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

1. 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.

2. 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.

3. 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.

1. 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.

2. 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.

3. 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

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you’ve got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I’m keen to include it in the upcoming edition.

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.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe and follow me on LinkedIn or Twitter to never miss an update.

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.

1. 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.

2. 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.

3. 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.

1. 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.

2. 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.

3. 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:

1. Identify Your Wedge: What specific, painful “last mile” data problem will you solve? Be vertical.

2. Map Integration Surface: How will your product seamlessly integrate into existing data stacks (Snowflake, Databricks, CRMs)?

3. Assess Platform Squeeze Risk: Is your core feature immune to being built by an incumbent platform within 18 months?

4. Distinguish User vs. Buyer: Who uses it daily? Who signs the cheque? Design for both.

5. 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.

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

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you’ve got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I’m keen to include it in the upcoming edition.

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.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe and follow me on LinkedIn or Twitter to never miss an update.

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.

1. 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.

2. 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:

1. Product-Led Growth: The open-source project acted as a powerful marketing and acquisition engine, attracting developers who then brought Sentry into their organisations.

2. Cloud Adoption: The shift towards cloud-native architectures made Sentry’s managed service even more appealing, as companies increasingly preferred to offload operational burdens.

3. 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

1. 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.

2. 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.

3. 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.

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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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

1. 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.

2. 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.

3. 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.

4. 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.

Subscribe now

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.

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

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

Derek

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

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.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe below and follow me on LinkedIn or Twitter to never miss an update. 

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:

1. 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.”

2. 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.

3. 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:

1. Become a Platform: Be the company that acquires the point solutions and integrates them into a single, cohesive offering.

2. 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

1. 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.

2. 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.

3. 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.

For the ❤️ of startups

Leave a comment

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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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

1. 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.

2. 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.

3. 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.

4. 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.

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

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

Derek

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

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.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe below and follow me on LinkedIn or Twitter to never miss an update. 

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:

1. Physical infrastructure (data centres, power, cooling)

2. Virtualisation layer (hypervisors, orchestration)

3. Managed services (databases, queues, caching)

4. Developer tools (deployment, monitoring, security)

5. 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

Leave a comment

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

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you’ve got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I’m keen to include it in the upcoming edition.

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.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe and follow me on LinkedIn or Twitter to never miss an update.

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.

Everyone’s building agents. Almost nobody’s shipping them to production. The gap between demo and deployment is where the real money sits. $2.8B flowed into agentic startups in H1 2025 alone — but most of that capital chases wrappers. Your pitch is: we sell the boring reliability layer that turns pilots into production. We’ve been here before. Just with worse technology and different buzzwords.

The Original Problem

Businesses have always paid humans to do repetitive work that computers should handle. The problem wasn’t recognising this — it was building systems reliable enough to trust with real operations. Early automation was brittle. Rule-based systems broke the moment reality deviated from the script. Robotic Process Automation (RPA) could click through SAP screens, but couldn’t handle exceptions. Integration platforms could move data between systems, but couldn’t decide what to do with it. The constraint was intelligence. You could automate the happy path. You couldn’t automate judgement. LLMs changed that constraint. Suddenly you had systems that could read unstructured data, make decisions based on context, and generate outputs that looked human. The question shifted from “can we automate this?” to “can we trust it?” That’s where most founders are stuck today. Building an agent that works in a demo is easy. Building one that won’t bankrupt your customer when it hallucinates is the actual product.

The Numbers

Funding is flowing, but market sizing is noisy. The only number that matters early: cost-to-serve per successful outcome, including human review.

The acquisition numbers tell the story better than market projections. Incumbents are paying billions for distribution and trust. If you’re building in this space, that’s your exit benchmark — or your competition.

The Timeline

Era 1: Rule-Based Automation (1990s–2010s)

What enabled it: GUIs, APIs, enterprise software standardisation. The first wave was about replacing mouse clicks. RPA tools like Blue Prism and UiPath sold “digital workers” that could log into systems, extract data, and update records. The pitch was simple: automate the boring stuff your analysts hate doing. The playbook: identify high-volume, low-complexity tasks. Build bots that mimicked human actions. Deploy them in controlled environments where exceptions were rare. Why it ended: Brittleness. These systems broke constantly. A UI update would kill your bot. An unexpected error message would leave it stuck in a loop. You needed armies of bot maintainers. The promised ROI evaporated in maintenance costs. Blue Prism went public at £1.6B in 2016, then sold to SS&C for £1.1B in 2022 after maintenance costs crippled their margins. Banks were spending more on bot babysitters than they saved on headcount. What it means today: If your agent can’t handle the thousand edge cases that break your system at 3am, you’re building RPA with better PR.

Era 2: Integration Platforms (2010s–2020s)

What enabled it: Cloud APIs, Zapier-style workflow builders, microservices. The second wave moved from UI automation to API-first integration. Tools like Workato and Tray.io let you connect systems without brittle screen scraping. The pitch evolved: don’t automate actions, automate workflows. Key exit: MuleSoft, acquired by Salesforce for $6.5B in 2018. The playbook: build visual workflow builders. Let business users create integrations. Charge based on tasks executed. Why it ended: Still no intelligence. You could move data between systems, but you couldn’t make decisions about that data. Every conditional required manual configuration. Complex workflows became unmaintainable spaghetti. What it means today: Your agent needs to do more than connect APIs. If a human still has to configure every decision path, you haven’t solved the problem.

