpless and the intelligent-leaning bandits, will play a pivotal role as a filtering mechanism to the mess the stupids can create.

The Stupid’s Dilemma

A major caveat on the “stupid” quadrant.

The stupids represent the most challenging group to manage, as they are the furthest from the intelligent quadrant and often have the potential to cause the greatest harm to the organization.

Unlike the helpless or bandits, who can be guided into the intelligent area with relative effort, rehabilitating a stupid is significantly more difficult and resource-intensive.

Their lack of awareness or poor decision-making often leads to actions that fail to contribute positively and can actively disrupt organizational goals, undermine team dynamics, and drain the energy of others, particularly intelligent individuals.

This makes them the hardest group to align with the company’s vision.

Given the difficulty and potential risks, careful consideration is needed before attempting to “rehabilitate” a stupid.

The organization must assess whether the effort is worth investing time and resources, as success is far from guaranteed.

In most cases, minimizing their impact may be more effective through strict oversight, reassignment to roles with limited influence, or, if necessary, transitioning them out of the organization to protect its culture and long-term success.

The organization can focus on driving growth and alignment with the intelligent quadrant by prioritizing efforts on those more east to “rehabilitate” like the helpless or bandits.

While minimizing the risks posed by those in the stupid category, especially to prevent the burnout of the intelligent.

Thus, the amount of damage and the unpredictability of the stupid make it one of the hardest human categories to deal with to prevent systemic damage.

A Filtering Mechanism To Stupidity

Last corollary.

The key to protecting the organization from irreversible damage caused by the stupids lies in leveraging the strengths of those closer to the intelligent quadrant.

The helpless individuals near the intelligent side, the bandits with high contribution potential, and the intelligent individuals must work together to act as a filter, ensuring that the stupid’s actions are contained and their impact minimized.

When the helpless and bandits are closer to the intelligent quadrant, they can be guided to align their actions with organizational goals, making them valuable allies in maintaining a healthy organizational culture.

By collaborating with the intelligent, these individuals help create a strong, cohesive force that prevents the stupids from gaining influence or causing harm.

Together, they can create processes, structures, and accountability measures that act as safeguards against poor decision-making or disruptive behavior.

As more of the organization shifts toward the intelligent quadrant, it creates a filtering effect where the stupids are either rehabilitated (though rarely) or rendered ineffective in their ability to cause damage.

This dynamic ensures the company remains resilient, with its core aligned toward growth, collaboration, and long-term success.

The closer the majority of the organization moves to the intelligent side, the safer the company becomes, as this alignment effectively isolates and neutralizes the negative effects of the stupids.

When the company loses the intelligent side of it, the helpless and bandits quadrants will move toward the stupid side, thus posing a survival threat to the organization.

Putting It All Together: Filtering Out Stupidity, While Channeling Helplessness and Banditism Toward Win-Win Actions, Through An Intelligent System

Thus, as we’ve seen so far, among the four main types of people.

It’s critical that the trainable-helpless and intelligent-leaning bandit work with the intelligent to proceduralize a system that keeps the stupid out or narrows them down to such simple tasks that can’t pose an organizational threat.

While it’s worth investing time in these three categories (trainable-helpless, intelligent-leaning bandit, and intelligent), it’s probably worth minimizing, filtering out the non-trainable helpless and the stupid-leaning bandit and, of course, the stupid.

No matter how much you try, these will team up to amplify stupidity and mess things up irreversibly.

