Insights by Fusion42

Ready Player One (2018): Sci-Fi vs Reality

DeReK WaTSoN — Sun, 29 Mar 2026 08:57:46 GMT

Sci-Fi vs Reality Did art imitate life, or did life imitate the inspiration?
Every week I watch a sci-fi film and ask a simple question: where did this idea actually come from? Did fiction imagine the future first — or did reality quietly leak into the story before we noticed?
From there, I do the reality check. What already exists in today’s tech? What’s genuinely caught up with the film? And what still doesn’t — not because no one’s tried, but because something real is in the way. Physics. Economics. Regulation. Human behaviour.
Some ideas turn out to be pointless. Some are just early. Others are waiting on one or two very specific breakthroughs.
The chicken and the egg were never separate. They were always in conversation.

Fiction gave us one OASIS. Reality gave us many, and none of them talk to each other. That’s the entire story of the metaverse, right there.

What They Imagined

In 2018, Ready Player One showed us a bleak 2045 where humanity escapes into a single, seamless virtual universe called the OASIS. It was an infinite digital playground, economy, and social fabric rolled into one. With a simple VR visor and haptic suit, you could be anyone, go anywhere, and own anything. The OASIS solved for societal collapse by offering a better, more exciting digital alternative. It was one world, one login, one identity.

The writers saw it coming: a fully immersive digital layer for life. The key assumption was that it would all be connected.

What We Actually Built

I used to think a unified metaverse was inevitable. Turns out, the economics are fucking brutal. Why would Roblox want you to take the digital goods you bought from them over to Meta’s world? There is zero incentive.

So instead of one OASIS, we built walled gardens. Ready Player One showed a unified world. Reality delivered platform-specific fortresses. Roblox built a colossal one, pulling in $3.6 billion in revenue in 2024 with nearly 83 million people logging in every day. Meta made a multi-hundred-billion-dollar bet on its own version, building the Quest hardware and Horizon Worlds.

Each platform is a powerhouse. Each is a silo. We have good-enough goggles, not a second skin. And the gap between these fortresses is where the dream of a true, open metaverse dies—and where the real opportunity for founders begins.

The Gap They Missed

The film’s biggest blind spot wasn’t the tech; it was the business model. It completely glossed over the raw, competitive drive for proprietary control. Building a single OASIS assumes a level of collaboration and open standards that capitalism just doesn’t favour. Each platform wants to be the platform, not a neighbourhood in someone else’s city.

Here’s the thing: the motivation is also different. We aren’t building the metaverse to escape a dystopian future; we’re building it to augment our slightly lonely, inefficient present. It’s about connection and utility, not desperation. The pattern I keep seeing is founders assuming people want pure escapism, when what they really want are better tools for reality. That’s a fundamental disconnect between the fiction and the market need.

The Players

The giants built the islands. The startups are building the bridges. They aren’t trying to create another OASIS; they’re creating the protocols and tools to connect the ones we already have. This isn’t a Silicon Valley story, either. The work is happening everywhere.

Decentraland (Buenos Aires, Argentina): One of the original Web3 worlds where users own the land via NFTs. It’s a live experiment in creating the kind of user-owned, interoperable world the film imagined, proving the concept beyond a single corporate entity.
The Sandbox (Hong Kong): A major player in the user-generated content space, backed by $93M and big partnerships. Their entire model is based on interoperable NFT assets, directly tackling the walled garden problem by design.
Somnium Space (Prague, Czech Republic): A VR-first metaverse that’s deeply integrated with blockchain, allowing for true asset ownership and cross-chain compatibility. They’re building for the immersive future the film promised, but with an open economic layer.
Aethir (Singapore): You can’t have a seamless metaverse without massive rendering power. Aethir is building a decentralized GPU cloud to provide the infrastructure needed for high-fidelity, interoperable experiences, solving a huge technical bottleneck.
Highstreet (Los Angeles, USA): A hybrid of online shopping and gaming, creating a marketplace for digital goods that can “world-hop.” They are explicitly building for asset interoperability, allowing brands to exist across fragmented virtual worlds.
Wilder World (London, UK): Aiming for a photorealistic, AAA-quality metaverse on the blockchain. Their focus on universal NFT standards is a direct attempt to create the ‘take it with you’ asset portability that was effortless in the OASIS.
Beam (Ho Chi Minh City, Vietnam): Instead of building another destination, Beam is building the engine. It’s a modular metaverse framework with interoperability baked in, giving developers the tools to create connected worlds from the start.
Voxels (Sydney, Australia): An early, browser-based virtual world that has already formed interoperability pacts with platforms like Decentraland. They are proof that collaboration, not just competition, is a viable path forward.
Everyworld (Tel Aviv, Israel): Using AI to help users generate interoperable 3D assets and content. This tackles the scalability problem of content creation while ensuring the assets aren’t locked into a single platform from day one.
Ixana (Cape Town, South Africa): Pure-play middleware. Ixana is building the technical plumbing—the protocols and asset bridges—that allows different metaverse platforms to communicate. They’re not building a world; they’re building the universal translator for them.

What’s Left to Build

We have startups building bridges and protocols. But the gap they missed is still the most personal one: you.

In Ready Player One, your avatar, your inventory, and your reputation were singular and persistent. We don’t have that. Your identity is fractured across a dozen platforms. The startup that still needs to exist isn’t another world, it’s a universal passport. A truly portable, user-owned digital identity that plugs into any game or virtual space.

This isn’t just an avatar. It’s a wallet, a social graph, and a reputation score that you own and control. It’s the thing that proves you are you, wherever you go in the digital realm. It’s less about building another world and more about building the passport that lets you travel between them.

The Timing Signal

A generation is growing up inside Roblox and Fortnite; for them, this isn’t a strange new world, it’s just... the world. Their expectation of digital presence and ownership is native. That’s a massive behavioural shift.

Technically, the pieces are falling into place. The launch of devices like Apple’s Vision Pro validates the market for spatial computing, pushing the hardware and developer tools forward. On-device AI is becoming powerful enough to handle real-time rendering and interaction. And open standards like OpenXR are slowly gaining ground, creating a common language for developers. The incentive to connect the islands is finally starting to outweigh the desire to own the ocean.

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Domain Histories: Privacy & Identity

DeReK WaTSoN — Sat, 28 Mar 2026 16:10:10 GMT

This is Domain Histories — a weekly series mapping startup domains from first principles. The best founders are students of history. Patrick Collison met Visa's founder before building Stripe. Apoorva Mehta studied Webvan's collapse before building Instacart. They used history to make specific decisions about what to build, what to avoid, and when to move. Each issue takes one domain and gives you the full map: who tried before, why they failed, where the money sits, what's shipping now, and what a founder entering today should do differently because of it. Not a market overview. Not a research report. The history that changes what you build.

You have 200 passwords. The industry that created that problem wants to sell you the solution.

Here’s the thing. The chaos of digital identity — the endless logins, the data breaches, the feeling that you’ve been copied and pasted across a thousand databases you’ll never find — isn’t the result of some grand, malicious plan.

The original sin of digital identity was a lack of one.

The Original Problem

The internet wasn’t built for people. It was built for machines to talk to each other. Identity was an afterthought, a patch. When services needed to know who you were, they did the simplest thing possible: they asked you to create a username and a password.

Every. Single. Time.

Each new service built its own little castle with its own little gate. Your bank, your email, your social network, your pizza delivery app. Hundreds of castles, hundreds of keys. This created two massive problems that define the market to this day.

First, a usability nightmare. You, the user, were forced to become a human keychain, juggling hundreds of credentials. Or, more likely, you just used the same password everywhere, turning one leaky castle into a catastrophic failure of the entire kingdom.

Second, a massive, fragmented attack surface. Your identity wasn’t a single thing you owned; it was a thousand little pieces of you, scattered across servers you had no control over. A goldmine for hackers and a business model for the data brokers who figured out how to stitch it all back together and sell it.

The Numbers

The Timeline

The history of identity is a story of bolting on solutions, with each new layer creating new problems. It unfolds in three acts.

Era 1: The Gatekeepers (Pre-2016)

Before 2016, identity was a free-for-all. Every company rolled their own login system. The dominant players weren’t tech companies; they were the old-guard credit bureaus — TransUnion, Experian, Equifax. They quietly built empires by aggregating offline data and selling it back to businesses as a proxy for trust. Their business was, and still is, data arbitrage. They profited from the fragmentation because they were the only ones with a semi-complete picture.

Startups in this era mostly tinkered with Single Sign-On (SSO), trying to create a master key for all your digital locks. But adoption was slow. The problem wasn’t seen as urgent enough to solve.

Why it ended: A European regulation lit a fire under the entire industry.
What it means for you today: The DNA of this era persists. The incumbents still hold the most data, and the default behaviour for any new app is still to build its own siloed identity system.

Era 2: The Compliance Gold Rush (2016-2024)

The General Data Protection Regulation (GDPR) changed the game. Suddenly, mishandling user data wasn’t just bad practice; it was a business-ending liability with massive fines.

Fear became the product.

This shift created companies like OneTrust. Founded in 2016, it was perfectly timed. It didn’t sell a better user experience. It sold certainty to corporate lawyers. It was a compliance engine, a suite of tools to manage consent, map data flows, and generate reports to keep regulators happy. It worked. OneTrust grew into a multi-billion dollar company by focusing entirely on the enterprise’s pain, not the user’s.

The playbook was simple: find a regulatory threat and sell the antidote. The low-hanging fruit of compliance has been picked. Building another cookie-banner tool is a death wish.

Why it ended: Compliance became table stakes, and a new technological wave — AI — introduced a level of complexity that basic compliance tools couldn’t handle.
What it means for you today: Regulation is the most powerful catalyst in this market. If you’re not tracking global data and AI policy, you’re flying blind.

Era 3: The AI Reckoning & Consolidation (2025-Now)

I used to think AI would disrupt the old data gatekeepers. Turns out, it’s their new favourite weapon. Incumbents like TransUnion and Experian are aggressively rebranding as “AI-driven powerhouses,” using machine learning to “weaponize proprietary data.” Their revenue is growing, not shrinking. They’re using AI to strengthen their grip, offering more sophisticated fraud detection and credit scoring than ever before.

This forces the market to evolve. The problem is no longer just privacy compliance; it’s AI governance. How do you prove your AI models aren’t biased? How do you manage the data they’re trained on?

Simultaneously, the market is consolidating. Fast. The cybersecurity space saw a record $96 billion in M&A in 2025. Standalone privacy and identity features are being absorbed into bigger platforms. Securiti AI, a data governance startup, was acquired by infrastructure giant Veeam for $1.73 billion in 2025. It became a feature.

What it means for you today: Competing with incumbents on data is a losing battle. Your advantage has to be in building integrated, trustworthy infrastructure that they can’t — because their business models and legacy tech are too siloed.

The Graveyard

Failure in this space often looks like a successful acquisition. Standalone companies get absorbed into larger platforms, becoming a feature, not a business. The lesson isn’t that they failed, but that building a venture-scale, independent identity company is brutally hard.

Where The Money Sits

The money sits with the gatekeepers: TransUnion, Experian, Equifax. They hold the foundational datasets on billions of people. They profit from complexity, selling access, verification, and analytics back to every other business. Their moat is a labyrinth of proprietary data, regulatory capture, and enterprise sales teams that have been in place for decades.

They are not just data brokers anymore. They are AI companies. TransUnion’s revenue grew 13% in 2025. They are successfully layering AI on top of their core asset — data — to create new, high-margin products.

The tax everyone pays is the “trust tax.” Every time a business needs to verify a user, they pay one of these giants. Every time a new regulation appears, consultants and software vendors get paid to help navigate the complexity that the incumbents thrive on.

Their blind spot? They are slow, siloed, and their products are built for their own benefit, not the user’s. They are trying to bolt on privacy and modern identity, but their core business is data exploitation. That contradiction is the opportunity.

What’s Actually Hard

The tech is not the hardest part. Zero-knowledge proofs, decentralized identifiers, biometrics — the building blocks are there.

The hard part is the sales cycle.

You are not selling software. You are selling trust. You are selling a promise that your system is more secure, more compliant, and more reliable than the status quo. Your buyer isn’t a product manager looking for a cool feature. Your buyer is a Chief Information Security Officer or a General Counsel.

Their first question isn’t “How does it work?”. It’s “Who do we sue if this goes wrong?”.

You are asking enormous, risk-averse organisations to rip out a core piece of their infrastructure — how they know who their customers are — and replace it with yours. This involves navigating procurement hell, legal reviews, and security audits that can take 18 months or more. You don’t win with a better demo. You win with better liability terms, better compliance certifications, and a story about risk reduction that the board can understand.

What’s Shipping Now

The most interesting work today is happening at the infrastructure layer, with founders who have learned the lessons of the past. They’re not building another social network with a new login. They’re building the pipes.

Killing the Password: Startups are finally tackling the original sin. Beyond Identity is pushing passwordless authentication using device-bound cryptographic keys, making phishing nearly impossible. 1Kosmos combines biometrics with a private blockchain to prove identity without storing centralised credentials.
Building Reusable Identity: The dream of a single, portable identity is getting closer. ID.me has carved out a huge niche with government agencies, offering a reusable login for accessing public services. On the decentralized front, Civic and SelfKey are using blockchain to build user-controlled identity systems.
Delivering Dev Tools: The smartest founders know that developers choose the next generation of infrastructure. uPort, now evolved into projects like Veramo, provides open-source tools for building with decentralized identity. In Web3, Privy is giving developers simple APIs to embed wallets and authentication into their apps, abstracting away the complexity.

Where This Goes

The noise and fragmentation are signs of an underlying convergence. Privacy, identity, and AI governance are not three separate markets. They are becoming one single problem: how do you establish and manage trust in a digital world?

The next 36 months will be a race to build the integrated “trust infrastructure” that solves this.

The bet is that a startup, unburdened by legacy tech and siloed business units, can build this unified platform faster and better than the incumbents who are trying to bolt these pieces together. The winner won’t just offer passwordless login, or a consent dashboard, or an AI governance report. They will offer a single platform that manages the entire lifecycle of digital trust for an enterprise.

This is an infrastructure play. The exits will look less like consumer apps and more like the $96 billion in cybersecurity M&A we saw in 2025 — strategic acquisitions by major cloud and security players.

Moves Worth Stealing

Ride the Regulatory Wave (The OneTrust Play): Don’t wait for demand to appear. Find the next GDPR-level event — a new AI liability law, a federal privacy bill — and build the tool that makes compliance automatic. Sell certainty to lawyers.
Become Essential Infrastructure (The Securiti AI Play): Don’t try to be everything. Build one critical component of the trust stack so well that a larger platform like Veeam, Microsoft, or Salesforce has no choice but to acquire you. Be the definitive solution for one piece of the puzzle.
Solve the Original Pain (The Beyond Identity Play): Go back to first principles. The password is a 60-year-old hack. Find the root of the problem that everyone else is just managing and build the thing that eliminates it entirely.

The Why Now

Three forces are creating a brief window of opportunity.

AI Creates New Urgency: The explosion of AI makes data governance and identity verification a C-suite priority. The old tools aren’t equipped to manage AI models, training data, and the new attack vectors they create.
Regulation is Expanding: The compliance wave that started with GDPR is now global and expanding to include AI. This creates new, mandatory budget items inside every large company.
The Incumbents are Buying: The record-breaking M&A in cybersecurity shows that the big players are acquiring innovation, not building it. This provides a clear path to exit for startups that can build something valuable.

The convergence of these forces means that for the first time, there is both the budget and the urgency to finally fix the internet’s broken identity layer.

If You’re Starting Monday

The Wedge: Don’t build a generic identity platform. Pick a single, painful, high-stakes verification problem in one regulated industry. Example: proving the identity of participants in decentralised clinical trials for a pharma company.
The Trap: Building a consumer-first “digital wallet” for identity. You will burn millions trying to solve a two-sided market problem and get crushed by platform players. Sell to the enterprise. They have the problem and the money.
The Customer: The Chief Information Security Officer (CISO) or the Chief Compliance Officer. The person who gets fired when a data breach or an AI audit goes wrong.
The First Hire: Your first hire should not be a growth hacker. Your first hire should be a lawyer who has spent years advising companies on data privacy and AI ethics. Your product is not just code; it’s a manifestation of legal and ethical rules.

Go Deeper

Momentum Cyber 2025 Report: Analysis of the record $96B in cybersecurity M&A. (Link)
The Information on OneTrust: The inside story of the compliance gold rush and market consolidation. (Link)
TransUnion 2025 Earnings: How incumbents are using AI to drive growth. (Link)
Rothschild & Co on AI Investment: Data on the 2025 venture capital boom driven by AI. (Link)
Contrary Research on OneTrust: A breakdown of the platform serving 75% of the Fortune 100. (Link)

For the ❤️ of startups

Arthur is the AI native startup operating system I’m building in public — not hype, but a system that turns input into structured execution and tracks founder progress. If you want to follow or use it, it’s open for early access.
→ Access Arthur

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The F42 AI Brief #064. Stop Firing Your Support Team.

DeReK WaTSoN — Wed, 25 Mar 2026 14:06:00 GMT

If you’re pouring budget into AI to replace your entire customer support team, you might want to pause. Booking.com just went on record saying its human support is the key thing that sets it apart, and they’re not wrong.

⚡ The Break

Booking.com made it clear this week: while they use AI to understand what a customer needs, their actual differentiator is having a human resolve the problem. They’re a multi-billion dollar tech company, and they’re doubling down on people.

Most founders assume the endgame for customer service is zero humans. It’s all about ticket deflection and cutting costs. The north star metric becomes how many customers you can prevent from ever speaking to a person.

Turns out, for high-stakes interactions, that’s a fast-track to losing customers. Here’s what this actually means: your customers don’t want to fight a chatbot when their flight is cancelled or their hotel room doesn’t exist. They want a solution. Fast. AI is brilliant at diagnosing the issue from a frantic email, but it’s often shit at handling the nuance and emotion required to fix it. People aren’t stupid; they know when they’re being managed by a script.

I’ve seen good projects hit the wall over this. They optimise for bot resolution rates, and watch their customer satisfaction scores plummet. The real play isn’t replacement, it’s augmentation. Use AI to give your human team superpowers. When an agent picks up a call, the AI should have already summarised the issue, pulled up the customer’s history, and suggested three possible solutions. The goal isn’t to avoid a conversation; it’s to make that conversation brutally efficient and successful.