Era 3: Early AI Agents (2020–2023)

What enabled it: GPT-3, function calling, vector databases. The third wave added reasoning. LLMs could understand natural language instructions, decide which tools to use, and chain actions together. The pitch shifted again: autonomous AI that can figure out what to do. The playbook: wrap LLMs with tool access. Let them call APIs, query databases, and execute code. Market it as “AI that takes action.” Why it stumbled: Demos worked brilliantly. Deployments struggled with hallucinations, costs, and security. Few have solved it in a repeatable, self-serve way. What it means today: This is where most founders are right now. You’ve built something impressive. You can’t ship it because you don’t know what it’ll do under load.

Era 4: Production-Grade Agentic Systems (2024–Present)

What enabled it: Better models (GPT Claude Gemini and nemours open source models), structured outputs, observability tools, human-in-the-loop patterns. This is the era we’re in. The technology works. The challenge is reliability. Smart companies aren’t building fully autonomous agents. They’re building systems with guardrails: structured outputs that force valid responses, confidence scoring that flags uncertain decisions, human-in-the-loop workflows that catch errors before they propagate. Key signal: Moveworks, acquired by ServiceNow for $2.85B. Incumbents aren’t building this from scratch — they’re buying it.

The playbook: start with high-value, low-risk use cases. Build observability from day one. Design for human oversight. Charge based on outcomes, not API calls. What it means for founders: Your differentiation isn’t the LLM. It’s the boring infrastructure that makes it safe to deploy.

The Production Wall

Most failures in this space don’t make headlines because companies pivot before they die. But the patterns are clear:

The real graveyard is quieter: internal projects at enterprises that got killed after six months because they couldn’t move from pilot to production. That’s where most of the learning is happening. Red flags for week one:

• Can your agent explain every tool call? If not, you can’t debug.

• Can it be rate-limited per user? Unlimited spend isn’t production-ready.

• What’s your rollback path when it writes bad data? No undo, no deploy.

Where The Money Sits

The margins live between layers 2 and 3. Frameworks are commoditised. Applications are crowded. But orchestration — the boring infrastructure that routes Agent A’s output to Agent B’s input, handles rollback, manages cost — that’s where you can charge enterprise prices because nobody’s solved it yet. The value chain:

• Foundation models (OpenAI, Anthropic, Google)

• Agent frameworks (LangChain, CrewAI, AutoGPT)

• Orchestration platforms ← the gap

• Application layer (customer service, sales, operations)

Incumbents are buying in. ServiceNow paid $2.85B for Moveworks. Salesforce paid $6.5B for MuleSoft. They’re buying distribution and trust to embed agentic capabilities into existing platforms. They’ll own enterprise deployment within 24 months unless you can crack mid-market self-serve first. The blind spot: Mid-market companies with 100–1,000 employees. Too complex for Zapier. Too small for ServiceNow. Desperate for automation they don’t need a systems integrator to deploy. The tax everyone pays: LLM API costs, observability tooling, and human oversight. Your margin is what’s left after those three. Most founders underestimate the last one.

What’s Actually Hard

Reliability isn’t sexy, but it’s the entire product. Your agent needs to work 99.9% of the time, not 95%. That last 4.9% is where you’ll spend 90% of your engineering time. Observability from day one. You can’t debug what you can’t see. When an agent makes a bad decision, you need to trace exactly why: which context it used, which tools it called, what the model returned. Most founders bolt this on later. It’s too late. Cost control under adversarial conditions.

A founder we know watched their agent get tricked into a loop that called GPT-4 8,000 times in an hour — $3,200 gone before the alert fired. Production means rate limits, circuit breakers, and assuming users will try to break you. Human-in-the-loop isn’t a fallback — it’s the design.

The best systems don’t try to eliminate humans. They route decisions to humans when confidence is low, and learn from those decisions to improve. Building this feedback loop is harder than building the agent. Multi-step reliability compounds. An agent that’s 95% accurate per step is 77% accurate over three steps. By step ten, you’re at 60%. You need much higher per-step reliability than you think.

What’s Shipping Now

The companies actually deploying agents in production are doing boring, specific things:

• Customer service triage: Agents that read support tickets, categorise them, pull relevant context, and draft responses for human review. Not fully autonomous — augmented.

• Sales research: Agents that monitor buying signals, enrich lead data, and flag opportunities. The human still closes the deal.

• Code review: Agents that scan PRs for security issues, style violations, and potential bugs. The developer still approves the merge.

• Data entry: Agents that extract structured data from unstructured sources (emails, PDFs, forms) and populate systems. With human spot-checks.

Notice the pattern? Real deployments keep humans in the loop for the final decision. The agent does the grunt work. The human does the judgement.

Where This Goes

Consolidation around production-grade platforms. The framework wars will end. Winners will be the ones that solve observability, cost control, and reliability. Agentic systems will merge with existing workflow automation.

You won’t “buy an AI agent.” You’ll upgrade your CRM, and it’ll come with agents built in. ServiceNow‘s Moveworks acquisition is the blueprint. Assume distribution will be bundled into incumbent suites. Build for plug-in deployment and procurement from day one. Don’t start multi-agent. Start single-agent with strict contracts. Your roadmap to multi-agent: (a) typed tool interfaces, (b) shared state store with versioning, (c) conflict resolution policy.

Moves Worth Stealing

• Start with the audit trail, not the agent. Before you build autonomy, build the infrastructure to understand every decision your system makes. This becomes your differentiation when you’re selling to enterprises.

• Charge for outcomes, not API calls. The companies winning right now are the ones that absorb the LLM cost risk and charge based on results. This forces you to optimise for reliability, not features.

• Design for the escalation path. When your agent hits uncertainty, what happens? The best systems route to humans seamlessly, learn from their decisions, and get better over time. Build this workflow from day one, not as an afterthought.

The Why Now

Three things changed:

1. Models got reliable enough. GPT and Claude don’t hallucinate as much. Structured outputs give you guarantees. Function calling actually works.