Recap: In This Issue!1. AI and Human TraitsAI as a Reflection of Humanity: Since AI is trained on human-generated data and built on neural architectures inspired by the human brain, it is reasonable to expect it to exhibit human-like psychological traits, both positive (e.g., optimism, kindness, companionship) and negative (e.g., deception, shortsightedness, stupidity).Humans in the Loop: Despite AI advancements, human oversight remains critical to managing and guiding AI effectively, underscoring the importance of understanding human psychology.2. Cipolla’s Laws of Human StupidityThe five laws identify stupidity as the most dangerous human trait, highlighting its counter-intuitive prevalence and systemic risks.Core principles include:Underestimation: We always underestimate the number of stupid individuals.Independence: Stupidity is independent of other traits like education or background.Lose-Lose Actions: Stupid individuals harm others and themselves without gain.Underestimation of Harm: Non-stupid people often fail to grasp the damage stupidity can inflict.Dangerous Consequences: Stupidity is the most dangerous human characteristic due to its ability to cause irreversible harm.3. Human Cooperation MatrixCipolla’s third law is the foundation for a matrix categorizing four types of individuals:Intelligent: Engage in win-win actions (benefiting both themselves and others).Bandit: Engage in win-lose actions (benefiting themselves at the expense of others).Helpless: Engage in lose-win actions (harming themselves to benefit others).Stupid: Engage in lose-lose actions (harming themselves and others).This matrix provides a lens for managing organizational dynamics, particularly in team building.4. Strategies for Managing Quadrants in TeamsThe Stupid Quadrant:Containment: Narrow their focus to specific, harmless tasks to minimize organizational disruption.Caution: Rehabilitation is often resource-intensive and rarely successful.The Helpless Quadrant:Transformation: Focus on training competent but helpless individuals to adopt a win-win mindset.Awareness Building: Help them see the broader organizational context to align their contributions with company goals.The Bandit Quadrant:Strategic Management: Differentiate between “intelligent-leaning” bandits (high performers) and “stupid-leaning” ones.Integration: Allow high-performing bandits to operate independently while proceduralizing their successes for organizational benefit.The Intelligent Quadrant:Growth and Retention: Provide tailored career paths and development opportunities.Burnout Prevention: Protect them from the negative effects of others (e.g., stupidity or inefficiency).Collaboration: Foster teamwork and cross-functional engagement to amplify their impact.5. Minimizing Stupidity’s ImpactBuild a system where intelligent individuals, trainable helpless individuals, and intelligent-leaning bandits collaborate to filter out or neutralize stupidity.Filtering Mechanisms:Use the cooperative efforts of intelligent team members and trainable individuals to isolate and contain stupidity.Proceduralize intelligent actions to strengthen the organization’s resilience.Organizational Survival: Ensure a critical mass of intelligent and aligned team members to prevent the organization from shifting toward a “stupid-dominated” culture.

With massive  Gennaro Cuofano, The Business Engineer

This is part of an Enterprise AI series to tackle many of the day-to-day challenges you might face as a professional, executive, founder, or investor in the current AI landscape.

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The Disruptor Trajectory

The concept of the “Disruptor Trajectory” embodies a familiar yet profoundly transformative pattern within industries where new entrants challenge established incumbents. Coined as “The Slowly, Then Quite Suddenly Takeover Effect,” this framework underscores how disruptors gradually gain ground before overtaking incumbents in a seemingly abrupt shift, reshaping markets and challenging conventional wisdom.

At the heart of this trajectory lies performance dynamics. Incumbent firms, represented by the flat, steady trajectory on the graph, often demonstrate sustained but incremental growth. These organizations typically rely on mature technologies, existing market dominance, and optimized processes to maintain a stable level of performance over time. However, this stability can often lead to complacency, with incumbents focusing on optimizing their existing offerings rather than exploring disruptive innovations.

In contrast, disruptors follow a different path. At first, their performance may lag significantly behind that of incumbents. Early on, disruptors typically cater to niche markets or undervalued customer segments, experimenting with innovative approaches that appear unrefined or impractical to industry leaders. The red trajectory on the graph captures this initially modest performance, where the disruptor’s impact on the broader market seems negligible.

However, as time progresses, the disruptor leverages iterative innovation, cost advantages, and technological breakthroughs to rapidly improve their performance. This acceleration is not linear but exponential, reflecting the compounding effects of innovation. As their solutions gain traction, disruptors transition from serving niche audiences to attracting mainstream customers. This phase is marked by the “crossover point,” where the disruptor’s trajectory surpasses that of the incumbent.

The “crossover point” is pivotal. For incumbents, it signals the moment their longstanding market leadership is at risk. For disruptors, it represents a tipping point where they shift from being challengers to market leaders. The suddenness of this shift is what makes it so disruptive; incumbents, accustomed to their dominance, often fail to recognize or respond to the disruptor’s progress until it’s too late.

Several factors contribute to the success of disruptors in this framework:

Underserved Markets: Disruptors often identify gaps in the market that incumbents overlook, allowing them to establish a foothold and refine their offerings without immediate competition.Technological Innovation: Leveraging new technologies, disruptors create solutions that redefine customer expectations and reduce costs, making them highly competitive.Agility: Unlike incumbents, disruptors are not burdened by legacy systems or bureaucratic inertia, enabling them to adapt quickly to market changes.Customer-Centricity: Disruptors often prioritize solving real customer pain points over maximizing profit, earning loyalty and trust.