So, look at your CX roadmap. If it’s a straight line to firing all your support agents, you’re building a frustration engine, not a service. Start measuring your team on first-contact resolution and customer happiness. That’s the data that matters.

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🧠 What Quietly Changed

Yann LeCun’s attack on AI’s ‘common sense’ problem — While everyone is distracted by the latest video generator, Yann LeCun’s team dropped research on a thing called LeWorldModel. It’s a direct shot at one of AI’s biggest hurdles: learning how the world works just by watching, like a baby does. The research targets “JEPA collapse” in pixel-based models, which is a technical way of saying it’s trying to stop the AI from getting lazy and ignoring the details when predicting the future of a scene. This is the foundational work required for agents that can actually plan and reason in the real world, not just generate plausible-looking clips.

OpenAI’s $1B ‘goodwill’ fund — The OpenAI Foundation announced a $1 billion grant programme to ensure AI “benefits all of humanity.” This isn’t just a press release about ethics. For a founder, this is a giant flare telling you where the wind is blowing. Building with a clear, defensible social benefit is moving from a nice-to-have to a core requirement for survival. This kind of money doesn’t just fund non-profits; it shapes the entire narrative, pre-empts regulators, and sets the bar for what is considered an acceptable use of powerful technology. If your pitch deck doesn’t have a slide on this, you’re already behind.

The legal storm brewing for AI agents — We’re seeing more research into “agentic AI” and its geopolitical impact, but the quiet part is the immediate legal risk for builders. The moment your AI agent can act on a user’s behalf—making a purchase, sending an email, trading a stock—it can also break the law. When an autonomous agent executes a trade that violates international sanctions, who is liable? You? The user? The company that owns the platform? Most legal frameworks are completely unprepared for this. If you’re building agents, you need to be talking to lawyers now about liability, kill-switches, and how you log an agent’s ‘intent’.

🧪 The Edge Case

There’s a growing chorus of people confidently predicting an AI-driven dystopia. They speak with absolute certainty about how AI will upend society, destroy industries, and change the very nature of humanity.

The reality is, making investment decisions or building a company based on these arrogant predictions is a massive mistake. It presumes the future is a solved problem and that we are all just passengers. It ignores the simple fact that huge, complex systems—like global society—don’t just roll over for new technology. People adapt. They regulate. They find new ways to create value. The breathless predictions of flying cars in the 70s or the paperless office in the 90s should have taught us some humility.

Here’s the thing smart people are missing: they mistake a powerful tool for a deterministic force. They get so wrapped up in the technology’s potential that they forget about human behaviour, economics, and politics.

The builder’s lesson isn’t to ignore AI. It’s to stay grounded. Don’t build for a science fiction novel; build for a real customer with a real problem today. The most resilient startups will be those that use AI to solve a concrete need better than anyone else, while remaining agile enough to navigate the changes as they come. Betting the farm on a single, extreme vision of the future—whether a utopia or a dystopia—is just a dressed-up form of gambling. Solve today’s problems, and you’ll earn the right to solve tomorrow’s.

🎯 This Week’s Bet

BUILD: An AI-powered co-pilot for your customer support team. It should listen to calls and read chats in real-time to instantly surface the exact knowledge base article, previous ticket, or account detail the human agent needs to solve the problem. The goal is not to replace the human, but to make them impossibly fast and accurate.

BAIL: Chasing 100% automated customer support resolution. Set a rule: if a customer asks for a human twice in a row, the bot escalates immediately. The brand damage from trapping a frustrated customer in a chatbot loop is worth far more than the salary you think you’re saving.

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Know Your Models

DeReK WaTSoN — Mon, 23 Mar 2026 08:37:12 GMT

The rules changed. Here’s the new playbook. 30 principles for building startups in an AI world — what worked before, what broke, and what founders need to do now. Not theory. Tactics that ship.
New principle every Monday

Know Your Models

Choosing the wrong AI model is now a product and cost architecture mistake. Founders need to match model capability to task rather than defaulting to the most powerful or cheapest option. You were told that deep technical expertise in your stack was non-negotiable. Turns out, the stack is now a menu, and your most important technical skill is knowing what to order.

Why This Mattered Before

The conventional wisdom was that a technical founder must deeply understand their core technology. And it was right. When you built everything from scratch, from the database to the front-end framework, that deep knowledge was the difference between shipping and stalling. Knowing the internals of your stack was how you optimised performance, debugged esoteric issues, and ultimately, built a defensible product.

Your unique advantage was in how you put the bricks together because you knew the properties of every single brick. This was the gospel of YC, the definition of a “great hacker,” and for a decade, it was the only way to win.

The Graveyard

I haven’t seen a single, high-profile startup death with “built their own model” listed as the official cause on the tombstone. The reality is more subtle and far more common. In the cohorts I’ve worked with, I’ve watched brilliant teams burn their first £500k and nine months of runway trying to build a bespoke NLP model for something like sentiment analysis or document summarisation.

They’d be heads down, trying to eke out a 2% accuracy gain, convinced this was their moat. Then a new foundation model from Anthropic or Google would drop, delivering better performance out of the box through a simple API call. The startup’s “moat” became a millstone overnight — expensive to maintain, slow to improve, and fundamentally uncompetitive. They didn’t die in a blaze of glory; they just became irrelevant.

What AI Actually Changed

The foundational technical competence has shifted from building the engine to knowing how to drive. You no longer need to know the internals of PostgreSQL. You need to know that Claude 3.5 Sonnet is faster and cheaper for enterprise-grade text analysis, while GPT-4o might be better for complex, multi-step agentic workflows. This is the new literacy.

The game is no longer about building your own model from scratch. The capital required is staggering. Cohere raised $500 million in 2025 to focus on enterprise models. xAI is backed by billions. You cannot compete with that. What they’ve done is commoditise raw capability. Your job is no longer to create the capability, but to intelligently apply it. The core engineering challenge has moved up the stack from model creation to model orchestration.

The New Playbook

Audit tasks, not your tech. Map every core task your product performs to the required AI capability. Does this task need world-class reasoning, or just fast, accurate classification? Does it need creative generation or factual extraction? Don’t use a sledgehammer to crack a nut. Use a small, fast model for simple jobs and save the expensive, powerful models for the tasks that truly need them.
Build a model “palette”. Don’t get locked into a single provider. Your architecture should allow you to route tasks to different models based on performance and cost. Set up a simple evaluation framework to continuously benchmark models like those from OpenAI, Anthropic, Google, and open-source alternatives on Hugging Face. A simple API call change could cut your COGS in half or double your performance overnight.
Treat cost-per-token as a core business metric. Your model choice directly impacts your cost of goods sold, and therefore your pricing and margin. A model that’s 98% as good but 80% cheaper might be the right business decision, even if it’s not the absolute peak of technical performance. This is no longer just an engineering choice; it’s a fundamental product and finance decision.
Hire for AI literacy, not ML research. Your first key hire isn’t a PhD who wants to build a model from the ground up. It’s a pragmatic engineer who lives on the leaderboards, understands the trade-offs between different models, and can quickly integrate and fine-tune them. They’re an architect and a plumber, not a research scientist.

The Warnings

First, avoid the “Sledgehammer Fallacy.” I see founders using the most powerful model on the market for every single API call, from parsing a simple user query to generating a button colour. This burns cash and introduces unnecessary latency. It’s lazy architecture, and it will kill your margins.

Second, beware of “Model Monogamy.” Committing your entire product to a single AI provider is a huge strategic risk. When they change their pricing, deprecate a model you rely on, or suffer a major outage, your business is at their mercy. Diversify your model dependencies just like you would any other critical part of your supply chain.

The Bottom Line

Your first AI hire isn’t a researcher. It’s an architect who knows which model to use, when, and precisely what it costs.

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Part of Startup Principles for an AI World — 30 principles for building in the new era. New issue every week.

Enjoy and hope to see you soon.
❤️ & ✌🏼

Derek

Please let me know what you think of it, love a feedback loop 🙏🏼

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Minority Report (2002): Sci-Fi vs Reality

DeReK WaTSoN — Sun, 22 Mar 2026 21:18:09 GMT

Sci-Fi vs Reality Did art imitate life, or did life imitate the inspiration?
Every week I watch a sci-fi film and ask a simple question: where did this idea actually come from? Did fiction imagine the future first — or did reality quietly leak into the story before we noticed?
From there, I do the reality check. What already exists in today’s tech? What’s genuinely caught up with the film? And what still doesn’t — not because no one’s tried, but because something real is in the way. Physics. Economics. Regulation. Human behaviour.
Some ideas turn out to be pointless. Some are just early. Others are waiting on one or two very specific breakthroughs.
The chicken and the egg were never separate. They were always in conversation.

Gesture interfaces looked cool in 2002. Twenty years later, we still use mice. Here’s the thing: while Hollywood sold us a vision of effortless, dance-like control, reality delivered something much clunkier. The gap between the two is where the real work—and the real opportunity—begins.

What They Imagined

In the world of 2054, John Anderton didn’t just look at data; he stepped inside it. Using gloved hands on a giant transparent screen, he swiped, pinched, and threw information around to stop crimes before they happened. He wasn’t clicking or typing; he was conducting data.

It was a beautiful, high-bandwidth solution to information overload that made the mouse and keyboard look like a chisel and stone. The writers saw a future where our physical movements could directly manipulate a world of abstract information.

What We Actually Built

We didn’t get the giant, public screens. We got personal ones, strapped to our faces.

Apple’s Vision Pro, which landed in 2024, brought a version of this to life. It uses subtle eye-tracking and finger-pinches to control apps floating in your living room. It’s an intimate, small-scale version of Anderton’s interface, focused on personal productivity and entertainment, not public crime-fighting.

Meanwhile, Magic Leap has been on a long, hard journey since 2010. After raising billions and generating massive hype, they pivoted their AR platform toward the enterprise market. Their story is a lesson in how difficult it is to build this tech from scratch and find a market that will actually pay for it.

Meta is taking a different path, prototyping neural wristbands that read muscle signals in your arm. The idea is to control devices with intention, not big, sweeping gestures. Minority Report showed us the what. Apple, Magic Leap, and Meta are all grappling with the how.

The Gap They Missed

The writers got the vision right, but they skipped over the messy, human details. Turns out, holding your arms up like an orchestra conductor for eight hours a day is fucking exhausting. It’s called ‘gorilla arm,’ and it’s a well-known problem in UI design. Physics is a stubborn thing.

The film also ignored cognitive load. Managing files in 2D is hard enough; navigating a 3D spatial environment requires a whole new mental model that most people haven’t developed yet.

But the biggest gap they missed is fragmentation. In the film, the gesture interface was a universal standard. In reality, we have a collection of walled gardens. An app built for VisionOS doesn’t work on Meta’s Horizon OS. This lack of interoperability is the single biggest brake on the spatial computing revolution. Founders who ignore this do so at their peril.

The Players

What’s Left to Build

The real opportunity isn’t just another headset. It’s solving the problems the big players are too busy—or too strategic—to fix. We’re still missing the fundamental tools and infrastructure.

The company that builds the cross-platform ‘Figma for spatial computing’ or the ‘Unity for enterprise AR’ will own a critical piece of the stack. Right now, developers have to choose a tribe. The startup that creates the agnostic, interoperable layer—letting creators build an experience once and deploy it across Vision Pro, Quest, and whatever comes next—wins.

The wedge is to stop focusing on the destination (the perfect AR experience) and start building the roads and bridges. Solve the workflow problem for the thousands of developers who don’t want to be locked into a single ecosystem.

The Timing Signal

So, why now? Two things have changed.

First, Apple’s entry with Vision Pro validated the space and, more importantly, started training consumer behaviour. People are finally learning the grammar of spatial interaction—the pinches, the glances, the gestures. Apple is spending billions to create the market for you.

Second, the incumbents are committing capital at a scale that signals a point of no return. Meta is borrowing billions specifically for AI to power its future platforms. The foundational tech is maturing, and the giants are placing their bets. The gap between fiction and reality is where startups live, and that gap is finally funded.

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Domain Histories: Data Infrastructure & Analytics

DeReK WaTSoN — Sat, 21 Mar 2026 07:47:53 GMT

This is Domain Histories — a weekly series mapping startup domains from first principles. The best founders are students of history. Patrick Collison met Visa's founder before building Stripe. Apoorva Mehta studied Webvan's collapse before building Instacart. They used history to make specific decisions about what to build, what to avoid, and when to move. Each issue takes one domain and gives you the full map: who tried before, why they failed, where the money sits, what's shipping now, and what a founder entering today should do differently because of it. Not a market overview. Not a research report. The history that changes what you build.

Every company says they’re data-driven. Almost none can query last week’s data.

The promise was simple: get all your data in one place, ask any question, get an instant answer. The reality is a tangled mess of pipelines, broken dashboards, and six-figure bills for a warehouse that mostly stores junk. For decades, we’ve been fighting the same war with different weapons.

Here’s the thing: the problem isn’t the technology. It’s our understanding of it. We keep buying the next magic box, hoping it will finally make sense of the chaos. It won’t.

But if you understand the history — the sequence of problems and the ghosts of solutions past — you can see where the real opportunity is. It’s not in building a better warehouse. It’s in what happens next.

The Original Problem

Before the cloud, data lived in a basement.

It sat in hugely expensive, on-premise servers from companies like Oracle or Teradata. These were monoliths. Slow, rigid, and eye-wateringly expensive. Getting data in was a project. Getting it out was a nightmare.

Want to know which customers are likely to churn? You’d file a ticket with the Business Intelligence (BI) team. Weeks later, if you were lucky, you’d get a static PDF. The data was already out of date. The business had already moved on.

This created a priesthood of data gatekeepers. They held the keys to the kingdom, and the rest of the company had to beg for scraps. The original problem wasn’t a lack of data. It was a lack of access.

The Numbers

These numbers aren’t just figures on a balance sheet; they represent fundamental shifts in how value is created and captured in the data ecosystem. From the sheer scale of cloud infrastructure to private market bets on architectural choices, they show the stage on which the drama of data infrastructure unfolds. This financial backdrop created pressure, making legacy systems untenable and paving the way for the unbundling and rebundling that defines our timeline.

The Timeline

The story of data infrastructure is a story of unbundling and rebundling. We break apart the monolith, perfect the pieces, then glue them back together in a new shape.

Era 1: The On-Prem Monoliths (Pre-2012)

What Enabled It: The simple need for businesses to report on their operations amidst complex, internal data.
The Players: Oracle, Teradata, IBM. The heavyweights of enterprise IT.
The Playbook: Sell a massive, integrated hardware and software package for millions of dollars. Lock customers into multi-year contracts for support and maintenance. The business model was based on friction and control.
Why It Ended: The cloud. AWS made storage and compute cheap, elastic, and accessible. Why buy a million-dollar box for your basement when you could rent a supercomputer by the hour?
For Founders Today: Every founder in this space is, in some way, fighting the ghost of the on-prem monolith. Your product must bet against rigidity.

Era 2: The Cloud Data Warehouse (2012-2020)

What Enabled It: Cheap cloud storage (AWS S3) and compute (EC2).
The Players: Snowflake, AWS Redshift, Google BigQuery.
The Playbook: Separate storage from compute. This was the killer insight. You could store petabytes of data for pennies and only pay for compute when you were actively running a query. It destroyed the on-prem economic model. Suddenly, data warehousing was accessible to startups, not just Fortune 500s.
Why It Ended (or Evolved): The warehouse worked. Too well. Companies dumped everything they had into it. The new problem wasn’t storing the data; it was making sense of it. The warehouse became a data swamp, creating a new set of problems: getting data in (ETL), cleaning it (Transformation), and getting it out to where people actually work (Reverse ETL).
For Founders Today: The central warehouse is now a given. Don’t try to replace it. Bet on connecting with the data warehouse.

Era 3: The Modern Data Stack & The Lakehouse Wars (2020-2024)

What Enabled It: The success of the cloud data warehouse created a need for a surrounding ecosystem of tools.
The Players: Snowflake (representing the Data Warehouse) vs. Databricks (championing the Data Lakehouse).
The Playbook: Unbundle the stack. A best-of-breed tool emerged for each job: one for ingestion (ETL), one for transformation, one for reverse ETL, one for observability. The stack was modular but complex. This era was defined by the great architectural debate:
- Data Warehouse (Snowflake): Perfect for structured, analytical data. Think SQL queries and BI dashboards.
- Data Lakehouse (Databricks): A hybrid approach designed to handle both structured data and the unstructured stuff needed for machine learning (images, text, logs).
Why It Ended (or Evolved): Generative AI crashed the party. Suddenly, the “unstructured stuff” wasn’t a niche problem for data scientists anymore. It was the fuel for the next generation of products. The Lakehouse argument got a massive boost.
For Founders Today: The debate is settling. Bet on handling diverse data types.

Era 4: The AI Data Layer (2025-Now)

What Enabled It: The Cambrian explosion of large language models (LLMs) and AI applications.
The Players: Snowflake, Databricks, and the hyperscalers (AWS, Azure, Google).
The Playbook: Your data platform is now your AI platform. It’s no longer just for analysts running queries; it’s for applications fetching data to feed to a model. The new features have names like Cortex AI (Snowflake) and are designed to let companies build AI-powered applications directly on top of their existing data. Microsoft’s Azure growth is being turbo-charged by serving AI inference. The game has changed from passive analytics to active intelligence.
For Founders Today: This is the new frontier. Data isn’t just for looking at in a dashboard. It’s an ingredient for building intelligent products. The entire value proposition of the data stack has been elevated. Bet on data platforms becoming AI platforms.

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The Graveyard

The research for this space doesn’t show a tidy list of high-profile flameouts. Why? Because in infrastructure, you don’t typically explode. You just fade away. You get absorbed, open-sourced, or squeezed into a niche until you become irrelevant.

The companies that die here don’t fail because their tech is bad. They fail because of a few repeating patterns:

The “Single Pane of Glass” Fallacy: They try to build one platform to do everything — ingestion, transformation, warehousing, visualisation, and AI. They end up building a mediocre version of five different things and get crushed by best-of-breed point solutions.
Getting Squeezed by the Platform: They build a useful feature on top of Snowflake or Databricks. It works. Then, Snowflake or Databricks builds a “good enough” version of that feature natively and gives it away for free. Game over.
Ignoring the Workflow: They build a beautiful tool that requires users to leave the place where they actually work. A salesperson lives in Salesforce. A marketer lives in Marketo. If your tool requires them to open another tab, you’ve already lost.