2. Enterprises got desperate. Labour costs are up. Hiring is hard. Productivity is flat. Companies that wouldn’t touch AI two years ago are now mandating it.

3. The infrastructure exists. Observability tools, vector databases, orchestration frameworks — the boring stuff that makes agents production-ready is finally available. You don’t have to build it from scratch.

The window is open. But it won’t stay open forever. The incumbents are buying their way in. If you’re starting today, you’re racing against both startups and incumbents. The startups have velocity. The incumbents have distribution. You need both.

If You’re Starting Monday

The wedge: Pick one workflow in one vertical where humans do repetitive judgement work. Customer service ticket routing. Sales lead qualification. Code review. Not “AI for everything” — one specific thing you can ship in three months. The trap: Building for full autonomy. You’ll waste a year trying to eliminate humans from the loop. Design for augmentation instead. The agent handles the grunt work. The human handles edge cases. Ship that.

The customer: Mid-market companies (100–1,000 employees) in regulated industries. They have the budget and the pain, but they can’t afford multi-year enterprise implementations. They need something that works out of the box with minimal customisation. The first hire: Not a prompt engineer. An infrastructure engineer who’s built production systems at scale.

The LLM is the easy part. Reliability is the product. Week 1 ship list:

(i) eval harness with 200 real cases,

(ii) tool-call logging + replay,

(iii) per-tenant spend caps,

(iv) write-path staging + undo,

(v) escalation queue with SLAs.

Start with something boring that works. Scale from there. The companies trying to build AGI will run out of money. The companies solving real problems will print it.

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Go Deeper

• Agentic AI Venture Funding: $2.8B in H1 2025 — Where the money’s going and what VCs are betting on

• ServiceNow’s $2.85B Moveworks Acquisition — What it signals about incumbent strategy in agentic AI

• AI Startup Funding Trends (Crunchbase) — Broader AI investment context and where agentic fits

• 2025 State of Generative AI in the Enterprise (Menlo Ventures) — Enterprise adoption patterns and the pilot-to-production gap

• Top AI Agent Startups by Funding — Market sizing and the key players

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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Cap tables decide who wins when you finally win. Get them wrong, and you can build for 7 years only to walk away with less than your first corporate salary. In this masterclass, we break dow…

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🔥 In this piece:

• 🤖 What AI-Native Really Means

• 🧩 SaaS vs SaS: The New Foundation

• 📉 Why the Old SaaS Scoreboard Fails

• 📊 The New AI-Native Scorecard

• 🎯 Why This Matters for Founders & Investors

If you measure AI-native platforms with old SaaS KPIs, you’ll back the wrong founders and misprice the winners.

For anyone building or investing in the AI wave, one thing is now clear: if you evaluate AI-native companies using traditional SaaS metrics, you will misdiagnose the business, misunderstand the economics, and allocate capital in all the wrong places.

For twenty years, SaaS operated on a stable foundation. Software helped humans complete workflows. Pricing was per seat. Growth was linear. And the metrics were predictable: ARR, the Rule of 40, LTV:CAC, net retention, and those comforting 80–90% gross margins.

That model was correct for its era. It is not correct for this one.

A new class of company has emerged — Cursor, Lovable, Manus and others — and they do not behave like SaaS. They do not scale like SaaS. They do not retain users like SaaS. Their cost structure is nothing like SaaS.

These companies are AI-native.

Not “SaaS with an AI feature.”
Not “a wrapper on GPT-4.”
Not “we fine-tuned a model.”

AI-native is an entirely different product architecture — and therefore an entirely different economic system.

Yet the industry is still scoring them on the wrong scoreboard.

🤖 What AI-Native Really Means (Not What People Think)

Most people assume “AI-native” means building your own model, training your own LLM, or deploying proprietary architectures.

This is a misunderstanding.

AI-native = the AI does the work, not the human.

More precisely:

• The AI is the operator.

• The product delivers finished outputs, not workflow steps.

• The unit of value is the completed job, not time spent in product.

• Every unit of value carries a real inference cost.

• The product cannot exist without the AI performing the work.

This has nothing to do with whether you use Claude, GPT-4, Llama, Mistral, or a hybrid router.

AI-native = outcome-producing, cost-bearing, value-generating systems.

Once you define it this way, the economics become obvious.

LISTEN TO THE DEEPDIVE PODCAST

🧩 SaaS vs SaS: The New Foundation

Fusion42 and Investor-X use a distinction that is becoming foundational across the ecosystem:

SaaS — Software-as-a-Service

• Human = operator

• Software = tool

• Value = enabling workflows

• Costs = fixed, near-zero marginal cost

• Pricing = per seat

• KPIs = adoption, usage, engagement

SaS — Service-as-Software

• AI = operator

• Software = delivery wrapper

• Value = completed output

• Costs = variable (inference per task)

• Pricing = usage, jobs, credits, agents

• KPIs = tasks completed, cost-to-serve, outflow retention

Most modern AI-native products are SaaS shells with SaS cores — a familiar interface wrapped around an autonomous service engine.

And once you see this, the conclusion is unavoidable:

SaaS metrics measure human behaviour.
SaS metrics measure output economics.

Judging SaS platforms with SaaS metrics is like diagnosing an electric drivetrain with petrol-engine tools.
You’ll get confident answers — and all of them wrong.

📉 Why the SaaS Scoreboard Fails (Metric by Metric)

1. Gross Margin: Not a Percentage — a Trajectory

Typical SaaS: 80–90%
Early AI-native: 40–60%

Not because AI-native is weak, but because:

• SaaS COGS is flat

• AI-native COGS scales with outputs

• every action incurs compute cost

• users vary massively in cost-to-serve

The correct KPI is Gross Margin Trajectory — not the static percentage.