The implications of the Disruptor Trajectory are profound. For incumbents, it serves as a warning against complacency. Companies must prioritize continuous innovation, even at the risk of cannibalizing their own products, to stay ahead of disruptors. For disruptors, the framework highlights the importance of persistence, agility, and a clear focus on delivering value.

Ultimately, the “Slowly, Then Quite Suddenly Takeover Effect” underscores a broader truth: in a rapidly evolving world, adaptability and innovation are key to survival. Markets are not static, and those who fail to anticipate or respond to disruption risk being left behind.

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Capital Concentration In AI

I’ve explained in Capital Concentration In The AI Era how capital is flowing into the AI industry based on the various layers of the full AI stack:

To confirm this, Databricks has secured a massive $15.3 billion in financing, comprising $10 billion in Series J equity and $5.25 billion in debt from major financial institutions.

This funding elevates its valuation to $62 billion and brings its total funding to $19 billion.

Notably, Meta joined as a strategic investor, reflecting Databricks’ growing influence in large-scale AI projects, particularly as its platform enables centralized and standardized data for machine learning tasks.

The financing will be directed toward new AI product development, global expansion, and potential acquisitions, solidifying Databricks’ leadership in the AI and data management ecosystem.

CEO Ali Ghodsi also hinted at a possible IPO in 2025, though he stressed prioritizing liquidity for employees.

This move underscores increasing corporate interest in AI technologies, with Meta also investing in other AI-focused companies such as Scale AI.

Databricks’ ability to attract high-profile investors and develop cutting-edge AI solutions positions it as a key player in the industry’s future, particularly as businesses worldwide continue ramping up their AI investments.

Indeed, in the same issue, I’ve also explained how capital is finally flowing in the vertical AI layer:

The last example is ElevenLabs, the AI-driven synthetic voice technology startup that has raised $250 million in a Series C funding round, valuing the company between $3 billion and $3.3 billion.

The round was led by ICONIQ Growth, with participation likely from existing investors such as Andreessen Horowitz, which previously led its $80 million Series B in January 2024.

Founded in 2022 by Mati Staniszewski and Piotr Dabkowski, the company has grown rapidly, driven by its AI-powered tools for voice cloning, dubbing, and speech transformation.

ElevenLabs’ products cater to diverse use cases, from text-to-speech translation and voice cloning to creating entirely new voices, attracting high-profile clients like Washington Post, HarperCollins, and gaming companies.

The company’s API-based platform has seen a sharp rise in adoption, with its annualized recurring revenue (ARR) soaring from $25 million in 2023 to nearly $90 million by late 2024.

Despite facing early controversies over misuse of its technology, ElevenLabs has implemented robust detection tools and safeguards, solidifying its reputation in enabling speech-based AI services.

With this funding, ElevenLabs plans to expand its offerings, scale globally, and address competition from industry giants like Google and OpenAI, while maintaining a moderate valuation multiple of 37x ARR, reflecting tempered investor enthusiasm amid broader market trends.

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Apple’s AI Re-Org To Fix Its AI Strategy

Apple has initiated a leadership shift to address its AI and Siri strategy, assigning veteran executive Kim Vorrath to oversee improvements in these critical areas.

Vorrath, known for her success in delivering major Apple projects like Vision Pro and iPhone software, will collaborate with AI chief John Giannandrea to enhance Siri’s infrastructure and optimize the company’s in-house AI models.

The focus is rebuilding Siri’s foundation and advancing Apple’s AI capabilities to remain competitive in the rapidly evolving virtual assistant and AI landscape.

This leadership change highlights Apple’s urgency to catch up with rivals like Google and Amazon, who have outpaced it in AI-driven virtual assistants and broader AI innovation.

By leveraging Vorrath’s extensive experience, Apple aims to deliver more powerful, intuitive AI solutions that can reshape its position in the market.

A minor detour.

Microsoft vs. Salesforce And The Tipping Point of AI

Apple’s Re-org to fix and tackle AI also opens up a deeper question, which I’ve addressed in The Build vs. Buy Dilemma in Enterprise AI.

Or pretty much, the tipping point, or when the technical isn’t enough, as it requires a cultural re-org of the company to tackle a strategic move!

Many incumbents are undergoing this AI re-org.

Remember to subscribe to premium to get that, as it’ll be an in-depth analysis of all the AI re-org that all the major players are undertaking to tackle the AI challenge!

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