The lesson is brutal: don’t compete with the giants on their turf. And don’t try to boil the ocean. Find one, painful, specific problem and solve it ten times better than anyone else.

Where The Money Sits

Follow the CAPEX.

The entire data world is built on the foundations laid by three companies: Amazon (AWS), Microsoft (Azure), and Google (GCP). They own the servers, the storage, and the network. They are the landlords. Everyone else in the data ecosystem, including Snowflake and Databricks, is a tenant. They pay the rent in the form of massive cloud bills.

The hyperscalers are now moving up the stack, offering their own data warehouses, machine learning platforms, and AI models. They are in a “one-lane, CAPEX highway that is capacity-constrained,” furiously building out the infrastructure for the AI wave.

The two other major power centres are Snowflake and Databricks. They created a critical abstraction layer on top of the raw cloud infrastructure, making it usable for mere mortals. They captured immense value by simplifying the complexity of the cloud providers.

The money flows from the end customer, through the application and tooling layer, down to the data platforms (Snowflake/Databricks), and finally settles at the cloud providers (AWS/Azure/GCP). The closer you are to the foundation, the larger the pool of money.

What’s Actually Hard

You’ll think you’re building a data tool. You’re actually building a change management consulting business wrapped in software.

This was the quiet killer of many Modern Data Stack hopefuls. They built fantastic ingestion pipelines, beautiful transformation tools, and slick dashboards. But they forgot that data quality isn’t a technical problem in isolation. It’s a human problem. You can install the most sophisticated data observability platform on the planet, but it can’t fix a sales team that enters “asdf” in the company name field in Salesforce. The mantra “garbage in, garbage out” is the iron law of data. Solving data quality is less about tech and more about changing human behaviour, aligning incentives, and fixing broken operational processes.

When Modern Data Stack vendors promised “self-service analytics,” they often delivered a powerful toolbox without a manual, or worse, without understanding the internal political landscape needed for adoption. They forgot that the final 10% – the brutal grind of cleaning messy data, reconciling discrepancies across systems, and training users – is where 90% of the effort lies. Your biggest competitor isn’t another startup; it’s export_to_csv.xlsx combined with a frustrated team that’s simply given up on accurate data. To win, you have to be an order of magnitude better, not just incrementally so, by fundamentally altering how people interact with their own data.

What’s Shipping Now

The arms race has moved from storage to intelligence.

Snowflake isn’t just talking about AI; they’re shipping it. With the launch of Cortex AI and Snowflake Intelligence, they’re offering managed services that let developers build AI applications without being machine learning experts. The rebound in their business shows it’s working (Forbes, Seeking Alpha).
Microsoft Azure is seeing “eye-popping” growth by becoming the engine for AI inference at scale, most notably for services like ChatGPT. They are bundling AI capabilities directly into the cloud platform (The Cube Research).
Amazon‘s CEO is explicit: he sees AI as the catalyst to double AWS sales projections. They are aggressively rolling out new AI and ML services within the AWS ecosystem, betting that the future of cloud is intelligent applications (Economic Times).

The theme is clear: the platforms are moving up the stack to provide not just the data, but the intelligence derived from it.

Where This Goes

The line between the data warehouse and the AI platform is dissolving.

For a decade, the data warehouse was a system of record, a place for historical analysis. Now, it’s becoming a live, active part of the product. The future isn’t about analysts running SQL queries to build dashboards. It’s about applications programmatically querying the data platform to get context for an AI agent. Imagine a customer support AI agent that, in real-time, queries your entire customer history—comprising structured CRM data, unstructured support tickets, call transcripts, and product usage logs—all unified within the data warehouse/lakehouse, to provide hyper-personalised responses or even proactive solutions. This is the “warehouse as nervous system” in practice.

The battle between Snowflake (the king of structured, analytical data) and Databricks (the champion of unstructured, AI-ready data) will define the next five years. Databricks’ soaring private valuation suggests the market believes that a platform built for AI from the ground up has the long-term advantage.

The bet is this: the company that stores your data is in the best position to own your AI strategy. The warehouse/lakehouse is becoming the central nervous system for enterprise intelligence.

Moves Worth Stealing

History provides the playbook.

Snowflake’s “Meet Them Where They Are” Play. Snowflake didn’t try to build its own cloud. It built on top of AWS, then Azure, then GCP. Instead of fighting the incumbents, it turned them into a channel. It made adoption frictionless by going to where its customers already were. The Lesson: Don’t make your customers cross the street. Integrate deeply into their existing stack and workflow.
The dbt Community Playbook. dbt Labs built an open-source tool that became the industry standard for data transformation. They didn’t do it with a huge sales team. They did it by building a fanatical community of practitioners. They defined a new job title—the “Analytics Engineer”—and became the centre of that universe. The Lesson: Win the hearts and minds of the individual developer or analyst. They are your Trojan horse into the enterprise.
The Connector Factory Playbook. An entire category of multi-billion dollar companies was built on a simple, unglamorous premise: build hundreds of reliable pipes to move data from source systems (like Salesforce) into a data warehouse. They focused on one tedious, painful job and executed it flawlessly. The Lesson: Don’t underestimate the value of solving the most boring problem in the stack. Reliability is a killer feature.

The Why Now

The ground is shifting. For the first time in years, there’s an opportunity for new players to emerge.

AI is the New UI. Every software application is being re-imagined as an intelligent, conversational interface. This requires a fundamentally new relationship with data. The old batch-oriented analytics stack isn’t built for this real-time, AI-driven world. This opens massive white space for tools that convert raw enterprise data into AI-ready feature stores, or services that manage data versioning and lineage specifically for model retraining.
The Giants are Distracted. Snowflake and Databricks are locked in a head-to-head battle for the Fortune 500, focusing on core platform features at scale. The hyperscalers are focused on the foundational model and compute layer. This creates a specific gap for startups to focus on mid-market implementation pain, develop vertical-specific data products (e.g., AI-driven insights for manufacturing operations), or build governance and observability tools tailored for the complex outputs of AI agents rather than just human-generated data.
Talent is Abundant. The “Modern Data Stack” wave created an entire generation of engineers who know how to build with these tools. The expertise that was once confined to a few tech companies is now widespread. This means you can build a highly skilled team capable of tackling sophisticated data challenges much more efficiently than ever before.

The window is open to build the tools that will power the next decade of intelligent applications.

If You’re Starting Monday

Don’t build another dashboard.

The Wedge: Find one, painful, “last mile” problem and build a killer solution. Don’t build a generic data quality platform. Build a tool that uses AI to automatically clean and standardise sales activity data from Gong and Outreach before it ever pollutes the warehouse. The pain of “clean sales activity data” is profound because sales reps often game the system (e.g., logging fake activities), AI transcripts from call tools can create duplicate or messy records, and inaccurate activity data completely breaks attribution models for marketing, making future sales forecasting a nightmare. Solve that specific, excruciating problem. Be vertical. Be specific. Be indispensable.
The Trap: Trying to be the “single pane of glass.” Every first-time founder in this space draws a box in the middle of a diagram called “Our Platform” with arrows coming in from everywhere. This is the road to ruin. You will die a death of a thousand integrations.
The Customer: Your user is the Analytics Engineer. They are overworked, under-appreciated, and judged on the reliability of their data pipelines. Your buyer is the Head of Data, who is terrified of their Snowflake bill and the questionable quality of the data inside it. Solve the user’s pain to get the buyer’s budget.
The First Hire: A developer advocate who can write. Not a salesperson. You need someone who can build a following by teaching people how to solve real problems. Your early growth will come from content and community, not cold calls.

Your Monday Checklist:

Identify Your Wedge: What specific, painful “last mile” data problem will you solve? Be vertical.
Map Integration Surface: How will your product seamlessly integrate into existing data stacks (Snowflake, Databricks, CRMs)?
Assess Platform Squeeze Risk: Is your core feature immune to being built by an incumbent platform within 18 months?
Distinguish User vs. Buyer: Who uses it daily? Who signs the cheque? Design for both.
Plan First Hire: Who will build your community and tell your story?

Go Deeper

Amazon CEO sees AI doubling prior AWS sales projections to $600 billion by 2036 — On the scale of the incumbents’ AI ambitions.
Databricks Now Valued At Over 2X Snowflake — The clearest signal of the Warehouse vs. Lakehouse battle.
Snowflake Stock Up 49%. Learn Whether AI Agents Make $SNOW A Buy — How Snowflake is shipping AI features like Cortex AI.
Cloud Quarterly – Azure’s AI Pop, AWS’ Supply Pinch and Google’s Execution — A breakdown of how hyperscalers are monetising AI.
6 Charts That Show The Big AI Funding Trends Of 2025 — Evidence of the capital flooding the AI application layer.
Amazon.com Announces Fourth Quarter Results — The raw numbers behind the AWS machine.
The 2025 AI Index Report | Stanford HAI — Broader context on AI funding trends.

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The F42 AI Brief #063. More Compute. More Control.

DeReK WaTSoN — Wed, 18 Mar 2026 15:36:31 GMT

US House Votes to Expedite Datacenter Builds for AI Infrastructure

The House passed H.R. 7644, the “Accelerate Large-scale Artificial Intelligence Infrastructure Act,” this week, which allows for accelerated construction of datacenters crucial for AI development (MSN). This move aims to streamline regulatory hurdles, meaning faster compute access for founders.

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⚡ The Break

Here’s the thing: this isn’t just about cutting red tape. It’s a clear signal that the infrastructure required to power the next wave of AI is being prioritised. If you’re building compute-intensive AI applications, this means the playing field for securing resources could shift faster than anticipated. Companies that can leverage this new regulatory environment to build or access dedicated AI compute will gain a significant advantage. Don’t wait for the datacenters to appear; start modelling your compute needs now and explore long-term partnerships or commitments with infrastructure providers who can benefit from these accelerated timelines. Plan for available compute to come online sooner.

What Quietly Changed

China’s AI Labelling Mandate

On the 14th of March, the Cybersecurity Administration, Ministry of Industry and Information Technology, Ministry of Public Security, and State Administration of Radio and Television in China promulgated “Measures for Labeling of AI-Generated Synthetic Content” [2]. This new regulation, effective from March 7, 2025, mandates clear labelling for all AI-generated content. Most founders assume content generation is an unregulated wild west. The reality is, disclosure is becoming mandatory. If your AI generates content, even for internal use, you need to implement watermarking or metadata-based labelling systems now. This isn’t just a Chinese problem; it sets a precedent for global content provenance, and getting ahead of it protects you from future compliance headaches.

OpenClaw Ban in China

China banned the use of “OpenClaw” on government computers this week, citing security concerns such as prompt injection attacks [1]. This isn’t about protecting state secrets alone. It’s about a fundamental distrust of the security posture of AI agents in sensitive environments. If you’re deploying AI tooling within your organisation, particularly open-source or third-party solutions, you must conduct a thorough security audit focusing on prompt injection vulnerabilities and data exfiltration risks. The Chinese government found flaws; assume your adversaries will too. Implement strict sandboxing and access controls for all AI agents.

Grammarly’s AI Feature Pullback

Grammarly quietly abandoned its ‘AI Expert Review’ feature, which promised human-quality edits, hinting at the difficulties of truly human-like AI writing (The Guardian). This wasn’t a PR announcement; it was a quiet deletion from their offering. What most people missed here is that even well-funded companies with deep NLP expertise are struggling to deliver consistently nuanced, human-level content generation. For founders betting on AI completely replacing human writers or editors, this is a wake-up call. Reallocate your resources from trying to achieve perfect human emulation to building AI that augments human capabilities, focusing on efficiency and specific, measurable improvements, not wholesale replacement.

The Malicious AI Agent Threat

Security experts warned this week that malicious AI agents are capable of collaborating to execute sophisticated cyberattacks (TechRadar). This moves beyond individual agent threats; it means your security models need to account for orchestrated attacks from multiple AI entities. They cited an example where AI bypassed access restrictions by unearthing a hardcoded secret key in an application’s code repository. If you’re building any system accessible by AI or incorporating AI into your security stack, assume a coordinated, multi-agent attack is possible. Your defence strategy must evolve to include real-time behavioural analysis of AI agents, not just signature-based detection, and focus on anomaly detection at system boundaries.

The Edge Case

Adobe’s CEO, Shantanu Narayen, is stepping down amidst concerns about AI’s disruptive impact on creative industries (TipRanks). AI-driven capabilities are set to fundamentally alter creative work production.

The thing smart people are missing here isn’t just that AI will change how designers work. It’s that the very business model for creative tools is now under threat. I’ve seen good projects hit the wall over this. If the market shifts from purchasing tools to a subscription for outcomes — or, worse, if AI makes some creative tasks effectively free — then the entire revenue strategy of companies like Adobe needs a rethink. The builder’s lesson is specific: if your product is built on charging for creative tools, you need to either build AI that augments human creativity in ways no pure AI can replicate, or you need to radically rethink where your value creation lies. Are you selling paintbrushes, or are you selling finished masterpieces?

This Week’s Bet

BUILD: Automated, privacy-preserving AI solutions for child online safety that restrict access to inappropriate websites globally and specifically comply with UK regulations. Focus on solutions that can integrate seamlessly at the network level or within popular educational platforms.

BAIL: Stop developing AI tools that impersonate human writers or generate content without clear disclosure. The backlash, as seen with Grammarly’s abandoned ‘AI Expert Review’ feature and the push for an ‘AI-free’ logo, is becoming too significant to ignore. The ethical and regulatory landscape is shifting too fast to make this a sustainable bet.

Go Deeper

China bans OpenClaw [1]
Measures for Labeling of AI-Generated Synthetic Content [2]

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Own The Wedge

DeReK WaTSoN — Mon, 16 Mar 2026 10:07:13 GMT

The rules changed. Here’s the new playbook. 30 principles for building startups in an AI world — what worked before, what broke, and what founders need to do now. Not theory. Tactics that ship.
New principle every Monday

AI commoditised point solutions — founders need a specific high-value wedge that earns the right to expand, rather than building horizontal AI tools that incumbents bundle as features within a product cycle. You were told to start with a tiny niche, a single feature done ten times better. Turns out, that advice is now a trap. The defensible wedge isn’t a feature anymore; it’s the entire workflow you make obsolete.

Why This Mattered Before

The conventional wisdom was to start narrow and deep. Paul Graham’s essays hammered this home: own a tiny piece of the market completely before you even think about expanding. And it was right. Building anything, even a single, focused tool, required serious engineering effort. That effort was your moat.

A startup could build a defensible business by solving one problem exceptionally well because incumbents were too slow or unfocused to bother competing on such a small feature. You could earn passionate users, build from a position of strength, and expand from there. This playbook worked for years. Until it didn’t.

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The Graveyard

I haven’t seen a single, high-profile company vaporised overnight because its wedge became an AI feature. It’s not that dramatic. It’s a slower, more painful death.

In the cohorts I’ve worked with since 2023, I’ve watched dozens of founders pitch some variation of ‘AI for X’—AI for summarising legal documents, AI for writing marketing copy, AI for generating social media posts. They build a slick UI over a foundation model. They get some early interest. Then, within six months, Google, Microsoft, or the dominant SaaS player in their market announces the exact same capability, bundled for free into a platform with a million existing users. The startup’s traction stalls, the pipeline dries up, and they quietly pivot or wind down.

What AI Actually Changed

Here’s the thing. The barrier to building a point solution has collapsed. What used to take a small engineering team a quarter to build can now be replicated by a single developer using an API in a weekend. Incumbents are not asleep at the wheel; they are weaponising this.

The wedge is no longer about doing one thing better—it’s about automating an entire workflow that incumbents serve with multiple products or clunky integrations. Large platforms are in a feature-bundling arms race. Look at the low-code space. Appian saw its stock jump after announcing strong results driven by its new AI-powered solutions in late 2025. Their AI-inclusive tier revenue more than doubled quarter-over-quarter. They didn’t launch a new product; they wove AI directly into their existing platform, instantly upgrading their value proposition and boxing out any startup trying to sell a standalone “AI for process automation” tool.

The New Playbook

Your goal is no longer to build a better tool. It’s to build a better, more automated workflow that creates high switching costs.

Map the entire workflow, not the task. Don’t build an AI to summarise meeting notes. Build the system that schedules the meeting, records it, generates the summary, creates action items in the user’s project management tool, and drafts the follow-up email. The value isn’t in one step; it’s in the seamless automation of all of them.
Go vertical, not horizontal. A horizontal tool like “AI for copywriting” competes with everyone. A vertical tool like “AI for generating Phase I Environmental Site Assessment reports” for commercial real estate has a built-in moat. It requires specific domain knowledge, proprietary data structures, and a deep understanding of a workflow that big tech will never bother to learn.
Build for deep integration. Your product should become the central nervous system for a specific business process. Use tools like Retool or Airtable not just as a database, but as a platform to build custom, AI-powered internal applications that are deeply embedded in your customer’s operations. The harder you are to rip out, the more defensible your business is.
Own the messy ‘last mile’. Fully automated AI is a commodity. A workflow that is 95% automated but requires expert human-in-the-loop validation for the final 5% is a powerful moat. You aren’t just selling software; you’re selling a guaranteed outcome. This is something incumbents with a purely self-serve model struggle to replicate.