2. CAC: High CAC + High COGS = Dead Company

When marginal costs are real, the only viable GTM is:

• product-led

• viral

• instant value

• self-serve

The correct KPI is CAC trendline vs inference efficiency, not CAC payback.

3. Retention: The Real Curve Starts at Month 3

AI-native products attract “AI tourists.”
Month-1 retention is noise.

The real signal is:

M3 → M12 Cohort Retention

Healthy curves show:

• tourists out

• professionals in

• usage up

• spend up

• cohorts that dip → flatten → rise (“the smile”)

Traditional NRR hides this.

4. Engagement Metrics Are Obsolete

SaaS measures:

• DAU/MAU

• time in product

• workflow usage

AI-native must measure:

• jobs completed

• workflows executed

• assets produced

• tasks automated

Engagement is not value. Output is value.

5. Pricing: Seats Are Dead. Outcomes Win.

SaaS pricing is cost-agnostic.
AI-native pricing must be cost-aligned:

• usage

• credits

• outcomes

• tasks

• per-agent (“AI employee”)

Outcome-based pricing aligns value, cost, and margin.

📊 The New Scoreboard: The Investor-X Scorecard

-

1. Inference Cost per Active Customer (ICAC)

Your new North Star.
Falling ICAC = compounding business.

2. Jobs Owned per Account

The real measure of dependency, value, and moat.

3. M3 → M12 Retention

By Month 3, only real users remain.
Healthy: 85–120% M12/M3.

4. Gross Margin Trajectory

Direction matters more than destination.

5. Wallet Share of Customer AI Spend

Seat count is irrelevant.
Workload share is everything.

🎯 Why This Matters

If you judge AI-native companies with SaaS metrics, you will:

• underestimate growth

• misread retention

• misunderstand cost curves

• misprice valuations

• and back the wrong founders

This shift is as large as:

• internet → cloud

• cloud → mobile

• mobile → AI-native

AI-native companies are not “SaaS with AI features.”
They are autonomous service engines delivered through SaaS interfaces.

Once the unit of value shifts from workflow → output, every metric must shift with it:

• economics

• pricing

• retention

• moats

• valuation

• strategy

This is why the SaaS scoreboard fails.
And it’s why those who adopt the AI-native scoreboard now will win the next decade.

Share Fusion42 by DeReK WaTSoN

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

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.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe below and follow me on LinkedIn or Twitter to never miss an update. 

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For the ❤️ of startups

✌🏼 & 💙
Derek

💡 Financial Models Don’t Lie (Antoine Bruna, Cap Quest)
Antoine opens up his financial projections playbook—the same model used across startups and private equity. He explains why you must build your own projections (“better be prepared to answer these kinds of questions”), how to …

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We explore:

• Dynamic AI integration and how autonomous systems are reshaping governance and decision-making.

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In this unfiltered and insightful session, Derek Watson and Sheena Pirbhai, alongside a panel of startup leaders and investors, unpack the realities of resilience in the entrepreneurial journey. From managing burnout to building a culture of self-awareness and adaptability, they explore actionable strategies to navigate the relentless pressures of start…

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Building the right foundations early is much easier than fixing overlooked basics later. Covering ten key pillars of any successful business, this checklist will help you avoid common mistakes, spot gaps, and stay on track to create a sustainable, backable business.

Index of Key Business Pillars

1. Product or Service

   1. Clear definition of the business's offerings.

   2. Address real customer needs, backed by solid market research and competitive insights.

2. Sales & Marketing

   1. Strategies for acquiring and retaining customers.

   2. Develop accurate customer personas and align sales strategies to these personas.

3. Customer Relationships & Retention

   1. Enhancing the overall customer experience.

   2. Implement loyalty programs and feedback systems to boost retention.

4. Operations

   1. Setting up effective operational workflows.

   2. Ensure processes and supply chains are scalable and well-documented.

      ---

      READ » What is Marketing in the Context of a Startup?

      Fusion42 by DeReK WaTSoN

      What is Marketing in the Context of a Startup?

      💡The Idea is Just the Beginning…

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      2 years ago · 5 likes · Derek Watson and DeReK WaTSoN

      ---

5. Technology Stack

   1. Building a robust and scalable technological infrastructure.

   2. Support growth through secure, scalable tech solutions and automation.

6. Financial Management

   1. Establishing solid financial frameworks.

   2. Develop clear revenue models, perform budgeting, forecasting, and monitor financial health metrics.

7. Legal & Compliance

   1. Ensuring the business adheres to legal standards.

   2. Choose the right legal structure and maintain compliance with laws and regulations.

8. Human Resources & Team

   1. Cultivating a strong organizational team and culture.

   2. Align team and culture with strategic goals and maintain effective leadership structures.

9. Growth & Innovation

   1. Scalability and ongoing innovation.

   2. Plan for business expansion and stay updated with industry trends for continuous improvement.

10. ESG (Environmental, Social, Governance)

    1. Integrating sustainability and ethical practices.

    2. Implement and monitor sustainable business practices and ethical governance.

Quick Checklist 👇 Glossary of Key Terms 👇

But first... The Founder's Truth, Your Identity and Core Values.

Getting to grips with your role as a founder is uber important on this wild ride. Recognising your unique entrepreneurial style is not just about personal insight—it directly influences how you shape your business’s goals and align them with desired and practical outcomes:

➡️ Innovators break new ground with solutions that aspire to change everyday life.

➡️ Hustlers are the dynamos, pushing the business forward with sheer grit and relentless execution.

➡️ Imitators smartly adapt successful models with a twist, rejuvenating what already exists.

➡️ Researchers wield deep insights and academic depth to develop cutting-edge offerings.

➡️ Buyers think big, driving growth through strategic acquisitions.

But knowing your entrepreneurial flair and understanding your customer and capital needs is just the start. Pinning down your core company values is absolutely critical. Adhering faithfully to your core values, the DNA of your culture, is crucial for realising your vision. Straying from these principles for short-term gains can derail long-term goals and can compromise integrity. Your founder type doesn’t just influence strategy but also embeds the core values that are your guiding light to achieve your long-term vision.

Innovators value creativity

Hustlers emphasise resilience

Imitators focus on adaptability

Researchers prioritise innovation

Buyers underscore scalability

A successful team requires a mix of different types, but the core values must always be respected. Without this, the company’s direction can slowly shift, often leading to toxicity. Iterating or pivoting must always align with these fundamental principles—they must always hold the one true line.

💎 Living your core values is crucial.

Here's how to make them active elements of your business operations:

Clear Communication: Keep it simple. Make sure everyone not only knows the core values but sees them in action every day.

Lead by Example: Set the bar with your actions. Lead the way, and others will follow.

Continuous Reflection: Keep asking, "Are we on track?" Regular check-ins keep us true to our values as we grow.

Your values define your company's heart and soul. It ensures a cohesion that truly reflects you and why the hell you are doing this.

1. Product or Service

Core Offering

🚀 ☐ Have you clearly defined what your business offers (product, service, or both)? 🚀 ☐ Does your offering actually solve a real problem for your target audience?

Market Research & Competitive Analysis

🔥 ☐ Have you tested your product/service with real customers and done your market research?

🔥 ☐ Do you understand the size of your market, your customers’ needs, and where your product fits?

🔥 ☐ Do you understand what products people use to solve the problem currently?

🔥 ☐ Have you checked out the competition? What are their strengths and weaknesses?

🔥 ☐ Are you staying on top of market changes and customer behaviour?

🔥 ☐ Do you have a clear idea of how your product stands out in the market?

🔥 ☐ Are people getting excited about it?

Innovation

⚙ ☐ Are you constantly tweaking or improving your product/service inline with your user feedback loops?

⚙️ ☐ Are you keeping an eye on innovation trends in your industry to stay ahead of competitors?

Intellectual Property

⚙️ ☐ Have you identified any IP and how to protect it?

💡Priority Tip: Start by validating your product and getting to know your market inside out. Once you're confident, keep innovating to stay one step ahead.

Join us today!

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🔄 The Evolution of SaaS into a Fully Automated Service as Software (SaS) Model

As technology marches on, new ideas and models keep popping up to reshape entire industries. One of the latest of these shifts is Service as Software (SaS)—a forward-thinking approach that builds on the well-established Software as a Service (SaaS) model. While SaaS has given users handy tools to carry out tasks themselves, SaS takes things a step further. By harnessing advanced AI capabilities, SaS doesn’t just provide the tools; it delivers entire services, running autonomously through software.

This evolution could open up a huge potential market, estimated at a staggering $4.6 trillion, as it aims to cover a much broader spectrum of business needs that used to rely on human labour. Let’s explore how SaS is set to redefine service delivery across a range of industries.

Vertical SaaS is gonna get smashed first

Read the previous article here:

Fusion42 by DeReK WaTSoN

Vertical SaaS is gonna get smashed first

🚨 Why Vertical SaaS Is Set to Face Major Disruption…

Read more

3 years ago · 1 like · DeReK WaTSoN

Listen to the Deepdive Podcast here:

🤖 What is Service as Software (SaS)?

In essence, Service as Software (SaS) is a model where AI agents and advanced automation deliver entire services, rather than simply offering tools or platforms for users to operate themselves. SaS is designed to carry out tasks autonomously—managing whole processes that would normally need human oversight. Think of AI agents that not only schedule meetings but also analyse and summarise discussions, organise follow-ups, and even handle customer relationships.

This shift makes SaS tools outcome-focused and task-driven, enabling businesses to tap into streamlined, automated services without any manual effort. With this approach, SaS offers a significant competitive advantage, helping businesses scale up more efficiently, reduce costs, and improve overall productivity.

⚖️ How SaS Differs from SaaS

The differences between SaaS and SaS reveal a shift in the overall philosophy of service delivery:

• Outcome-Oriented Services: Unlike SaaS, where users actively use tools to reach their goals, SaS is focused on delivering outcomes. Users no longer need to schedule emails or update databases themselves; SaS agents handle it all autonomously.

• AI-Driven Autonomy: SaS employs AI agents that can make decisions and adapt as they go, allowing them to take on tasks and improve over time. This level of autonomy reduces the need for human input in routine processes.

• New Pricing Models: SaaS usually operates on subscription or per-user pricing, whereas SaS could shift towards outcome-based pricing—charging based on actual results achieved, such as the number of deals closed, qualified leads generated, or customer interactions completed.
  
  
  

As SaS shifts from tools to outcomes, what processes in your organisation could be optimised—or even fully replaced—with autonomous services?

💹 Market Potential of SaS

The market potential for SaS is immense. While the global software market is substantial, the global services market is even larger, valued at around $4.6 trillion. By delivering complete services through software, SaS companies have the opportunity to tap into this vast space, offering scalable, automated solutions across industries like sales, marketing, cybersecurity, software engineering, and customer support.

🤖 AI-Driven Automation at the Core of SaS

At the core of SaS are AI agents—intelligent software capable of mimicking human behaviour to achieve specific outcomes. Unlike traditional automation, which relies on set workflows, these agents use AI to make decisions, adapt to new inputs, and continually improve. Powered by natural language processing, machine learning, and large language models, SaS agents can take on complex tasks that previously needed human involvement, such as:

• Writing Personalised Emails: AI agents can draft and send customised messages, managing customer interactions and follow-ups entirely on their own.