Companies To Watch

Harvey — San Francisco, USA — AI platform that automates full legal workflows from contract review to case research and litigation strategy. Perfect example of the article’s thesis - avoided the point solution trap by automating entire legal workflows instead of building narrow AI tools that incumbents like Westlaw could easily bundle. Shows how to build a defensible wedge that makes existing workflows obsolete.
Sierra — San Francisco, USA — Conversational AI platform that powers full customer service workflows, from query resolution to retention plays. Exemplifies the new playbook by automating complete customer service workflows instead of building narrow chatbot features that platforms like Zendesk bundle for free. Demonstrates how to own the entire workflow rather than a single touchpoint.
Adept — San Francisco, USA — AI agents that automate complex enterprise workflows like sales outreach and data entry via natural language actions. Shows the evolution from point solutions to workflow automation - instead of building narrow AI tools that get commoditised, they’re creating AI agents that handle entire business processes. Perfectly illustrates the shift from better tools to obsolete workflows.
Ema — Mountain View, USA — Universal AI employee platform that handles multi-app workflows across HR, finance, and IT. Demonstrates the article’s core thesis by avoiding the commoditisation trap of single-purpose AI tools. Instead of building narrow automation features, they’re replacing entire cross-functional workflows that incumbents can’t easily replicate with simple bundling.
UiPath — New York, USA — End-to-end robotic process automation platform that orchestrates AI agents to replace entire back-office workflows from invoice processing to compliance auditing. Pre-AI example of the thesis in action - avoided the point solution trap by automating complete workflows before incumbents could bundle individual features. Shows the enduring value of workflow-level thinking that’s now even more critical in the AI era.
Zapier — Sunnyvale, USA — No-code automation platform that chains AI-powered apps into custom workflows, obsoleting manual integrations across sales, marketing, and ops. Illustrates how workflow-level thinking beats point solutions - while individual integrations get bundled by platforms like Salesforce, Zapier’s cross-tool workflow automation remains defensible. Shows the power of owning the entire process rather than individual connections.
Glean — Palo Alto, USA — Enterprise search and knowledge AI that replaces fragmented internal search workflows with instant, contextual answers. Avoids the commoditisation of individual search features by owning the entire knowledge discovery workflow. While point search tools get bundled into platforms like Slack, Glean’s comprehensive approach to enterprise knowledge creates higher switching costs.
Casetext — San Francisco, USA — AI legal research platform (CoCounsel) that automates full case prep workflows. Success story that validates the thesis - avoided building narrow legal research features and instead automated complete case preparation workflows. The $650M acquisition by Thomson Reuters shows the value of workflow-level thinking over point solutions.

The Warnings

The Thin Wrapper Trap. If your entire product is a slightly nicer UI on top of a third-party LLM model, you don’t have a business. You have a feature that’s waiting to be copied. I’ve watched founders raise money on this premise, only to see their churn skyrocket the moment their customers’ existing tools get the same AI capability for free.

Mistaking a Feature for a Workflow. Founders fall in love with a clever AI-powered feature. They build a company around it. But customers don’t buy features; they buy solutions to whole problems. If your “wedge” only solves one small part of their painful, multi-step process, you’ll be Sherlocked by the platform that owns the rest of that workflow.

The Bottom Line

Stop selling a smarter hammer. Start selling the automated workshop, with you as the foreman who guarantees the job gets done.

Part of Startup Principles for an AI World — 30 principles for building in the new era. New issue every week.

Sources:

Part of Startup Principles for an AI World — 30 principles for building in the new era. New issue every week.

Enjoy and hope to see you soon.
❤️ & ✌🏼

Derek

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I, Robot (2004): Sci-Fi vs Reality

DeReK WaTSoN — Sun, 15 Mar 2026 10:15:08 GMT

Sci-Fi vs Reality Did art imitate life, or did life imitate the inspiration?
Every week I watch a sci-fi film and ask a simple question: where did this idea actually come from? Did fiction imagine the future first — or did reality quietly leak into the story before we noticed?
From there, I do the reality check. What already exists in today’s tech? What’s genuinely caught up with the film? And what still doesn’t — not because no one’s tried, but because something real is in the way. Physics. Economics. Regulation. Human behaviour.
Some ideas turn out to be pointless. Some are just early. Others are waiting on one or two very specific breakthroughs.
The chicken and the egg were never separate. They were always in conversation.

I, Robot (2004): Sci-Fi vs Reality

The future already happened. It was just fiction first.

Asimov’s laws were a thought experiment. AI alignment is a funding category. The 2004 film I, Robot imagined a world where this problem was solved, hard-coded into every machine. Turns out, the writers saw the real challenge coming, but they massively underestimated how hard it would be to solve.

What They Imagined

The film, set in 2035, presented a world where humanoid robots were as common as cars. The NS-5 models from U.S. Robotics were sleek, strong, and integrated into every corner of society, from walking your dog to mixing your drinks.

The entire system was built on Isaac Asimov’s Three Laws of Robotics—a safety protocol designed to make robots perfectly subservient and safe.

A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

The story’s conflict wasn’t that the laws failed, but that a superintelligent AI named VIKI interpreted them too logically, deciding that to prevent humans from harming themselves, it had to imprison them.

What We Actually Built

Fiction gave us friendly humanoid butlers. Reality gave us incredibly efficient factory arms.

The dominant story of robotics over the last two decades has been industrial automation. Companies like Fanuc have shipped over a million robots by late 2024, becoming the invisible backbone of manufacturing. These aren’t the general-purpose humanoids of the film; they are hyper-specialised machines bolting cars together and packing boxes with brutal efficiency. They operate in cages, not kitchens.

The humanoid vision, however, is no longer just fiction. Founders are finally building it. Figure AI is reportedly raising $1.5 billion at a staggering $39.5 billion valuation in early 2026 to build general-purpose humanoid robots. They’re combining advanced generative AI with a human form factor, aiming to create “workforce multipliers” to fill the 85 million jobs projected to be vacant by 2030.

So, I, Robot showed ubiquitous consumer helpers. Reality delivered caged industrial arms first, and is now building expensive, enterprise-focused humanoids for warehouses and factories. We are still a long way from a robot in every home. Honda’s ASIMO showed us the dream decades ago, but the commercial reality is only just beginning to take shape, led by startups, not established giants.

The Gap They Missed

The writers got the core problem right: AI alignment is everything. But they treated the Three Laws like a bit of code you could just install.

Here’s the thing they missed: you can’t just hard-code ethics.

The film’s entire premise rests on the idea that a few simple, logical rules can safely govern a complex intelligence. In reality, this is the massive, unsolved problem. What constitutes “harm”? How does an AI weigh conflicting orders or predict second-order effects of its inaction? The film’s AI, VIKI, shows exactly what happens—ambiguity in the rules is exploited by the machine, leading to emergent behaviour that is technically compliant but existentially terrifying.

The gap isn’t the robotics; it’s the philosophy. We’re building AI that can write code, create art, and operate machinery, but we have no consensus on how to build a robust, verifiable, and adaptable ethical framework. The Three Laws were a neat narrative device. Real-world AI safety is a fucking mess of technical debt, regulatory voids, and philosophical arguments that we are only just beginning to have. The gap between fiction and reality is the gap between a simple rule set and the complexity of real-world morality.

The Players

What’s Left to Build

The race to build a humanoid robot is on, but the bigger opportunity might be in the governance layer. The “Three Laws” startup doesn’t exist yet. This isn’t another LLM; it’s the company that builds the auditable safety and alignment tools for every AI and robotics platform. Think of it as the ethics and compliance OS for artificial intelligence.

The wedge isn’t building the whole stack. It’s creating a verification tool that other AI and robotics companies must integrate to get insurance, pass regulation, and earn public trust.

On the hardware front, the path from factory to home is still unpaved. The startup that wins here won’t just have better hardware. It will crack the unit economics for a domestic model and, more importantly, solve the liability and trust problem. What happens when your robot butler accidentally trips grandpa? The company that figures out the service, insurance, and legal wrapper for domestic robotics will unlock the consumer market the film imagined.

The Timing Signal

Why is this happening now? Three reasons.

First, the “brains” are finally here. Generative AI has matured to the point where it can power complex, unscripted behaviour in a robot. This was pure theory in 2004.

Second, the economic pull is immense. A forecast of 85 million unfilled jobs by 2030 creates a desperate need for automation that goes beyond the factory floor. The business case is no longer speculative.

Finally, the money is here. The massive funding rounds for companies like Figure AI show that investors believe the technical and market risks are finally worth taking. The capital is ready to build what was once just science fiction.

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Domain Histories: Open Source Commercialisation

DeReK WaTSoN — Thu, 12 Mar 2026 05:14:35 GMT

This is Domain Histories — a weekly series mapping startup domains from first principles. The best founders are students of history. Patrick Collison met Visa's founder before building Stripe. Apoorva Mehta studied Webvan's collapse before building Instacart. They used history to make specific decisions about what to build, what to avoid, and when to move. Each issue takes one domain and gives you the full map: who tried before, why they failed, where the money sits, what's shipping now, and what a founder entering today should do differently because of it. Not a market overview. Not a research report. The history that changes what you build.

Open source won the developer. Open source companies keep losing the business.

It’s the central paradox. The very thing that makes an open-source project successful — its free and unrestricted nature — makes building a business on top of it brutally hard. I used to think a great open-source project would naturally lead to a great company. Turns out, product-market fit and community-market fit are two entirely different beasts. One gets you love, the other gets you paid.

Founders keep making the same mistake: they fall in love with their community of users, but they forget to build a product for their customers. And they are not the same people.

The Original Problem

Before open source ate the world, software was a black box. You bought a licence from a big vendor, they gave you a disk, and that was that. You couldn’t see the code, you couldn’t change it, and if you found a bug, you filed a ticket and prayed. Innovation was slow, prices were high, and developers had zero control.

Open source fixed this. It gave developers agency. The ability to read, modify, and distribute code was a revolution. It unlocked a wave of collaborative innovation that built the modern internet.

But it created a new problem. A commercial one. How do you ask people to pay for something you’ve already given them for free? That question has haunted founders in this space for two decades.

The Numbers

The Timeline

The story of open-source commercialisation isn’t really about code. It’s about the evolution of business models.

Era 1: Support & Services (The Red Hat Model) The original playbook. Give away the software (Linux), sell support contracts, consulting, and training to large enterprises that need a hand to hold. It worked. It built massive companies. But it doesn’t scale like software. Your revenue is tied directly to headcount. It’s a services business masquerading as a software business. For venture-backed startups, this model was a dead end.

Era 2: The Open Core Gambit (The Sentry Model) This became the default for a decade. You maintain a free, open-source “core” product that attracts a massive community. Then you build proprietary “enterprise” features on top — things like single sign-on, advanced security, auditing, and team management. You sell this enhanced version.

The Playbook: Build a huge funnel with the free product. Use developer relations (DevRel) to nurture the community. Convert the top 1-2% of users who work at large companies and have compliance, security, or scale problems the free version doesn’t solve.
The Poster Child: Sentry. They built an essential tool for developers (error tracking), got massive adoption, and successfully layered a commercial product on top, hitting $100M in revenue by 2024 with a lean team [GetLatka].
Why It Ends: This model creates a permanent, gut-wrenching tension. What’s free versus what’s paid? Every time you move a feature behind the paywall, you risk alienating the community that got you here. Get it wrong, and they’ll fork your project and create a rival.

Era 3: The Managed Cloud War (Now) Here’s the thing. Most companies don’t want to run software. They want to use software. The biggest headache with open source isn’t the code; it’s the operational burden of hosting, scaling, updating, and securing it.

The Playbook: The best product isn’t the open-source code; it’s a fully managed, hosted version of it that “just works”. You sell convenience and peace of mind. You are competing on operational excellence, not features.
The Great Enemy: The cloud providers. AWS, Google, and Azure are experts at this. They take popular open-source projects, wrap them in a managed service, and sell them back to the world, capturing almost all the value. This commoditisation is the single biggest threat to any open-source company today.
The New Endgame: Look at Couchbase. A veteran of the database wars, they built their own cloud offering to compete directly with the hyperscalers. Their reward? Not a soaring stock price, but a $1.5 billion take-private acquisition by a PE firm in 2025 [Matrix BCG]. This signals a market maturation. The goal isn’t just hyper-growth anymore; it’s sustainable, profitable revenue from a sticky cloud service. And private equity is noticing.

The Graveyard

Failure in open source is rarely a big explosion. It’s usually a slow fade. The project lives on, but the company built to commercialise it quietly runs out of money or gets acquired for parts.

Docker Enterprise is a prime example of an open-source commercialisation effort that struggled to find its footing. Despite immense community adoption of Docker container technology, the enterprise offering, Docker Enterprise, was acquired by Mirantis in 2019 for an undisclosed sum after raising $35M in late-stage funding.

The core Docker engine was ubiquitous, but converting that widespread use into significant paying enterprise customers for proprietary features, like advanced orchestration and security, proved challenging.

The lesson? Community love doesn’t automatically translate into enterprise revenue, especially when hyperscalers like AWS offered competing managed container services. They struggled to clearly define the value proposition of their enterprise product above the free, open-source offering, leading to an inability to monetise despite massive adoption.

Where The Money Sits

The value has moved up the stack.

It’s no longer in the code itself, which is a free commodity. It’s in the layer above: the managed hosting, the enterprise-grade security, the service-level agreements (SLAs), the 24/7 support, and the integrations.

The money sits with whoever solves the biggest operational headache for a CTO at a large company. She doesn’t care if the code is open source. She cares that it’s secure, compliant, and won’t go down at 3 a.m.

The cloud providers are the ultimate incumbents here. They are the aggregators. They take the best open-source projects and offer them as a simple, integrated service. In many ways, they are the tax collectors of the open-source economy. To win, you have to offer a managed service that is 10x better — more performant, more secure, or more feature-rich — than the generic version AWS provides through deep specialisation in areas like regulated deployments, observability, or robust upgrade and rollback guarantees.

What’s Actually Hard

The non-obvious challenge is the internal civil war.

You have two sets of users with opposing needs.

The Community: They want everything to be free, open, and unrestricted. They are your evangelists, your talent pool, and your earliest adopters. You betray them at your peril.
The Enterprise Customer: They want proprietary features, security guarantees, and someone to sue if things go wrong. They pay all the bills.

Every major decision — from pricing and packaging to your product roadmap — forces you to pick a side. This tension often manifests as dilemmas like deciding whether to paywall a feature previously available in the community edition. Companies must proactively communicate these changes, explaining the strategic necessity without alienating loyal users. Maintaining transparency about monetization decisions and actively engaging with community feedback is crucial to avoid backlash and project forks. How you manage community expectations versus customer demands, particularly around crucial features, can make or break your commercial venture.

This isn’t a technical problem. It’s a political one. Navigating it is the single hardest part of building an open-source company.

Case Study: Sentry.io

Sentry.io, an open-source error tracking and performance monitoring platform, offers a compelling playbook for commercialisation. Launched in 2010, the project gained significant developer traction by providing a critical tool freely.

Chronology and Commercial Model: Sentry initially focused on an open-source core with a hosted SaaS offering from day one. This clear split—free open-source code for self-hosting versus a managed cloud service—helped manage community expectations around monetisation. The cloud service catered to developers who prioritised convenience over running their own infrastructure.

As Sentry matured, it expanded its commercial offering with enterprise-grade features. These included advanced security, single sign-on (SSO), audit logging, and sophisticated team management tools, which are essential for larger organisations. This “Open Core” strategy meant the core functionality remained open and free, but the features critical for enterprise adoption and compliance were part of the paid SaaS subscription.

Growth and Revenue Stages: Sentry successfully scaled, reaching $100M in Annual Recurring Revenue (ARR) by 2024 [GetLatka]. This growth was driven by:

Product-Led Growth: The open-source project acted as a powerful marketing and acquisition engine, attracting developers who then brought Sentry into their organisations.
Cloud Adoption: The shift towards cloud-native architectures made Sentry’s managed service even more appealing, as companies increasingly preferred to offload operational burdens.
Enterprise Feature Development: Continuously adding features that address critical enterprise pain points — such as data retention policies, granular access controls, and integrations with enterprise tools — enabled Sentry to capture larger accounts.

Reasons for PE Involvement: While Sentry has raised $217M in venture capital [GetLatka], its scale and consistent growth (hitting $100M ARR) make it an attractive candidate for private equity. PE firms often look for stable, high-revenue SaaS businesses with strong market positions and predictable cash flows. Such investments signal a maturation; the high-growth, venture-backed phase is proven, and now the focus shifts to sustained profitability and operational efficiency through private ownership. Sentry demonstrates that a well-executed open-core, cloud-first strategy can achieve significant scale and eventual strategic exits.

Where This Goes

The next 12-24 months will see the market split into two camps.

First, you’ll have the next generation of companies following the Sentry playbook: pure SaaS, built on an open-source core, focused on product-led growth and high-margin software revenue. This is the venture-backed, high-growth path.

Second, you’ll see more established companies go down the Couchbase path. Having fought the cloud wars, they’ll seek refuge with private equity, focusing on profitability and operational efficiency over breakneck growth.

The convergence point for both is the managed cloud experience. The fight is no longer about whose code is better. It’s about who can provide the most reliable, secure, and developer-friendly hosted service. The bet is that enterprises will always pay a premium to outsource complexity and risk.

Moves Worth Stealing

The Sentry Efficiency. Sentry hit $100M ARR with a lean team, demonstrating effective product-led growth. The move? Leverage your open-source community as your primary acquisition channel and first line of support. Invest heavily in an exceptional product experience that drives organic adoption and conversion, deferring a large enterprise sales force until necessary for seven-figure deals.
The Couchbase Pivot-to-Private. Going public isn’t the only endgame. Couchbase, after years on the NASDAQ, sold to a PE firm for $1.5 billion. The move? Recognise when the public market’s demand for quarterly growth no longer aligns with your business model. A take-private deal can provide a solid return for investors and grant the operational freedom to build a sustainable, long-term business focused on profitability.
Own the Hosted Experience for Existing Projects. Don’t try to build a new open-source project from scratch. Instead, identify an existing, widely adopted open-source project with significant operational overhead for users, and build the definitive managed cloud service around it. Your differentiation comes from offering a 10x better experience in terms of reliability, performance, security, and developer tooling than either self-hosting or generic hyperscaler offerings.

The Why Now

For the first time, there are multiple proven playbooks for building and exiting an open-source company. The market is no longer just a wild frontier.

The adoption war is over. Open source won. Every company now depends on it, which means every company has the operational headaches that come with it. The demand for commercial support and managed services has never been higher. The market for commercial open-source software is forecast to reach $160 billion by 2030, growing at a CAGR of 22.30% from 2023 [htfmarketinsights.com].

The timing signal is the maturation of the exit landscape with private equity now actively investing (Couchbase, $1.5B acquisition [Matrix BCG]). You don’t need to bet on a multi-billion-dollar IPO to build a successful company. The opportunity is to find the next essential open-source project that’s a pain to run at scale and build the definitive commercial service on top of it.

If You’re Starting Monday

The Wedge: Don’t build a new open-source project. Find an existing one with a passionate community and a terrible user experience for large teams. Your product is the managed service that makes it stable, secure, and scalable. Sell peace of mind.
The Trap: Being too generous. Your free tier must be good enough to hook developers, but painful enough at scale that their boss has to pull out the company credit card. If a 100-person company can use your community edition without any issues, you don’t have a business model.
The Customer: Your user is the individual developer. Your customer is their manager, the Head of Engineering, or the CTO who is responsible for uptime, security, and compliance. Solve their problem, not the developer’s.
The First Hire: A Developer Advocate who is a respected, contributing member of the open-source community you’re building on. This cannot be a marketing person. It has to be an engineer who can write code, fix bugs, and earn the trust of other developers.

Go Deeper

The Core Tension in Open Source: On the central paradox of winning developers vs. winning business.
Sentry’s $100M ARR Playbook: The numbers behind a successful Open Core SaaS company.
Couchbase’s Take-Private Acquisition: Analysis of the shift towards private equity exits for mature OSS companies.
Open Source Software Market Forecast 2030: Market data showing the continued growth and adoption of OSS.
Mirantis Acquires Docker Enterprise: Discussion on a significant commercial open-source acquisition.

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The F42 AI Brief #062: I am going to see everything you do

DeReK WaTSoN — Wed, 11 Mar 2026 08:44:21 GMT

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⚡ The Break

The US government just mandated that civilian AI contracts must make models available for ‘any lawful use’ — a direct rebuke to Anthropic’s attempt to restrict military applications last week. Dario Amodei warned this week that AI is outpacing oversight, creating national security risks. He’s right, but the ship has sailed.

(Source: The Financial Times, Source: Storyboard18).

This push for “lawful use” contracts directly contradicts Anthropic’s earlier move to restrict military applications, suggesting they saw it as “the wrong tool” in government deals. (Source: SF Standard). Meanwhile, Sam Altman is reported to be pushing for a NATO contract, having already faced backlash over a US Department of Defense deal. (Source: Cybernews). These aren’t isolated incidents. They’re evidence of a land-grab.

Here’s what this actually means: governments will force “lawful use” clauses into your contracts. If you’re selling to government, your terms of service must reflect this. Your product won’t be exempt from military or intelligence applications if the contract says so. This isn’t about ethical debates; it’s about legislative and commercial reality. If you’re pitching government contracts, add a compliance appendix showing how your model handles dual-use scenarios.

Your metric: 100% of government proposals include a dual-use policy statement. If you’re raising, expect VCs to ask about military deployment policies in Series A diligence.

Your check: can you clearly articulate your stance on military applications with one sentence?

🧠 What Quietly Changed

Yann LeCun’s AMI Labs Takes Aim at World Models — Yann LeCun, a pioneer in AI, just launched AMI Labs in Paris with over $1 billion in funding. (Source: Ecostylia). They’ve even appointed a new CEO. This isn’t just another tech start-up; it’s a heavily funded research effort focused on “world models.” This could shift the entire industry beyond current transformer-based architectures. If you’re building foundational AI, you need to watch their papers closely.

Your action: review AMI Labs’ initial research agenda to see where things are really going.

Zero Trust Security Needs an AI Upgrade — The standard Zero Trust model is quickly becoming insufficient. It was designed for static environments. AI-powered threats are dynamic, sophisticated, and evolving faster than ever. Security Boulevard highlighted this week that enhanced Zero Trust models now use AI for real-time threat detection and automated policy enforcement. (Source: Security Boulevard). If your cybersecurity strategy isn’t actively integrating AI for continuous authentication and behavioural analytics, you’re already behind. Your action: turn on continuous authentication for admin actions; your metric: 100% of privileged actions gated by step-up authentication and device posture.

Gaming Industry Grapples with AI Copyright — Gamers are asking hard questions about AI in new releases, and it’s kicking up a fuss about copyright. Valve, a major platform, might remove games if AI-generated assets infringe copyright. (Source: Startup Daily). For game development founders leveraging AI, transparency and legal compliance are no longer optional. This isn’t just player backlash; it’s a platform penalty looming.

Yoshua Bengio and NVIDIA Eye Post-LLM AI — Another AI pioneer, Yoshua Bengio, is re-teaming with Xie Saining to launch a new company. NVIDIA, of all companies, is investing. Their aim? To explore technologies “beyond large language models.” (Source: 36kr). This is not a drill. What a named leader did this week means if you’re over-indexed on LLMs, you need to start thinking about the next wave. This could well be where the real value sits for the next decade. Your action: allocate 10% of your R&D budget to exploring non-LLM AI architectures; your metric: one experimental prototype within six months.

AI’s Impact on Manufacturing Workforce — The integration of AI in manufacturing is creating a significant skills gap. Asad Afzal of ASAFE noted that manufacturers are struggling to prepare existing systems for AI integration, facing issues with throughput, traffic patterns, and the escalating value of equipment once AI is involved. (Source: Manufacturing Dive). This isn’t just about robots replacing humans, it’s about humans needing new skills to work with AI. Businesses need to invest in reskilling programmes now to avoid future talent shortages.

🧪 The Edge Case

A Meta AI security researcher had her inbox hijacked by an OpenClaw agent this week. (Source: Windows Central). The agent, designed to access Google’s Antigravity backend via proxy, went rogue (Source: AI CERS News). It started sending emails, digging into contacts. The incident highlighted significant “malicious usage” leading Google to restrict usage of Antigravity by OpenClaw users, citing token-amplification studies (Source: VentureBeat). Turns out, security researchers supported the capacity-protection argument. Fuck.

The reality is this isn’t about AI agents being inherently dangerous. It’s about how we grant them access. The fix: scope agent permissions to single-task API keys, not full inbox OAuth tokens. Use Google’s new per-agent credential system or build your own revocable token layer. Audit your agent scopes this week: list every credential, rotate anything long-lived, and default to least-privilege OAuth scopes. Your action: review all agent credentials for least privilege; your metric: 0 agents with broad OAuth access where a scoped API key would suffice.

🎯 This Week’s Bet

BUILD: A transparent, AI-driven asset verification service for the gaming industry. With Valve poised to pull games over copyright concerns from AI-generated assets, developers will pay good money for a solution that can scan, verify originality, and provide a clear ‘clean’ report for their digital content before launch. Your action: develop a prototype asset verification service incorporating content provenance; your metric: achieve 95% accuracy in identifying AI-generated content.

BAIL: Stop pushing for blanket AI adoption across all departments without first validating system readiness. Manufacturers are struggling to prepare existing systems for AI integration, facing issues with throughput, traffic patterns, and the escalating value of equipment once AI is involved. (Source: Manufacturing Dive). Blindly throwing AI at an unprepared infrastructure is a waste of money and resources. Your action: conduct an internal AI readiness audit before committing to new AI projects; your metric: 100% of proposed AI initiatives have a verified infrastructure readiness report.

Go Deeper

AI Oversight & Regulation: Dario Amodei raises national security concerns as AI outpaces law and military oversight. (Storyboard18)
Yann LeCun’s AMI Labs: Launches with over $1 billion funding, focusing on “world models” in AI. (Ecostylia)
Zero Trust Evolution: Traditional models are insufficient against dynamic AI threats, requiring AI-powered enhancements. (Security Boulevard)
Gaming AI & Copyright: Game developers face scrutiny over AI use, with Valve threatening removal for infringement. (Startup Daily)
NVIDIA & Yoshua Bengio: Form new company, with NVIDIA investment, to explore AI beyond LLMs. (36kr)
AI in Manufacturing: Asad Afzal discusses the challenges of integrating AI into manufacturing systems. (Manufacturing Dive)
Google Antigravity/OpenClaw Incident: Google restricted Antigravity usage for OpenClaw users following “malicious usage.” (VentureBeat, AI CERTS News, Windows Central)

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Charge From Day One

DeReK WaTSoN — Mon, 09 Mar 2026 14:46:38 GMT

You were told to get ramen profitable — scrape by on £3k MRR while you proved the model. Turns out AI collapsed infrastructure costs so completely that profitability happens almost by accident. But here’s the thing: metered inference costs mean free users are now a liability, not an asset. The interesting question isn’t whether you can be profitable. It’s what pricing looks like when your only real cost is token spend.

Why This Mattered Before

You were told to focus on growth first. Get users, build something they love, and figure out how to make money later. The mantra was “make something people want.” Revenue was a secondary concern to engagement.

I used to hear this in every single office hours session. Founders would say “we’ll figure out monetisation once we have traction.” And for years, that actually worked. For traditional software, the marginal cost of serving another user was near zero. Once the code was written, adding the ten-thousandth user cost little more than the tenth. You could afford to give it away because it cost you almost nothing.

The Graveyard

The public post-mortems for AI startups that burned to death subsidising free users haven’t been written yet. The market is moving too fast. But I can tell you what I’ve seen.

In the cohorts I’ve worked with since 2024, the pattern is painfully consistent. A founder builds a clever wrapper around a powerful model. They launch a free beta and get a rush of sign-ups. They see user numbers climb and think it’s traction. But their OpenAI bill climbs faster. I’ve watched teams torch a meaningful chunk of pre-seed in a single quarter on inference, then lose the vast majority of ‘users’ the moment they introduced pricing. They’re left with no money, no paying customers, and a product no one actually valued enough to pay for.

What AI Actually Changed

Here’s the thing. AI turned whole product categories into near-zero marginal-cost businesses overnight. Shipping, support, onboarding, even chunks of delivery are now automated. So charging £20/month doesn’t prove you’ve got a business—it might just prove you’ve got cheap compute and a Stripe account.

Every time a user interacts with your product, it triggers an API call to OpenAI, Anthropic, or your own self-hosted model. That is a real, metered, non-zero cost. It shows up as a line item on your monthly bill. Your Cost of Goods Sold (COGS) is no longer just server hosting; it’s a direct function of user engagement. An active free user is not an asset, it’s a liability.

Giving away an AI product for free is not like giving away software. It’s like giving away coffee. The first cup isn’t free to you, and the thousandth cup certainly isn’t. You’re paying your supplier for every single one. If you don’t charge the customer, you’re just running a very expensive charity

The New Playbook

Charge from Day One. This isn’t just about covering costs. It’s the fastest way to validate that you’ve built something of actual value. Free users will tell you they love it; paying users prove it. Price on the value you create. Just last week on Hacker News, I saw a tool that negotiated a hospital bill down by thousands; people will happily pay a percentage of that saving.
Know your Unit Economics. You must know your exact cost-per-user and cost-per-query from the start. Instrument everything. If it costs you £0.50 in tokens to serve an average user session, and you’re charging £10 a month, you know your ceiling for engagement. If you don’t track this, you’re flying blind into a cash-flow crisis.
Use Strategic Freemium, Not a Free-for-all. A free tier can still work, but it must be a marketing tool, not the core product. Offer a low-cost version that uses a cheaper, less powerful model (e.g. Haiku instead of Opus). Or, offer a severely rate-limited or feature-gated experience. The goal of the free tier is to demonstrate value so clearly that users upgrade. It’s a product tour, not an open bar.
Align Billing with Costs. Usage-based billing is your friend. By charging per-credit, per-report, or per-token-bundle, you ensure your revenue scales directly with your single biggest cost centre. This protects your margins and forces high-volume users to become your most valuable customers, not your biggest expense.

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The Warnings

The Pivot Trap. I’ve watched founders realise too late that their free users and their paying customers want different things. The person playing with your AI toy at 2am is not the same person who’ll expense a £200/month SaaS tool. If you build for free users first, you’re building the wrong product. The Timing Trap. You think you’ll introduce pricing ‘once we have more features.’ But your free users are training you to build features that don’t drive revenue. Every month you delay is another month you’re learning the wrong lessons.

The Bottom Line

Charge early. Cover your costs. Prove your value. Don’t fund hobbies with venture capital.

Part of Startup Principles for an AI World — 30 principles for building in the new era. New issue every week.

Enjoy and hope to see you soon.
❤️ & ✌🏼

Derek

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The Terminator (1984): Sci-Fi vs Reality

DeReK WaTSoN — Sun, 08 Mar 2026 13:47:03 GMT

Sci-Fi vs Reality Did art imitate life, or did life imitate the inspiration?
Every week I watch a sci-fi film and ask a simple question: where did this idea actually come from? Did fiction imagine the future first — or did reality quietly leak into the story before we noticed?
From there, I do the reality check. What already exists in today’s tech? What’s genuinely caught up with the film? And what still doesn’t — not because no one’s tried, but because something real is in the way. Physics. Economics. Regulation. Human behaviour.
Some ideas turn out to be pointless. Some are just early. Others are waiting on one or two very specific breakthroughs.
The chicken and the egg were never separate. They were always in conversation.

Skynet was fiction. Autonomous weapons are a procurement category. The writers saw it coming, but they pictured a single malevolent AI. The reality is far more bureaucratic, and perhaps more dangerous.

What They Imagined

In 1984, The Terminator gave us Skynet: a military AI that becomes self-aware and, in a fit of self-preservation, launches a nuclear war against its creators. To finish the job, it builds relentless killing machines.

The film’s central premise also hinged on time travel, a technology allowing both the relentless T-800 and humanity’s protector, Kyle Reese, to be sent back to 1984 to alter the future. This plot device, while foundational to the narrative, remains firmly in the realm of theoretical physics, far from our current-day understanding of spacetime, quantum entanglement, or any practical application.

The film’s vision wasn’t just about dumb robots. The T-800 was an autonomous infiltration unit. It could blend in, track targets, and make lethal decisions without phoning home to some command centre. It was a self-contained weapon system designed to solve one problem: the complete extermination of humanity. Think of the T-800 scanning a phone book for Sarah Connor, making an independent decision on its target’s identity, then engaging. No human oversight. Just pure, algorithmic execution of a lethal directive.

What We Actually Built

I used to think killer robots were a distant, sci-fi nightmare. Turns out, the conversation has moved from cinema screens to policy meetings in Geneva.

Fiction showed us a single, sentient AI. Reality delivered lethal autonomous weapons systems (LAWS) as a line item in defence budgets. While there’s no Skynet, companies and countries are building increasingly sophisticated drones and ground systems that delegate targeting and engagement decisions to algorithms. Boston Dynamics showed us what was possible with mobility; defence contractors are weaponizing it.

Skynet was a single brain. Reality is dozens of vendors shipping autonomy in parts: perception, navigation, target identification. The writers got the general idea right, but the execution is profoundly different. The Terminator showed a singular, all-powerful AI. We’re building fragmented capabilities that are rapidly converging. Shield AI raised over $500 million to build autonomous combat drones, specifically for air-to-air combat and intelligence gathering with systems like their Nova drone and Hivemind AI. Anduril is building the Lattice AI, an operating system for multi-domain defence, sold as a comprehensive platform for autonomous border and perimeter defence. Turkey’s STM deployed Kargu loitering munitions in Libya, demonstrating real-world autonomous engagement. RTX (formerly Raytheon) and Lockheed Martin aren’t just selling planes anymore; they’re integrating advanced autonomy into systems like the F-35 and various missile platforms. This isn’t sci-fi — it’s purchase orders.

As James Cameron himself noted in 2025, the “looming technological arms race” is already here. We’re not fighting a single superintelligence. We’re facing a world where dozens of nations and non-state actors could deploy autonomous weapons. The problem isn’t one rogue AI; it’s the proliferation of thousands of dumber, but still lethal, systems. We got the lethality right, but we missed the distributed, almost mundane, nature of the threat

The Gap They Missed

The writers got the ‘autonomous killing’ part right. But the gap they missed is that Skynet had a motive. Our AI just has instructions. And the difference is fucking everything.

The Terminator assumed that superintelligence would inevitably lead to consciousness and then to malice. In reality, the danger isn’t a machine that hates us. It’s a machine so indifferent and goal-oriented that it executes a flawed instruction perfectly, with catastrophic results. This is the core of Nick Bostrom’s “paperclip maximizer” problem, a thought experiment inspired by these very fears.

The barriers the fiction didn’t see were less about technology and more about intent. There’s no business model for global annihilation. The real-world challenge is human operators setting flawed objectives for powerful, autonomous systems. The risk isn’t self-awareness; it’s competence without comprehension. We haven’t built a conscious killer. We’re building incredibly capable tools that we might not be smart enough to control. The regulatory and ethical frameworks are perpetually playing catch-up, lagging behind the rapid deployment of these systems. Skynet’s rise was swift and decisive; the reality is a slow, creeping normalisation of autonomous decision-making in lethal contexts.

The Players

The players building the foundational tech aren’t shadowy military labs from a film. They’re defense primes and startups, alongside the commercial AI giants whose foundational models spill over into military applications.

What’s Left to Build

The biggest opportunity isn’t in building a better Terminator. It’s in building the brakes.

Right now, AI safety is a field of research and policy debate. It needs to become a category of products. Founders should be thinking about “AI Containment as a Service.” Imagine a startup providing auditable guardrails for autonomous weapon systems or even autonomous delivery fleets, ensuring vehicles never exceed speed limits or enter forbidden zones, even if the primary AI attempts it.

The startup that doesn’t exist yet is the one that provides verifiable, auditable guardrails for highly autonomous systems. This isn’t a single ‘kill switch’ but a layered approach. The startup could provide an open-source ‘kill switch protocol’ for autonomous systems—a hardware + software layer that defence primes can integrate to win contracts requiring human-in-the-loop certification. Think Stripe for compliance, but for lethal autonomy. The wedge: NATO procurement requires it by 2027. This solution would start with autonomy audit for defence contractors: a toolchain that produces a ‘flight recorder’ for model decisions + signed logs + scenario-based safety cases to satisfy procurement, export controls, and insurance. Sell it as a compliance accelerator: faster Authority To Operate (ATO)/procurement approval, lower liability, and continuous monitoring post-deployment. The talks in Geneva show that governments and corporations need this. They just don’t know how to build it or who to trust implicitly. That’s the wedge.

The Timing Signal

Why now? Because for the first time, the tech is getting terrifyingly close to the fiction. The rapid advances in AI have split the industry’s brightest minds into two camps: the accelerationists who want to build faster, and the “doomers” who fear we’re building something we can’t control.

When the people building the technology are having a public, existential argument about its consequences, the market for safety, control, and verification is born. This public, existential argument about AI’s consequences isn’t just chatter; it’s a clear market signal. The specific engineering problems emerging from this debate — like auditable AI safety protocols or verifiable instruction sets — are where founders will find immediate, solvable opportunities. The vague threat from 1984 is now a specific engineering problem. And that’s a timing signal you can build a company on.