• Scheduling Demos and Meetings: Rather than simply sending notifications, AI agents can handle the whole scheduling process, optimising timing and resource allocation.

• Customer Interaction and Support: AI agents can autonomously manage customer inquiries, resolve issues, and even escalate complex cases when required.

These agents don’t merely assist with tasks; they complete them, reshaping workflows and freeing human employees to focus on higher-level decision-making.

🏭 Industries Primed for SaS Transformation

Although SaS is still emerging, it’s already showing potential to reshape a variety of industries. Here’s a closer look at some key sectors where SaS could make a significant impact:

1. Sales and Marketing

In sales and marketing, SaS has the capacity to optimise customer engagement and streamline workflows:

• Lead Generation and Qualification: AI agents autonomously search for and qualify leads, allowing sales teams to focus on high-value prospects.

• Automated Outreach: From crafting personalised emails to scheduling demos, AI agents handle the entire outreach process, drastically reducing the workload for sales teams.

• Campaign Analysis: AI-driven insights enable SaS platforms to monitor and adjust campaigns in real-time, boosting performance and return on investment.

2. Software Engineering

In software development, SaS can streamline cycles, reduce errors, and increase overall efficiency:

• Code Generation and Review: AI agents can autonomously write, review, and optimise code, supporting developers and minimising human error.

• Incident Response and System Monitoring: AI agents detect, categorise, and resolve system issues, ensuring real-time problem-solving without human input.

• Continuous Integration and Deployment: SaS platforms driven by AI can autonomously handle updates, testing, and rollouts, making software deployment more efficient.

3. Cybersecurity

Cybersecurity is particularly well-suited for SaS, where AI agents can manage routine tasks, respond to threats, and coordinate across multiple platforms:

• Threat Detection and Response: AI agents autonomously handle alerts and, where possible, respond to threats, reducing security risks.

• Cross-Platform Coordination: Many organisations use multiple security platforms; SaS agents can unify responses across these tools, ensuring a coordinated defence.

• Adaptation to New Threats: SaS agents can learn from past incidents, improving their ability to recognise and counter new and evolving threats.

4. Legal Services

In the legal sector, SaS has the potential to reduce costs and increase productivity by automating research, compliance, and document management:

• Contract Analysis and Drafting: AI agents can review and draft contracts, autonomously flagging potential risks and ensuring regulatory compliance.

• Legal Research: AI can autonomously conduct legal research, searching databases and pulling relevant cases or statutes, providing lawyers with ready-to-use insights.

• Case Management: SaS agents can autonomously manage case files, deadlines, and court submissions, ensuring that all legal processes run smoothly and efficiently.

5. Accountancy

Accountancy, with its data-intensive workflows, is ripe for SaS-driven automation, helping firms process data, ensure compliance, and deliver insights with minimal human input:

• Automated Bookkeeping: AI agents can handle transaction recording, categorisation, and reconciliations autonomously, reducing time spent on routine bookkeeping.

• Audit and Compliance: SaS agents can review financial statements, flagging irregularities and verifying compliance with accounting standards, saving considerable time in audits.

• Tax Preparation and Filing: AI agents can autonomously prepare and file tax returns, ensuring accuracy and adherence to the latest regulations.

6. Financial Services

In financial services, SaS has the power to enhance efficiency, risk management, and client service, automating a wide array of tasks:

• Portfolio Management: AI agents can autonomously monitor and rebalance investment portfolios, responding to market changes and adjusting assets based on predefined strategies.

• Customer Onboarding: SaS agents can handle the entire onboarding process, from KYC (Know Your Customer) verification to account setup, reducing administrative time.

• Fraud Detection and Prevention: SaS-powered AI agents monitor transactions in real-time, detecting suspicious activity and triggering alerts or preventive actions.

  How would customer expectations change if AI could handle everything from lead qualification to full customer support? Could this alter the competitive landscape in sales and marketing?

  ---

SAVE YOUR FREE SEAT

💵 Pricing Models: Moving Beyond Subscriptions

One of the most compelling aspects of SaS is its potential to transform pricing structures. Unlike SaaS, which typically operates on a per-user or subscription basis, SaS paves the way for outcome-based pricing:

• Performance-Based Pricing: Here, businesses are charged based on successful outcomes, such as the number of qualified leads generated, sales closed, or issues resolved.

• Usage-Based Billing: Pricing can also reflect the level of service usage, like the number of emails sent, tasks completed, or customer queries handled.

• Hybrid Models: Some companies may combine subscription and outcome-based pricing, providing predictable revenue while ensuring costs align with the actual value delivered.

These flexible models make SaS both accessible and scalable, catering to companies of all sizes and allowing them to pay only for the outcomes they receive.

⚠️ Challenges and Opportunities in the SaS Landscape

As with any emerging technology, the adoption of SaS presents certain challenges:

• Data Privacy and Compliance: Since AI agents handle sensitive tasks, safeguarding data privacy and ensuring regulatory compliance is crucial, particularly in sectors like healthcare and finance.

• User Trust and Reliability: For AI agents to operate services autonomously, users need to trust that the software will consistently deliver accurate and reliable results.

• Industry Awareness and Adoption: As SaS is a relatively new concept, businesses may need time to fully understand and embrace it. Clearly differentiating SaS from SaaS will be essential for companies aiming to establish themselves in this space.

Despite these hurdles, the opportunities for SaS are vast. With rising demand for automation and AI-driven solutions, SaS has the potential to deliver real, measurable value to companies seeking to streamline operations, cut costs, and scale efficiently.

🔍 Early Examples of Service as Software

While fully autonomous SaS companies are still emerging, several firms are already incorporating key elements of the SaS concept, automating specialised services that previously required human input. Here are a few noteworthy examples:

1. Ema (Enterprise Machine Assistant): This startup deploys "universal AI employees" that can take on various roles in a workplace, from customer support to legal and compliance. This aligns well with the SaS concept of AI agents performing complex tasks

2. KOGO AI: Their 'AI agent marketplace' is designed to automate business operations across sectors, functioning like an app store for AI agents. This platform allows businesses to easily deploy various AI agents for their specific needs

3. Infloso's Molly: An autonomous AI marketer that can independently handle entire marketing campaigns. This is a good example of an AI agent delivering a comprehensive service (marketing) through software

4. Yellow.ai's VoiceX: An AI agent capable of handling high volumes of customer queries while delivering natural, context-aware answers. This represents the use of AI to provide a complete customer service solution

5. Nurix AI: While still in development, their aim to use AI agents with human-like voice and reasoning capabilities for e-commerce aligns with the SaS concept

These examples showcase the versatility and potential of SaS, with advanced AI automating complex processes across diverse fields. By managing entire workflows with little human involvement, these companies illustrate how SaS can transform industries that require high levels of expertise and specialisation.

🍽️ Fusion42's Initial Emphasis on Service as Software (SaS), eating our own medicine.

At Fusion42, we’re not just building a platform for the startup ecosystem; we’re building it with the very Service as Software (SaS) principles we aim to provide for founders, framers, and funders. By applying the same data-driven, AI-powered approaches we plan to offer to users, we’re gradually creating a platform that learns, adapts, and improves based on real interactions. Fusion42 is a work in progress, but it’s already beginning to embody the values it’s designed to deliver.

Building Fusion42 with Intelligent Data Ingestion and Analysis

We’ve started by embedding AI-powered data ingestion and analysis into Fusion42’s development. This approach allows our team to draw insights from user feedback, market trends, and platform interactions, ensuring we prioritise features that matter most to users. Although this system is still evolving, it helps us shape Fusion42’s roadmap so we can continually adapt to the needs of the startup ecosystem.

Personalising Fusion42’s Growth with Early Feedback Loops

Our personalised onboarding and retention processes are about more than just guiding users—they also provide crucial feedback on what’s working. Every interaction offers us insights into which features users find most valuable, helping us prioritise enhancements in line with real needs. This early feedback loop is key to Fusion42’s growth, making the platform more responsive to its community as it develops.

Developing a Data-Driven Go-to-Market Strategy

We’re also using SaS principles to shape our go-to-market strategy. Instead of relying solely on traditional approaches, we’re applying data insights to connect with early adopters, build strategic partnerships, and a network within the startup community. By focusing on early data from target users, we can refine our outreach, attract the right audience, and build credibility in a way that’s both efficient and scalable.

Moving Towards a Self-Enhancing Platform for Long-Term Impact

As Fusion42 grows, every interaction, connection, and user action feeds into our data repository, reinforcing the feedback loops that guide development. This continuous flow of insights allows us to refine Fusion42’s core features—whether it’s matchmaking or market insights—helping us strengthen the platform’s value over time and ensure it remains relevant to the ecosystem.

Laying the Foundation for Self-Improving Matchmaking Algorithms

A key part of Fusion42’s vision is our Intelligent Matchmaking System, which we’re refining to connect startups with compatible investors. Our AI algorithms are learning to assess compatibility based on evolving factors such as investment preferences and sector fit. While we’re still laying the groundwork, each match and user interaction contributes to a smarter, more accurate system. As Fusion42 grows, this matchmaking capability will adapt to the unique dynamics of the startup and investor landscape.

While we’re still in the early stages, our approach to SaS is ensuring that Fusion42 won’t be a static tool. It’s being built as a platform that learns from its users and evolves alongside the ecosystem it serves. This focus on adaptability, intelligence, and data-driven growth means that Fusion42 will increasingly support founders, framers, and funders as they navigate the startup landscape. In essence, Fusion42 is both a product of SaS principles and a reflection of the future we envision for the ecosystem.

🚀 The Future of Service as Software: A New Era in Technology

As technology progresses, Service as Software (SaS) offers an exciting glimpse into the future—one where agentic, AI-powered systems can autonomously manage tasks and deliver real outcomes, moving far beyond the user-driven SaaS model. This shift is transformative: rather than just providing tools, SaS delivers an entirely outcome-focused service that can handle everything from routine tasks to complex processes without human involvement.

In a SaS-driven world, businesses will streamline operations, reduce costs, and achieve consistent, data-driven results across various sectors. Agentic AI systems will be able to adapt to changing conditions, make autonomous decisions, learn from data on the go, and work seamlessly with other AI agents to optimise workflows. For fields like healthcare, legal services, and finance—where specialised knowledge is vital—SaS brings a new level of expertise, efficiency, and scalability that’s never been possible before.

At the heart of this future are master agents—advanced, agentic AI systems that can oversee and coordinate the work of other agents. Acting as central ‘intelligences,’ these master agents understand the roles of each individual agent, managing and directing them to keep processes running smoothly and aligned with broader business goals. They’ll take on a strategic role, handling complex, cross-functional tasks and ensuring all agents are working in harmony across the operation.

As more companies adopt these AI-driven service models and move towards outcome-based pricing, SaS may soon become the standard in both technology and business. This isn’t just a step up from SaaS; it’s a fundamental change in how we view and experience services, shifting towards a world where service delivery is frictionless, fully autonomous, and seamlessly woven into daily operations. With fresh players entering the SaS market and established firms taking on these principles, we’re on the brink of a transformative era—one where Service as Software redefines the very idea of service.