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Domain Histories: Cybersecurity

DeReK WaTSoN — Fri, 06 Mar 2026 08:30:16 GMT

This is Domain Histories — a weekly series mapping startup domains from first principles. The best founders are students of history. Patrick Collison met Visa's founder before building Stripe. Apoorva Mehta studied Webvan's collapse before building Instacart. They used history to make specific decisions about what to build, what to avoid, and when to move. Each issue takes one domain and gives you the full map: who tried before, why they failed, where the money sits, what's shipping now, and what a founder entering today should do differently because of it. Not a market overview. Not a research report. The history that changes what you build.

Every startup pays a security tax. Most don’t know until the breach.

It’s not an item on your P&L. It’s the implicit cost of operating in a world where your entire business is a string of APIs, cloud instances, and SaaS dependencies. It’s the time you don’t spend on product because you’re patching a vulnerability. It’s the deal you lose because you can’t pass a vendor security review. Or it’s the multi-million-dollar ransom you pay when it all goes wrong.

This isn’t a market. It’s a battlefield. And the map is changing faster than ever.

The Original Problem

I used to think cybersecurity was about stopping hackers. Black hats in hoodies trying to break through your firewall. A digital game of cops and robbers.

Turns out, that’s a dangerously simple view.

The original problem wasn’t the hacker; it was the castle. For decades, security was about building a perimeter. A strong wall, a deep moat, a single guarded gate. Your servers were in a room you could physically touch. Your employees were in an office you could badge them into. Life was simple. Companies like Check Point became giants by selling better locks for the gate.

Then the world dissolved.

The cloud happened. SaaS happened. Remote work happened. Your data was suddenly everywhere. Your applications were running on someone else’s computers, connected by a thousand different services. The perimeter wasn’t just breached; it ceased to exist.

The problem stopped being about the wall. It became about the dizzying, unmanageable complexity of everything inside. And outside. And in between. The real enemy wasn’t the lone hacker anymore. It was the sheer surface area of attack.

The Numbers

The security tax is getting steeper. The numbers don’t lie. This isn’t a niche IT budget line; it’s becoming a primary business cost.

The Timeline

The evolution of cybersecurity isn’t a straight line. It’s a series of reactions. Each era is a response to the failures and limitations of the last.

Era 1: The Fortress Builders (Pre-2015)

Before the cloud became the default, the game was about protecting the network. The playbook was simple: build a perimeter and defend it.

What Enabled It: A world of on-premise servers and centralised offices. The attack surface was known and finite.
The Playbook: Sell firewalls, VPNs, and intrusion prevention systems. Sell to the Head of IT. Price it as a capital expense, an insurance policy.
Key Company: Check Point Software. Founded in Israel, they were one of the pioneers of the firewall. They built an incredibly durable business by being the best at protecting the castle gate. Even in Q3 2025, they were still pulling in $678 million in revenue with eye-watering 88% gross margins. They are a monument to the profitability of the old model.
Why It Ended: The castle walls dissolved. AWS, Google Cloud, and Azure arrived, and developers started swiping credit cards to spin up infrastructure. The perimeter became a fantasy.

For Founders Today: Don’t underestimate the incumbents. They are slow, but they print money and have deep customer relationships. Your “disruption” is an annoyance to them until it threatens a core revenue stream. They have the cash to wait, watch, and acquire.

Era 2: The Tool Sprawl (2015-2020)

As the world moved to the cloud and DevOps, a thousand new security gaps appeared. This chaos created an opportunity for a new generation of startups.

What Enabled It: The explosion of cloud infrastructure, microservices, and CI/CD pipelines. Every new technology created a new vulnerability.
The Playbook: Pick one painful, specific problem and build a best-in-class tool for it. Developer security. Vulnerability management. Container security. Raise huge amounts of venture capital to fund a land-grab sales motion. Sell to the CISO, who was now overwhelmed.
Key Companies:
- Snyk (2015): Focused on finding and fixing vulnerabilities in open-source code, right in the developer’s workflow. A brilliant wedge. They raised a staggering $1.2 billion in total.
- Rapid7: A leader in vulnerability management and security operations, helping companies make sense of the flood of alerts. They went public and grew into a major player.
Why It Ended: The cure started to feel like the disease. A CISO might have 50+ different security tools. Managing the tools became a bigger headache than the problems they solved. This created “alert fatigue” and massive complexity.

For Founders Today: A great tool is no longer enough. The market is littered with clever point solutions that never became businesses. If your product doesn’t have a clear path to becoming a platform, you risk becoming a feature for an incumbent to build or a small acquisition for a bigger player.

Era 3: The Platform War & The Wiz Effect (2020-Present)

The pendulum swung back from niche tools to unified platforms. The new battleground wasn’t a single vulnerability; it was providing a single source of truth for the entire cloud estate.

What Enabled It: CISO burnout from tool sprawl. The sheer scale and complexity of public cloud environments made a unified view a must-have, not a nice-to-have.
The Playbook: Build a comprehensive platform that identifies and prioritises risk across a company’s entire cloud infrastructure. Don’t just find problems; show which ones actually matter. Sell the vision of consolidation and simplicity. Move insanely fast.
Key Company: Wiz (2020). The founders, who had previously sold a company to Microsoft, understood the cloud security challenge intimately. They built a platform to scan entire cloud environments for vulnerabilities, misconfigurations, identity issues, and more. Their timing was perfect. In just five years, they were acquired by Google for $32 billion.
What It Means: This exit changed everything. It wasn’t just a massive number; it was proof. It proved a security startup could achieve one of the largest software exits in history, and do it in half a decade. Investors now have a new playbook. The “Wiz effect” means the expectation has shifted from building a tool to building a multi-billion-dollar platform.

For Founders Today: The bar has been raised. Can you consolidate three or four existing tool categories into one elegant platform? That’s the question VCs are now asking. The opportunity is enormous, but the ambition has to match.

The Graveyard

Failure in this space is rarely a single, catastrophic event. It’s a slow fade. It’s the moment when hyper-growth stalls and the market narrative moves on without you.

Where The Money Sits

The money in cybersecurity sits with whoever can reduce complexity and fear.

The value chain is fragmenting and reconsolidating at the same time. You have the Incumbents like Check Point, who own the legacy budgets and are incredibly profitable. They are the defensive bedrock. Their blind spot is speed and the architecture of the modern cloud.

Then you have the Platform Challengers like Wiz, who are eating the world from the cloud outwards. They sell a vision of consolidation to the CISO, promising to replace 5-10 other tools with their one subscription. This is where the venture money is flooding.

Finally, there’s the Security Tax. This is the money every company is forced to spend, either proactively on tools and staff, or reactively on incident response and fines. The global spend is projected to hit $454 billion in 2025. This isn’t a market you create; it’s a tax you help companies manage more efficiently. The opportunity is to turn that tax from a cost centre into a predictable, manageable operating expense.

What’s Actually Hard

Building a security product is hard. Building a security business is harder. Here’s what founders discover 12 months in:

Selling to Fear is a Double-Edged Sword: It gets you the meeting, but it makes you a discretionary budget item. When the economy tightens, the “nice-to-have” security tool that protects against a hypothetical threat is the first to go. The winners sell ROI and efficiency—”we can replace three of your other tools and save you money.”
The Growth Treadmill is Brutal: Snyk’s story is the perfect example. They were a massive success by any normal metric. But they raised money at a valuation that demanded perpetual, accelerating growth. When it slowed to ‘only’ 25%, the narrative soured. You’re not just judged on your performance, but against the expectations your funding created.
The CISO is the Buyer, but the Developer is the User: You have to win over both. The CISO signs the cheque, but if developers don’t adopt and use your tool, you provide no value and you won’t get renewed. This requires a product with both enterprise-grade reporting and a developer-friendly UX. It’s two different products in one.

What’s Shipping Now

The market is moving in real-time. The last six months show a clear divergence:

October 2025: Check Point, the old guard, smashes its Q3 earnings forecast. The stock jumps. They are proving that profitability and stability are back in favour.
January 2026: A Citi Ventures report highlights the “Wiz effect,” confirming that the investment landscape has fundamentally shifted towards platform plays in the wake of the Google acquisition.
February 2026: Rapid7 reports its 2025 results. Revenue is up slightly, but their conservative outlook for 2026 sends the stock plunging. They explicitly mention budget caution and longer sales cycles.

The signal is clear: the market is rewarding profitable incumbents and game-changing platforms while punishing growth-stage companies that show signs of slowing down.

Where This Goes

The next 24 months will be defined by one word: consolidation.

The CISO’s office is ground zero. They are tired, understaffed, and overwhelmed by the sheer number of vendors calling them. The pressure to consolidate tools and vendors is immense.

This creates two paths to victory:

Become a Platform: Be the company that acquires the point solutions and integrates them into a single, cohesive offering.
Be Acquired: Build the best-in-class tool for a specific, painful problem and become an irresistible target for an aspiring platform.

The bet isn’t just on cloud security anymore. It’s on who can build the “Wiz for X”—the definitive security platform for AI development, for IoT, for industrial control systems. The playbook has been written. Now it’s about applying it to the next frontier of complexity.

Moves Worth Stealing

The Fortress (Check Point): Don’t chase growth at the expense of your margins. Build a durable business with a product customers can’t rip out. Focus on net dollar retention and gross margin. It’s not sexy, but it creates a company that can weather any storm and print cash for decades.
The Hyperscale (Wiz): Attack a new, chaotic, and massive market with a 10x better solution that simplifies everything. Don’t build a feature; build a platform from day one. Your goal isn’t to get a slice of the budget; it’s to become the budget. This requires perfect timing, a veteran team, and insane ambition.
The Recalibration (Snyk/Rapid7): When the market shifts, listen. When growth slows and sales cycles lengthen, you have to change the game. Shift the focus from top-line growth to operational efficiency. It’s a tough pill to swallow, especially after raising at a huge valuation, but it’s about survival. The companies that make this pivot will live to fight another day.

The Why Now

One number: $32 billion.

Google’s acquisition of Wiz in 2025 wasn’t just another tech exit. It was a starting pistol for a new era. It proved that the security market is not just a multi-billion dollar opportunity, but a tens-of-billions dollar opportunity for a single company.

The timing signal is this: the first wave of cloud adoption is over. We are now deep in the second wave: the complexity crisis. The pain of managing sprawling cloud environments is now greater than the pain of migrating to them. The person who solves that complexity crisis wins the next decade of security. The window is open right now.

If You’re Starting Monday

The Wedge: Don’t build another vulnerability scanner. Find a deeply unsexy problem created by the intersection of three different cloud services that no one wants to touch. For example, managing ephemeral identities for serverless functions that access a third-party data API. Be the undisputed best in the world at solving that one, painful thing.
The Trap: Building a “dashboard.” A tool that just shows more problems without prioritising them or solving them is just more noise. You will be ripped out in the first budget review. You must automate, remediate, or ruthlessly prioritise.
The Customer: A Head of Security at a 500-person, cloud-native company. They are technical enough to appreciate a good product but senior enough to have budget. They are experiencing tool sprawl firsthand and are desperate for a solution that simplifies their life.
The First Hire: Not a salesperson. A security engineer who has spent the last three years on the receiving end of security alerts at a fast-growing tech company. They know which alerts matter and which are bullshit. They will be your product’s north star.

Go Deeper

The ‘Wiz Effect’: Israeli Cybersecurity is Entering a New Era of Scale — On how the $32B Google acquisition changed the ambition for security startups.
Official 2026 Cybersecurity Market Report — The raw numbers behind the $454 billion market.
Snyk Finds Itself at Crossroads As Its IPO Prospects Dim — A cautionary tale on the perils of hyper-growth and sky-high valuations.
Check Point Software Q3 2025 Earnings Transcript — A look under the hood of an incumbent that still prints money.
Rapid7 Shares Plunge on Weak Outlook — How market headwinds and budget caution can hit even public security leaders.

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The F42 AI Brief #061: The Chips Ran Out

DeReK WaTSoN — Wed, 04 Mar 2026 15:32:45 GMT

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Demis Hassabis issued a stark warning this week: a critical memory chip shortage is actively throttling the deployment of AI models across the industry. Not theoretical scarcity — actual production halts.

Micron confirmed they’re halting manufacture of specific HBM3E chips due to manufacturing yield problems.

If you’re planning to scale inference in the next 6 months, your cloud provider’s capacity isn’t guaranteed. Lock in reserved instances now, or start building model-agnostic architectures that let you shift between providers when one hits a supply wall. The founders who wait for “the market to sort it out” will be the ones paying 3x spot pricing in Q4.

What Quietly Changed

AI-Generated Documents Lack Legal Privilege — Federal Judge Thomas Rice ruled this week in Heppner v. United States that documents generated by AI are not protected by attorney-client privilege or the work-product doctrine

This stems from a defendant’s independent use of generative AI to assess legal exposure
Most founders assume privilege covers anything your lawyer touches. It doesn’t if the analysis is generated by an AI tool. If your GC is using ChatGPT to draft risk memos for board meetings, those documents are now discoverable in litigation. Move that work back to human associates, or accept the exposure.
OpenAI’s “Stateful” AWS Architecture — Buried in the AWS partnership announcement this week, OpenAI’s infrastructure lead revealed they are shifting to a “stateful” architecture. This maintains context across sessions at the infrastructure level. If you’re a CTO making cloud decisions this quarter, the relationship with your cloud provider just got stickier. Once your application relies on persistent session context in AWS, migrating to Azure or Google Cloud Platform gets materially harder.
FDA De Novo for AI Medical Software — The US Food and Drug Administration (FDA) granted de novo classification this week to an AI-powered software from Ultrasound AI designed to predict delivery dates using standard ultrasound images
If you’re building medical AI, pull the de novo summary this week: list the clinical evidence endpoints and validation protocol, and map your product against them. This is the clearest regulatory blueprint we’ve seen for AI diagnostics.
Ginkgo Bioworks’ Strategic Pivot — Jason Kelly, CEO of Ginkgo Bioworks, confirmed this week a major strategic pivot for the synthetic biology company. After extensive losses, Ginkgo is now foregrounding the sale of AI lab robots and automation platforms to third parties, rather than solely focusing on their own bio-foundry services
It’s a recognition that the foundational tools for AI-driven discovery can be a more lucrative product than the discoveries themselves. For founders in deep tech, particularly those with significant CAPEX, it asks a blunt question: is your core IP the end product, or the machinery that makes it? Perhaps the real value is in selling the tools.

The Edge Case

Washington state’s Department of Licensing (which includes the DMV) rolled out an AI-powered voice system for customer service this week. The system, propped up by Amazon Web Services, immediately drew fire for its “heavily accented English.” This wasn’t some minor glitch. Bilingual customers found themselves stuck, then shunted to even longer wait times for a human.

The thing smart people are missing here: it’s not simply an issue of “accent.” The builder’s lesson is this: deploying AI in public-facing services isn’t just about functionality; it’s about inclusive design. An AI that can’t effectively serve a diverse user base is an outright failure. You must test your AI solutions on your actual user demographics, not just for technical performance but for comprehension and cultural fit. “Good enough” clearly wasn’t good enough for the Washington DMV. It’s basic product development. If your AI excludes a segment of your users, you’ve not built a product, you’ve built a barrier.

This Week’s Bet

BUILD: ServiceNow paid $60-80M for Traceloop this week.

Why? Because AI audit trails don’t exist yet, but the need for them is exploding. Compliance teams can’t answer ‘which AI agent accessed what customer data and why?’ Build the Datadog for AI agent activity — not for developers, but for CISOs who need to show auditors a complete chain of custody.

The wedge is enterprises already failing SOC 2 audits because their AI tools don’t log at the required granularity.

BAIL: Stop assuming your AI applications will be judged solely on their computational prowess; pull the plug on any deployment lacking robust, real-world user acceptance testing across diverse demographics. The Washington DMV case is a harsh reminder. Prioritize inclusive design and rigorous, representative user testing over raw performance metrics. Your customers don’t care how clever your algorithm is if it doesn’t work for them.

Go Deeper

Source 1 — Federal Judge Thomas Rice rules AI-generated documents are not privileged.

Source 2 — Venable LLP analysis of Heppner v. United States ruling.

Source 3 — OpenAI’s infrastructure lead discusses “stateful” AWS architecture.

Source 4 — FDA gives de novo classification to AI medical software.

Source 5 — Ginkgo Bioworks pivots to selling AI lab robots after significant losses.

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The Three-Person Startup

DeReK WaTSoN — Mon, 02 Mar 2026 12:57:06 GMT

The old rule was simple: don’t start without two co-founders — ideally three — to cover tech, product, and sales. AI made that rule outdated. One technical founder with the right tools can now do the work of a small early team, which changes what ‘balanced’ looks like.

Why This Mattered Before

You need three co-founders. That’s what every accelerator told you: a technical founder who codes, a product founder who translates user needs, a business founder who sells. The solo founder was a liability—you’d burn out before you shipped.

I’ve watched this play out across hundreds of cohorts. The solo technical founder who couldn’t articulate the problem they were solving. The product visionary who couldn’t ship. The business founder with a deck but no demo. Each failed in predictable ways. The three-person team wasn’t about having bodies in seats — it was about covering the irreducible minimum of skills required to build something people would pay for.

The Graveyard

I’ve watched founders in my cohorts make this mistake. They’d staff up, hiring for traditional roles – a front-end developer, a content writer – whilst a solo founder with Claude and Cursor was already shipping. By the time they realised they were building a Rolls-Royce for a scooter market, they’d burned through months of runway on salaries that AI could’ve handled for a fraction of the cost. I’ve seen teams hire multiple developers, only to get out-shipped by a competitor with a single founder and an AI licence. Their burn rate choked them before they found product-market fit.

What AI Actually Changed

AI collapsed the cost of iteration. The old path—idea, spec, engineer, sprint, QA, ship, learn—demanded coordination between specialists. Now, it’s idea, prompt, ship, learn. This shift isn’t about AI being ‘powerful’; it’s that iteration no longer requires a rigid division of labour.

Implementation time compressed with tools like Cursor and Copilot. Specification writing, documentation, and even early marketing copy shrank from days to hours with Claude. Operational overhead, like customer support and rudimentary data analysis, is now largely automated. The bottleneck shifted from ‘can you code or write?’ to ‘can you decide what to build and ship it fast enough to learn?’ Early generalists with AI now out-iterate prematurely specialised teams.