As AI agents gain the ability to autonomously drive outcomes and manage complex tasks, what role will humans play in an increasingly agentic world? Are we prepared for a future where machines not only support us but, in many cases, surpass our abilities in delivering value?

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For the ❤️ of startups

✌🏼 & 💙
Derek

Paul Graham, a big advocate for startup teams, usually sings praises about the magic of having two or three co-founders.

He’s been pretty clear about it:

🔄 However, his recent spiel on Founder Mode seems to flip the script, pushing the idea of a single, dominant leader at the helm. Founder Mode is supposed to ramp up focus and speed up decision-making, but when you’ve got multiple co-founders, it can slide into being too controlling. That's a recipe for tension as everyone jockeys for positioning, and honestly, the subtleties will get lost because we tend to cherry-pick what resonates with us from the top of the article and not the notes at the bottom.

🎯 I totally get the need for having a central figure like a CEO to keep things coherent and aligned. But it’s easy to see how other co-founders might read this as a green light to start pushing their own agendas, leading to chaos where nothing substantial gets done. The best ideas need a level playing field to come out on top, but without open dialogue, you end up making decisions in a vacuum—that can lead to choices that don't align with the company’s core mission or needs.

💼 Ultimately, a CEO/ Founder needs to be involved in all facets of the business, keeping their finger on the pulse to steer everything in the right direction, join the dots and keep things true to the company’s mission and vision.

😊 The Good

• Visionary Leadership: Founder Mode empowers leaders to drive their company with a strong, clear vision, aligning with original goals and responding swiftly to industry changes.

• Rapid Decision-Making: Enables leaders to execute quick, decisive actions, crucial in a startup’s early stages or competitive markets, cutting through bureaucratic delays.

• High Engagement and Commitment: Often leads to deep personal investment from leaders, boosting their commitment to success and encouraging similar levels of dedication among employees.

• Innovative Agility: Boosts the company's ability to innovate rapidly, essential for startups in fast-evolving industries or facing technological disruptions.

• Stronger Company Identity: Quickly establishes a distinct company identity and culture, critical for branding and early recognition.

• Decisive Crisis Management: Prepares startups to handle crises with fast, effective decisions, crucial in high-stakes situations.

• Clear Accountability: Establishes a direct line of accountability, increasing stakeholder confidence in the company’s leadership.

• Fusion42 is thrilled to offer you an exclusive ticket to the world’s largest and highest-rated Technology and Startup event, GITEX Technology Week and North Star, this October in Dubai. Experience cutting-edge innovations and connect with industry leaders at one of the globe’s most anticipated tech gatherings.

• Your Complimentary Access Includes:

  • Full Weeks Pass: Explore every facet of GITEX Global, Expand North Star+, and all related exhibitions at the Dubai World Trade Centre and Dubai Harbour.

  • Early Access: Gain privileged entry to the Korean pavilions 2 hours before the general public for a sneak peek at the latest technological breakthroughs.

  • Tailored Networking Opportunities: Secure 1:1 meetings with key Korean government officials and pioneering startups.

  • Exclusive Business Networking Dinner: Mingle with top industry figures and investors at a special dinner, fostering valuable connections.

• A big thank you for this opportunity goes to KONNECTIKA Global and the Korean Government Institutes of Entrepreneurship, who have made these passes possible.
  

Apply here

😕 The Bad

• Role Confusion and Overlap: Unclear boundaries in multi-leader teams can lead to responsibilities overlapping, causing friction and inefficiency.

• Potential for Burnout: The intense involvement required can exhaust founders, impacting their health and the organisation’s stability.

• Challenges in Scaling: As the company grows, the intensive leadership style may hinder the ability to delegate and empower others, essential for effective scaling.

• Difficulty in Team Building: Overly dominant leaders can inhibit the development of other team members, potentially stunting the growth of internal leadership.

• Limited Diversity of Thought: Over-reliance on one leader's decisions can stifle creativity and limit problem-solving capabilities within the team.

• Risk of Misalignment with Team Values: A dominant leader’s vision might not align with the entire team’s values, leading to disengagement and cultural mismatch.

😬 The Ugly

• Risk of Authoritarianism: Without proper checks and balances, Founder Mode can evolve into a dictatorial style, stifling innovation and suppressing dissent.

• Amplification of Narcissism: If not managed, the mode can heighten narcissistic traits in leaders, skewing decision-making towards personal gains over company welfare.

• Cult of Personality: Overemphasis on a single leader can make the company too reliant on one individual, risking instability if that leader departs.

• Increased Key Person Risk: Heavy reliance on a single individual heightens organisational risk if that leader cannot perform their duties due to unforeseen circumstances.

• Potential for Ethical Failures: Leaders may make expedient decisions that compromise ethical standards, particularly in the absence of collaborative checks.

🔍 Founder Mode has the potential to significantly impact startups through its focus on swift, decisive leadership and deep personal investment. However, its success hinges on careful implementation and a robust governance framework to mitigate risks such as authoritarian tendencies and potential burnout. For startups with multiple co-founders, it is essential to define roles clearly and cultivate a culture that emphasises collaboration and checks and balances. This ensures that while Founder Mode can offer substantial benefits, it does not undermine the core ethical foundations and collective objectives of the company. Remember, skipping the fundamentals of solid business ethics and foundations in favour of any trending mode can lead to serious repercussions that might circle back to challenge the business's integrity and sustainability.

Share Fusion42 by DeReK WaTSoN

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

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.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe below and follow me on LinkedIn or Twitter to never miss an update. 

Subscribe now

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✌🏼 & 💙
Derek

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