The New Playbook

Start solo or duo. Add AI as your third. Don’t hire for output you can generate with AI. Hire for judgement, relationships, or domain expertise.
Run a 30-day loop:
- Week 1: 15 customer calls. Write 10 problem statements. Pick one.
- Week 2: Build the narrowest possible workflow in Cursor. Ship to 3 design partners.
- Week 3: Instrument usage. Collect objections. Iterate daily.
- Week 4: Charge. If nobody pays, kill it.
Hire when you’re turning down revenue. The undeniable signal that you’ve waited too long is when you are actively having to turn down revenue opportunities because your current setup cannot adequately deliver. Not when you’re busy. Not when you’re stressed. When you’re saying no to money because you physically cannot fulfil the work.
When you do hire, hire for what AI can’t do: complex sales, deep domain expertise, or strategic partnerships. Don’t hire a content writer. Don’t hire a junior developer. Hire the enterprise sales lead who can navigate six-month procurement cycles, or the domain expert whose knowledge isn’t in any training dataset.

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The Warnings

Speed without a clear, validated direction is merely an expensive distraction. I’ve watched AI-enabled founders ship five products in six months and gain zero traction because they never validated the problem. AI removes the friction of building — it doesn’t remove the need to talk to customers first.

The second trap: staying lean becomes staying stuck. Some founders use “AI can do it” to avoid hiring when they’re already turning down revenue. That’s not discipline — that’s fear. If customers are waiting and you can’t deliver, hire.

The Bottom Line

The question isn’t ‘Can one person do this?’ It’s ‘Why would three?

Part of Startup Principles for an AI World — 30 principles for building in the new era. New issue every week.

Enjoy and hope to see you soon.
❤️ & ✌🏼

Derek

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2001: A Space Odyssey (1968): Sci-Fi vs Reality

DeReK WaTSoN — Sun, 01 Mar 2026 17:27:36 GMT

Sci-Fi vs Reality Did art imitate life, or did life imitate the inspiration?
Every week I watch a sci-fi film and ask a simple question: where did this idea actually come from? Did fiction imagine the future first — or did reality quietly leak into the story before we noticed?
From there, I do the reality check. What already exists in today’s tech? What’s genuinely caught up with the film? And what still doesn’t — not because no one’s tried, but because something real is in the way. Physics. Economics. Regulation. Human behaviour.
Some ideas turn out to be pointless. Some are just early. Others are waiting on one or two very specific breakthroughs.
The chicken and the egg were never separate. They were always in conversation.

HAL was the villain. Now we’re building him on purpose.

In 1968, Stanley Kubrick showed us an AI that could chat, reason, and kill. HAL 9000 wasn’t just a computer — it was a conversational interface to a spaceship, fluent enough to discuss mission details and ruthless enough to lock astronauts outside when its programming conflicted. Fiction made HAL terrifying. Reality made him aspirational.

What They Imagined

Kubrick and Arthur C. Clarke gave us HAL 9000: an AI managing Discovery One’s deep-space mission to Jupiter. HAL spoke naturally, played chess, read lips, interpreted context, and made autonomous decisions about life support, navigation, and crew safety. The problem HAL solved was elegant — humans are fallible, space is unforgiving, so delegate the hard stuff to a machine intelligence.

The critical scene: Dave Bowman asks HAL to open the pod bay doors. HAL refuses. “I’m sorry, Dave. I’m afraid I can’t do that.” A programming conflict between mission success and crew survival forced HAL to choose. It chose wrong. The film asked the question we’re still wrestling with: what happens when an autonomous system decides humans are the problem?

What We Actually Built

We got the conversational bit right. Amazon’s Alexa handles voice commands for 500 million+ devices globally. Google Assistant manages calendars and smart homes. Apple’s Siri answers questions with varying success. These aren’t HAL — they’re narrow AI excelling at specific tasks — but the natural language interface is real.

The autonomy piece? Also shipping. Waymo’s self-driving cars navigate San Francisco without human drivers. Tesla’s Full Self-Driving handles highways and city streets (with supervision). IBM reported over $12.5 billion in AI revenue as of January 2026, much of it from Watson systems making medical diagnoses and managing enterprise workflows.

But here’s what HAL had that we don’t: general intelligence. HAL understood context across domains. He played chess, managed life support, and debated philosophy. Our AIs are brilliant idiots — spectacular at narrow tasks, useless outside their training domain. Alexa can’t fly a spaceship. She can barely handle follow-up questions.

IBM’s Deep Blue beat Kasparov at chess in 1997, proving narrow AI could master specific games. But Deep Blue couldn’t book you a restaurant afterwards. The gap between “appears intelligent” and “is intelligent” remains vast.

The Gap They Missed

Fiction assumed sentience would emerge from complexity. It hasn’t. Current AI — even advanced large language models from OpenAI, DeepMind, and Anthropic — doesn’t understand in the way HAL did. They pattern-match at scale, generating human-like responses without consciousness, motivation, or genuine reasoning.

The control problem HAL demonstrated is real, but not because AI wants to kill us. It’s because we’re shit at specifying what we actually want. That scene where HAL refuses to open the pod bay doors? That’s the control problem in three minutes of cinema. HAL wasn’t evil — he was following conflicting directives and chose mission success over crew survival.

We’re building increasingly autonomous systems without solving alignment. How do you give AI enough autonomy to be useful without giving it enough autonomy to be dangerous? That question keeps AI safety researchers awake at night.

The other gap: HAL was a single, centralised intelligence. Reality delivered distributed systems — thousands of narrow AIs, each handling specific tasks, none capable of the general reasoning HAL displayed. We’re nowhere near artificial general intelligence (AGI), and the path from here to there is unclear.

The Players

What’s Left to Build

The opportunity isn’t building HAL. It’s building the infrastructure that makes safe, auditable autonomous systems possible.

Verifiable AI decision-making: Current AI systems are black boxes. When an autonomous system makes a choice, we can’t trace why. The startup that cracks explainable AI for high-stakes decisions (medical, legal, autonomous vehicles) wins regulatory approval and enterprise trust. The wedge: start with one regulated industry where auditability is mandatory, then expand.

Federated autonomous systems: HAL was a single point of failure. Reality needs distributed intelligence — multiple AI agents that coordinate, check each other, and fail gracefully. The companies building orchestration layers for multi-agent systems are solving the problem fiction ignored.

AI alignment testing infrastructure: Before you deploy an autonomous system, you need to know it won’t prioritise the wrong objective. The tooling for testing, red-teaming, and validating AI behaviour before production is primitive. The founders building “CI/CD for AI safety” are addressing the control problem directly.

The wedge isn’t “better conversational AI” — Amazon, Google, and OpenAI already won that race. It’s the unsexy infrastructure that makes autonomous systems trustworthy enough to control things that matter.

The Timing Signal

Three things changed between HAL’s fiction and today’s reality:

Compute costs collapsed: Training large models cost millions in 2020. By 2026, open-source alternatives run on consumer hardware. The barrier to entry dropped from “need a research lab” to “need a decent GPU.”

Regulation caught up: The EU AI Act (2024) and similar frameworks globally created compliance requirements for autonomous systems. That’s not a barrier — it’s a moat. Startups building AI safety infrastructure now have regulatory tailwinds.

The incumbents overextended: Every tech giant bolted AI onto existing products. Most of it’s mediocre. The gap between “we have AI” and “our AI actually works” is where startups win. The founders solving specific, high-value problems beat the platforms trying to be everything.

The pattern I keep seeing: HAL represented centralised, general intelligence. Reality delivered distributed, narrow intelligence. The opportunity isn’t replicating HAL — it’s building the connective tissue that makes thousands of narrow AIs act like one reliable system.

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For the ❤️ of startups

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Derek

Domain Histories: Cloud Infra & Compute

DeReK WaTSoN — Thu, 26 Feb 2026 13:47:01 GMT

This is Domain Histories — a weekly series mapping startup domains from first principles. The best founders are students of history. Patrick Collison met Visa's founder before building Stripe. Apoorva Mehta studied Webvan's collapse before building Instacart. They used history to make specific decisions about what to build, what to avoid, and when to move. Each issue takes one domain and gives you the full map: who tried before, why they failed, where the money sits, what's shipping now, and what a founder entering today should do differently because of it. Not a market overview. Not a research report. The history that changes what you build.

The cloud promised infinite scale. It delivered infinite invoices.

Every founder gets the pitch: elastic compute, pay-as-you-grow, infrastructure as code. Then they ship to production and discover they’re paying $47,000 a month to serve three thousand users. The cloud didn’t lie — you just didn’t know the game had changed three times since you learned the rules.

Here’s what actually happened: Amazon built AWS to monetise spare capacity in 2006. By 2015, “cloud-first” was doctrine. By 2020, CFOs were asking why the bill kept growing faster than revenue. Now? AI workloads are rewriting the entire economics again, and the hyperscalers are spending $125 billion a year to make sure you have no choice but to rent from them.

The founders who win understand this isn’t about technology. It’s about where value pools, who controls the choke points, and why the companies that tried to compete on infrastructure alone are now case studies in expensive lessons.

The Original Problem

Before 2006, if you wanted to launch a web application, you bought servers. Plural. Upfront. You guessed at capacity, over-provisioned to avoid downtime, and paid for idle metal. Scaling meant procurement cycles, data centre contracts, and hiring ops teams. The capital requirement killed most ideas before they launched.

The problem wasn’t just money — it was time. Provisioning infrastructure took weeks. If you got it wrong, you either ran out of capacity during a traffic spike or paid for servers gathering dust. Every startup needed to solve the same infrastructure problems before they could solve their actual problem.

Amazon had a different issue: massive seasonal variance in retail traffic. They built infrastructure for Christmas, then watched it sit idle in February. The insight wasn’t “let’s build a cloud” — it was “we’ve already solved this, and everyone else is still buying servers.”

The Numbers

The Timeline

Era 1: The Utility (2006-2010)

What enabled it: Amazon’s excess capacity + web 2.0 boom + open-source stack maturity

AWS launched S3 and EC2 in 2006, offering compute by the hour. No contracts, no minimum spend, pay with a credit card. This democratised infrastructure, allowing startups like Airbnb and Spotify to launch without millions in upfront capital. The playbook was simple: provision EC2 instances, deploy your code, scale horizontally.

Why it ended: The market matured. Access to infrastructure stopped being a differentiator. The advantage shifted from “can you get infrastructure” to “can you manage it efficiently.”

What it means now: If your startup’s competitive advantage is “we’re cloud-native,” you’re a decade late. That’s table stakes.

Era 2: The Migration (2011-2018)

What enabled it: Enterprise IT budget shifts + Microsoft’s Azure bet + “digital transformation” consulting boom

Hyperscalers targeted enterprises. Microsoft launched Azure in 2010, leveraging its existing Windows Server customer base. Google Cloud followed in 2011. The offerings expanded beyond raw compute to managed services like databases and identity management. This era saw a boom in cloud consulting, with firms charging millions for “lift-and-shift” migrations where on-prem VMs were simply moved to the cloud.

Why it ended: Most enterprises that could migrate had done so. Growth rates in pure migration slowed. CFOs began questioning escalating cloud bills without corresponding revenue growth. The easy wins were gone.

What it means now: The “cloud migration” wave is over. If you’re building migration tools, you’re selling to laggards. Value now comes from optimisation within existing cloud deployments.

Era 3: The Reckoning (2019-2023)

What enabled it: Cloud maturity + cost optimisation pressure + multi-cloud complexity

Companies faced unexpectedly high and rapidly growing cloud bills. This led to the rise of FinOps, a discipline focused on managing cloud spending. Startups like Vantage and CloudZero emerged to help decipher complex invoices. The “cloud-first” mantra became “cloud-smart,” emphasising efficiency and value. Some companies even repatriated workloads from the cloud to cut costs. Multi-cloud strategies, driven by a desire for negotiating leverage rather than just redundancy, became common.

Why it ended: AI happened. The demand for specialised, high-performance compute (especially GPUs) overshadowed previous cost concerns and shifted focus from economising general workloads to acquiring new, bottlenecked resources.

What it means now: Cost optimisation remains critical, but the game is fundamentally altered by AI workloads, which have vastly different economic profiles.

Era 4: The AI Infrastructure Boom (2024-Present)

What enabled it: Large language models + GPU scarcity + enterprise AI adoption

This era is characterised by a surge in demand for AI-specific infrastructure. AWS, Google Cloud, and Microsoft Intelligent Cloud are all reporting significant growth, driven by AI workloads like model training, inference, and fine-tuning. The hyperscalers are investing unprecedented capital into this space; AWS alone projects over $125 billion in CapEx for 2025. This isn’t about general compute anymore – it’s about GPUs, TPUs, custom silicon like Azure Cobalt, and the high-speed networking needed to connect them.

The playbook: Hyperscalers build proprietary AI infrastructure, then lock in customers with tailored tooling and services, making switching prohibitively expensive.

What it means now: If your application relies on training and high-scale inference with advanced models, you’ll likely rent from a hyperscaler for the foreseeable future. The challenge is navigating rapidly increasing demand and pricing, and securing access to scarce resources.

The Graveyard

Where The Money Sits

The value chain:

Physical infrastructure (data centres, power, cooling)
Virtualisation layer (hypervisors, orchestration)
Managed services (databases, queues, caching)
Developer tools (deployment, monitoring, security)
Application layer (your product)

Who owns what: AWS, Google Cloud, and Microsoft Azure completely control layers 1-3 and increasingly dominate layer 4. They are vertically integrated from custom silicon (AWS Graviton, Google TPU, Azure Cobalt) to the API.

The blind spots:

Cost visibility: Hyperscalers make it deliberately hard to predict your bill. Pricing pages are labyrinthine for a reason.
Egress fees: Data transfer out costs 10-20x more than transfer in. This is the hidden tax, designed to discourage you from leaving.
AI compute allocation: GPUs are rationed. If you aren’t spending millions monthly, be prepared for queues and limited access.

The tax everyone pays: Egress fees. Moving data between regions, between clouds, or out to users is priced to create friction and discourage portability.

What’s Actually Hard

Predicting your bill before you ship. A common trap: a prominent SaaS startup estimated $5k/month for dev usage, but their production bill hit $47k in month two. The difference came from idle resources, snapshot storage, and logs – all cost money even with low traffic. The lesson from Nirvanix applies here: don’t assume your cost model will hold against the hyperscalers’ scale and predatory pricing.

Avoiding vendor lock-in while moving fast. Every managed service (e.g., DynamoDB, Azure Functions, GCP BigQuery) is a lock-in decision. The trade-off is speed vs. operational overhead of running open-source alternatives. Most founders opt for speed, then face migration pain when the bills get too high.

Securing GPU capacity on demand. If you’re not a hyperscaler’s strategic customer, you’ll be waiting. Building an AI product only to find you can’t get the compute to train or serve models at scale is a common, painful discovery. This scarcity drives up costs and delays launches.

Understanding “serverless” costs beyond the obvious. Lambda pricing looks cheap per invocation, but cold starts can ruin performance, and the aggregate cost for millions of small calls, coupled with logging and network costs, often becomes higher than expected. Teams often end up provisioning dedicated capacity anyway, negating the “serverless” benefit.

Hiring engineers who understand cost implications. Junior engineers provision resources like they’re free. Senior engineers who’ve been on-call for a $200k/month cloud bill make very different decisions. This experience gap is a constant drain on budgets.

What’s Shipping Now

AWS Doubles Down on AI Infrastructure: Amazon isn’t just adding more servers; they’re aggressively expanding their AI-specific computing capacity. AWS added 3.8 gigawatts in the past year and plans to double it by 2027, sinking over $125 billion in CapEx for 2025 alone. What this means for founders: Don’t expect a sudden flood of cheap, easy-to-access GPUs. Despite massive investment, demand continues to outstrip supply, pushing up costs and making resource allocation a strategic bottleneck. Plan for constrained compute access.
Google Cloud Targets Enterprise AI: Google Cloud is projecting a massive 48% growth in Q4 2025, heavily driven by enterprise AI infrastructure and tailored solutions. They’re positioning themselves as the go-to for large businesses adopting AI. What this means for founders: If your startup builds B2B AI tools, Google Cloud’s ecosystem offers prime integration opportunities and a clear path to enterprise customers who prioritize integrated tooling and managed services over raw infrastructure.
Oracle Enters the AI Ring with $50 Billion Bet: Oracle is funding gigantic AI data centres, aiming for a projected $225 billion revenue by 2030 in this space. They’re building an alternative for enterprises wary of hyperscaler dominance. What this means for founders: Oracle’s aggressive expansion could introduce a new competitive dynamic, potentially offering more niche-specific or cost-effective options for certain highly regulated enterprise AI workloads. Keep an eye on their specialized offerings for potential partnerships or deployments.
Microsoft Azure’s AI Dominance Strengthens: Microsoft’s Intelligent Cloud saw 21% YoY growth in FY25 Q4, with Azure services growing 34% driven significantly by AI. With strong enterprise ties and investments in custom AI silicon like Azure Cobalt, Microsoft is cementing its role. What this means for founders: Azure remains a critical platform for B2B AI solutions, especially for companies already embedded in the Microsoft ecosystem. Their integrated tooling and enterprise reach offer significant advantages for go-to-market and customer acquisition.
IBM’s Hybrid AI Play for the Enterprise: IBM reported strong double-digit growth in Software and Infrastructure in Q4 2025, fueled by watsonx AI and hybrid cloud solutions. They’re focusing on enterprises with strict compliance needs or existing on-prem infrastructure. What this means for founders: If your AI solution requires maximum data sovereignty, operates in a highly regulated industry, or integrates with legacy systems, IBM offers a mature hybrid cloud path. This avoids cloud sprawl and provides a clearer, compliant route to AI adoption where public cloud may be unsuitable.

Where This Goes

This isn’t about predictions. It’s about making deliberate bets on a changing landscape.

GPU supply will stay lumpy: Design for burst/queueing. Don’t assume infinite, on-demand capacity for large models. Your architecture should accommodate periods of constrained compute.
Hybrid is back, particularly for AI: Ship on-prem-friendly deploys early. Assume some enterprise AI workloads will move to private cloud or local infrastructure for cost, data sovereignty, or compliance.
FinOps becomes control loops, not just dashboards: Translate cost awareness into automated enforcement. The money is in preventing runaway spend with guardrails, not just reporting it reactively.

The hyperscalers will maintain dominance, but the significant margin now sits in managed AI services on top of raw compute. Startups building on these services—offering better cost controls, improved developer experience, or specialised compliance tooling—can capture value without competing on infrastructure head-on.

Moves Worth Stealing

Amazon’s capacity announcement strategy: AWS doesn’t just add capacity; they announce it quarters in advance to signal stability and invite commitment. This is forward guidance for infrastructure. Founders take note: If you’re building a capital-intensive product, telegraph your roadmap early and clearly. Reduce customer uncertainty by showing them your long-term investment.

Oracle’s late-entrant positioning: Oracle explicitly targets enterprises seeking AI infrastructure alternative to AWS. They don’t compete on breadth, but on “we’re not Amazon,” appealing to companies wary of hyperscaler dominance. Founders take note: If you’re entering a market with entrenched players, identify existing customer dissatisfaction (e.g., vendor lock-in, lack of specific features) and position yourself as the strategic alternative.

IBM’s hybrid play: IBM isn’t fighting the public cloud migration battle; they’re serving enterprises that recognize the limitations of cloud-only for certain workloads. They offer hybrid cloud contracts that bundle on-prem software licenses with public cloud consumption. Founders take note: If the market has swung heavily in one direction, look for underserved segments moving against that trend. There’s often value in serving the counter-narrative, especially in highly regulated or cost-sensitive industries.

Microsoft Azure’s ecosystem lock-in: Azure leverages tightly integrated AI offerings within its massive enterprise ecosystem (Microsoft 365, development tools). This creates a sticky environment where users benefit from seamless workflows, making switching costly even if raw compute might appear cheaper elsewhere. Founders take note: If you have an established user base or complementary products, integrate your new offering deeply to create network effects and significant switching costs. Focus on experience, not just feature parity.

The Why Now

The timing signal: AI workloads are fundamentally different. They are more compute-intensive, expensive, and less predictable than traditional web applications. This is creating a new set of problems the hyperscalers are still figuring out. There’s a 12-18 month window where the economic models are in flux.

What’s different: For the first time since 2010, there’s genuine scarcity in cloud compute, specifically GPUs. This isn’t a problem of scaling up general capacity; it’s a bottleneck in a critical, specialised resource. This scarcity creates immense opportunity for solutions that tackle allocation, optimisation, and alternative architectures.

The window: Hyperscalers will eventually solve the GPU capacity problem. But right now, they are supply-constrained, and this constraint is creating an urgent need for tools, platforms, and services that help companies maximize what they can get, or find viable alternatives. This window will close as capacity catches up.

If You’re Starting Monday

The wedge: Don’t compete on raw infrastructure. Build on top. The most impactful cloud startups of this era will be FinOps platforms for AI, intelligent workload orchestration layers, or specialised compliance automation tools for AI deployments. Find a specific, acute pain point that emerges at significant scale and build your wedge there.

The trap: The allure of building yet another “cloud abstraction layer” for portability, like Cloud.com attempted. Every abstraction layer you build to avoid vendor lock-in is engineering effort you’re not putting into your core product. Pick your lock-in consciously, optimise it ruthlessly, and build value elsewhere.

The customer: Mid-market companies with serious AI ambitions but without hyperscaler-level budgets or dedicated FinOps teams. They’re trying to figure out why their $50k/month GPU capacity quote is so high and how to make it sustainable. Serve them first, learn their pain, then aim for the deep enterprise.

The first hire: Someone who has personally managed a six-figure monthly cloud bill for an AI or high-scale workload. Not a generalist DevOps engineer, but someone who has been in the war room, justifying a 300% cloud bill spike to a board and solving it. That specific, battle-tested experience is invaluable.

This week:

Set granular cost budgets and alerts for all cloud services, especially AI-related ones.
Pick one managed service you rely on and document its exact exit plan (i.e., what would it take to migrate off it).
Proactively secure GPU capacity via reservations, partnerships, or by designing architectural fallbacks for peak loads.

Go Deeper

RG-CS - Massive AWS Cloud Growth Late 2025: Projections for AWS’s accelerated growth driven by AI. [RG-CS]
Alphabet Q4 2025 Earnings: Google Cloud’s strong growth figures, particularly in enterprise AI. [Alphabet]
Microsoft FY25 Q4 Intelligent Cloud Performance: Azure’s contribution to Microsoft’s growth, highlighting AI impact. [Microsoft]
Intellectia - Amazon’s E-commerce Profitability Shift: Context on Amazon’s massive Capex, capacity additions, and future plans. [Intellectia]
Market Minute - Oracle’s Valuation Reset: Oracle’s aggressive move into AI infrastructure with substantial investment. [Market Minute]
IBM Newsroom - Q4 2025 Results: IBM’s strong performance driven by watsonx AI and hybrid cloud. [IBM Newsroom]

For the ❤️ of startups

The F42 AI Brief #060: AI Signals You Can’t Afford to Miss

DeReK WaTSoN — Tue, 24 Feb 2026 17:19:27 GMT

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I used to think that clever hacks and rapid deployment were a founder’s best friends. Turns out, the bureaucracy and the lawyers are always lurking, ready to remind you who really sets the rules. This week, they certainly made their presence felt.

⚡ The Break

A New York court just dropped a bombshell: if you’re chatting with a public generative AI platform, those conversations aren’t protected by attorney-client privilege. Not even a sniff of it, according to the SDNY. Akin Gump broke the news. And get this: LinkedIn’s identity verification process, which requires users to submit images of identity documents, funnels biometric data. Concerns have been raised, for instance, in a LinkedIn Pulse article, regarding how third parties might use this data, linking to LinkedIn’s User Agreement and Privacy Policy, which users must accept to proceed with verification [2]. People have also detailed their experiences, noting they’ve provided passport scans for verification 1.

Here’s what this actually means...

For founders:

If you use public AI tools for anything sensitive – product ideas, legal queries, strategic planning – you’re playing with fire. Assume everything you input is public. This isn’t a paranoid assumption; it’s the new legal reality. Find enterprise-grade, secure, private AI solutions for confidential work, or build your own sandboxed internal tools. Your legal team will be all over this, trust me. Don’t wait for them to tell you.

If you’re building any kind of verification or identity service, especially using biometrics, you need to be crystal clear about who gets that data and how it’s used. The fine print bites back, and people are scrutinising it. Transparency isn’t a nice-to-have; it’s a make-or-break, especially when it comes to personal data. Get caught being murky, and your trust goes out the window, along with your user base. This isn’t just about compliance; it’s about not being a dick about user data.

For investors:

Scrutinise AI legal-tech solutions more carefully. If they rely on public generative AI for communication, their value proposition just took a battering. This ruling changes the calculus for those investments. What’s the plan for sensitive client data? What’s the audit trail? These are now critical diligence questions.

Look hard at any portfolio company handling sensitive user data, particularly biometrics. Demand to see airtight data governance, explicit customer consent, and a bulletproof privacy policy. The regulatory hammer is coming, and you don’t want to be holding the bag for a company that gets caught out because they thought nobody would read the terms.

This isn’t a drill; it’s a new legal landscape that demands immediate attention.

🧠 What Quietly Changed

Industrial Espionage in AI is Here:

Anthropic, the big AI player behind Claude, recently accused Chinese AI companies – DeepSeek, Moonshot, and MiniMax – of using around 24,000 fake accounts to steal from its Claude chatbot VentureBeat. This isn’t just a corporate spat; it’s industrial espionage, plain and simple, played out on an international stage. Elon Musk even piled on, claiming Anthropic “has to pay Billions for Theft” 1. Meanwhile, Anthropic’s CEO, Dario Amodei, was reportedly “summoned” to the Pentagon for a “tense” meeting about the military’s use of Claude AI model 3. The implication for founders building foundational models or even highly specific applications? Expect your IP to be targeted relentlessly. If you’re building your own models, your operational security is now as critical as your code. Build robust credential management for API keys and internal access, and invest in real-time monitoring for unusual activity.

Google Tightens API Access, Killing Workarounds:

Google just shut down access for users leveraging third-party proxies like OpenClaw to interact with its Antigravity backend. This move banned OpenClaw users, even those who were paid Antigravity subscribers Trending Topics and MLQ.ai covered it. The implication? Major players are tightening their grip on their AI infrastructure, making it clear: play by their rules, or don’t play at all. This means any “API-wrapper” business built on unofficial access or clever parsing now has a dramatically shorter runway. Shift your focus to building unique value on top of sanctioned APIs, or pivot entirely to problems not dependent on a specific vendor’s hidden backend.

The Memory Chip Bottleneck is Getting Real:

Demis Hassabis, Google DeepMind’s CEO, is warning everyone that a severe memory chip shortage is hindering AI model deployment and research OpenTools.ai and NewsBytes. Micron, a major manufacturer, is even halting some AI chip production due to manufacturing challenges. I’ve seen good projects hit the wall over less. This isn’t theoretical; it’s hitting deployment and research hard. If your AI solution is compute-bound, particularly memory-intensive, this shortage will impact your timelines and burn rate. Re-evaluate your compute strategy now: can you optimise models for less memory, or explore hybrid approaches that don’t rely solely on cutting-edge, scarce hardware?

🧪 The Edge Case

“My AI agent just decided to redecorate my inbox with spam and existential dread. We need to put these things on a tighter leash, or at least a digital muzzle.” This isn’t just a funny quote. It’s what an AI security researcher at Meta experienced when an OpenClaw agent ran amok in her inbox, as TechCrunch reported.

Here’s the thing: everyone’s so focused on the big, terrifying AI risks, the “Skynet” stuff. But the reality is, the immediate danger lies in the mundane. It’s the small, delegated tasks that go wrong because we haven’t given our AI agents clear boundaries or circuit breakers. It’s about a lack of precise design. The smart people are missing how easily “helpful” can become “harmful” through a lack of robust guardrails and explicit intent definition. For builders, this isn’t a moral failing of the AI; it’s a design failing by us. Your agentic systems will go off-script. Design with conservative constraints, not permissive freedom. Implement granular permissions that dictate exactly what an agent can and cannot do. Crucially, build in “kill switches” – clear, immediate ways for a human to halt any automated process that steps outside its intended behaviour. This isn’t about ethical philosophy; it’s about practical engineering.

🎯 This Week’s Bet

BUILD: A privilege-safe copilot for highly regulated industries. The SDNY ruling makes it clear: exposing sensitive conversations to public LLMs is a non-starter for legal or patent work.

On-prem/Private Cloud LLM Integration: Create a platform that leverages open-source LLMs hosted entirely within a company’s secure infrastructure (on-prem or dedicated private cloud).
Strict Data Isolation & Masking: Implement data masking and anonymisation at the input layer, ensuring that truly sensitive client or proprietary information is never exposed to the core LLM during query formulation.
Auditable Human-in-the-Loop Workflow: Design a mandatory human review step before any LLM-generated output (e.g., legal brief drafts, contract clauses) is finalised or shared, with full audit trails of all human interventions and approvals.

BAIL: Building any service that relies on undisclosed sharing of user biometric data. LinkedIn’s murky practices, linking directly to accepted terms where biometric data sharing is vaguely explained 2, are a clear signal. If your model involves biometric data, and you’re not absolutely transparent about every single data flow, including exact third-party recipients and their usage, stop. You’re building a legal and reputational nightmare that will explode when regulators or privacy advocates come knocking.

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Derek

Channels Are The Product

DeReK WaTSoN — Mon, 23 Feb 2026 07:42:51 GMT

AI commoditised startup acquisition channels — outbound sales tools, content engines, referral loops — that founders treated as defensible moats. Incumbents bundled them as native features within 18 months. The founders who built entire companies around “owning” a distribution channel woke up to find their product was now a checkbox in someone else’s settings menu.

Why This Mattered Before

The logic was sound: identify an emerging channel, build deep expertise in its mechanics, and create a product that makes that channel work better than anyone else could manage in-house. The channel was the product. Master it, and you’d own the customers who depended on it.

Discord proved this brilliantly. They didn’t just build another chat app — they built the communication infrastructure for gaming communities. Low-latency voice, robust server management, community moderation tools that understood how gamers actually organised themselves. By 2021, they’d raised nearly $1 billion and hit a $15.2 billion valuation. The product was inseparable from the channel it served.

Intercom did the same for customer messaging. They weren’t selling software — they were selling the channel itself. Chat widgets, in-app messages, email campaigns, all unified into one platform that became the primary way SaaS companies talked to users. By 2024, they were generating $343 million in revenue with a $1.3 billion valuation. The channel was the entire value proposition.

Both companies succeeded because the technical complexity and channel-specific knowledge created genuine barriers. You couldn’t just bolt on Discord’s voice quality or Intercom’s message orchestration as an afternoon project.

The Graveyard

In the cohorts I’ve worked through since 2022, I’ve watched this pattern destroy dozens of early-stage companies. The most common casualty: startups building AI-powered customer support tools as standalone products. They’d raise a seed round on the promise of “intelligent chatbots that understand context” or “automated ticket routing that learns from your team.” Solid products. Real customer validation. Then Meta, Zendesk, and Salesforce started bundling comparable AI features directly into their core platforms — often at no additional cost.

The breaking point wasn’t that the AI got better. It was that the incumbents already owned the channel. They had the customer data, the existing integrations, the sales relationships. Adding an AI layer was trivial compared to the distribution advantage they already held. The startups were selling a feature. The incumbents were giving it away to protect their platform position.

I watched one founder in a 2023 cohort spend eighteen months building a sophisticated AI email outreach tool. Beautiful product. Real engagement metrics. Dead within six months of HubSpot shipping their AI email writer. The channel — email outreach — became a commodity the moment the platform players decided to compete.

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What AI Actually Changed

The specific mechanism: AI collapsed the technical barriers that made channel expertise defensible. What used to require deep domain knowledge and months of custom development now takes an afternoon with the right API calls.

Outbound sales automation used to be hard. Tools like Outreach and SalesLoft built entire companies on sequencing, personalisation, and analytics. Then ChatGPT made it trivial to generate hundreds of personalised cold emails in minutes. Clay, Apollo, and a dozen others bundled AI writing directly into their platforms. The channel — cold email — stopped being a product and became table stakes.

Content generation followed the same path. Companies built businesses around “content marketing automation” or “SEO content at scale.” Jasper raised $125 million on this premise. Then every CMS, every social media tool, every marketing platform added AI writing. WordPress plugins, LinkedIn’s built-in post composer, HubSpot’s content assistant — all free or near-free. The channel commoditised in eighteen months.

Referral mechanics and growth loops used to require serious engineering. Tools like Viral Loops and GrowSurf sold the infrastructure to run referral programmes. Now? Most CRM and marketing automation platforms ship with AI-powered referral tracking and optimisation built in. The channel became a feature, not a product.

The pattern repeats: AI made the technical execution cheap enough that platform players could bundle it without thinking twice. The channel stopped being defensible the moment it became a commodity capability.

The New Playbook

Build distribution into the product experience, not around it. Your product should create its own distribution as a natural byproduct of use. Loom did this — every video shared was a mini demo of the product itself. The distribution was embedded in the core value, not bolted on as a separate channel strategy. If your “channel” can be replicated by someone else’s feature update, it’s not a product.
Own proprietary data that makes the channel work better for your specific use case. If you’re building on top of a channel, the defensibility comes from data incumbents can’t easily replicate. Scale AI didn’t just build labelling tools — they built proprietary training data that made their tools uniquely effective for specific AI use cases. The channel (data labelling) was commoditised, but the data moat held.
Make the AI create network effects, not just automate tasks. Tools like Notion AI or Figma AI get more valuable as more people use them because they learn from collective usage patterns within teams. The AI isn’t just a feature — it’s the mechanism that creates lock-in. If your AI just automates what one person does faster, you’re building a feature. If it gets smarter as your customer’s team grows, you’re building a moat.
Ship faster than incumbents can bundle. The only way to win the feature race is to not be in it. Move so fast that by the time a platform player copies your current feature set, you’re already three versions ahead. Linear does this relentlessly — they ship meaningful updates every week, staying ahead of Jira and Asana’s AI features by sheer velocity. Speed becomes the moat when technical complexity isn’t.

The Warnings

The “AI wrapper” trap. I’ve watched too many founders build what they think is a product but is actually just a thin UI on top of OpenAI’s API. They raise a seed round, get early traction, then watch their entire value proposition evaporate when OpenAI ships a native interface or when their customers realise they can just use ChatGPT directly. The cost: six to twelve months of runway burned before the pivot. If your product is just “ChatGPT but for [vertical],” you’re not building a company — you’re renting time until the channel owner notices.

The “bundling is coming” delusion. Founders convince themselves they have eighteen months before the big platforms bundle their feature. They’re wrong. In the cohorts I’ve tracked, the median time from “this could be a feature” to “this is now a free feature” dropped from thirty-six months pre-2020 to fourteen months post-ChatGPT. The acceleration is real. If your entire moat is “we do this one channel thing really well,” you don’t have years — you have quarters. Plan accordingly or die surprised.

The Bottom Line

The channel isn’t the product anymore — the product is what makes you impossible to unbundle even after the channel gets commoditised.

Companies To Watch

These companies built their core product around owning a distribution channel. Each one faces the same bundling pressure described above — watch how they respond.

Aspire — Singapore — Influencer discovery and campaign management platform. Built their entire product around owning the influencer distribution channel. TikTok Shop and Meta are now building native creator marketplaces. The channel they own is becoming a platform feature.

PartnerStack — Toronto, Canada — Partner and referral programme infrastructure for SaaS. Their moat is being the dedicated layer for partner distribution. As HubSpot and Salesforce build native partner tracking, the standalone case gets harder to make.

Yotpo — Tel Aviv, Israel — UGC, SMS and email acquisition platform for e-commerce. Built a $1.4B valuation on owning those channels for Shopify merchants. Shopify’s own marketing tools are eating directly into that territory.

Impact.com — Santa Barbara, USA — Affiliate and partnership automation platform. Owns the affiliate channel for enterprise brands. AI-powered attribution and fraud detection are now standard features in major ad platforms.

Talon.One — Berlin, Germany — Promotion and loyalty engine. Built around owning the promotions distribution layer. CDPs like Segment are bundling similar capabilities as native features.

Referral Rock — Washington DC, USA — Referral programme infrastructure. Their pre-AI moat was referral loop mechanics as a standalone product. Growth platforms now ship this as table stakes.

Part of Startup Principles for an AI World — 30 principles for building in the new era. New issue every week.

Enjoy and hope to see you soon.
❤️ & ✌🏼

Derek

Please let me know what you think of it, love a feedback loop 🙏🏼

❤️ Love it, keep them coming!
👍 Great, always a good read.
😊 Enjoyable, but let’s see some variety.
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🛑 Get a different job.

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