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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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

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.

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

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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.

Leave a comment

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

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.

Why this exists (and when it matters)

Search changed quietly — but not universally.

Your website is no longer read only by people. It is read, summarised, compressed, and repeated by machines — before a human ever sees you. When those machines misunderstand you, your market misunderstands you. No amount of polish fixes that.

That said, GEO is not equally important for every startup.

If you sell enterprise software through relationships, GEO won’t replace sales. If your growth is dominated by app‑store algorithms, GEO won’t outrank Apple. But if discovery, explanation, comparison, or recommendation matter to your business — GEO is no longer optional.

GEO is not hype. It is alignment under compression.

This guide shows:

• where GEO actually sits (and where it doesn’t),

• what it practically changes for founders,

• and how to implement it without agencies, theatre, or over‑engineering.

Is GEO worth your time? (Decision tree)

Use this page before you read anything else.

Step 1 — How do people find you today?

• Mostly through inbound search, content, or recommendations → go to Step 2

• Mostly through outbound sales or relationships → GEO is secondary, not critical

• Mostly through app stores or closed platforms → GEO has limited impact

Step 2 — Do people ask machines questions like these about your category?

• “What tool should I use for ___?”

• “What’s the best way to ___?”

• “Explain ___ and give examples.”

• Yes → go to Step 3

• No → GEO is useful but not urgent
  
  

Step 3 — Can a machine accurately describe you in one sentence today?

• Yes → GEO will amplify clarity

• No → GEO will expose misalignment (this is where the leverage is)

Step 4 — Do you care if AI systems cite or recommend you?

• Yes → GEO is worth your time now

• No → stop reading and revisit later

Rule of thumb: If AI‑mediated discovery or explanation touches revenue, trust, or pipeline — GEO matters.

1) What GEO is

Generative Engine Optimisation (GEO) is the practice of making your business legible to large language models.

Not rankable. Legible.

Important distinction: GEO is not creative or persuasive copywriting. It is literal clarity under compression — writing so machines can extract, summarise, and repeat you without interpretation.

Where SEO tried to win positions, GEO tries to win summaries.

Machines now answer questions like:

• “Who should I use for X?”

• “What’s a good tool for Y?”

• “Explain Z and cite examples.”

They don’t browse like humans. They extract → compress → cite.

If your site is vague, contradictory, or bloated with marketing language, machines cannot safely quote you. When they can’t quote you, they don’t recommend you.

GEO’s job: ensure that what you actually do is what machines repeat about you.

GEO is not

• A replacement for SEO

• A traffic guarantee

• A magic file or plugin

• Creative copywriting

GEO is

• Message integrity

• Machine trust

• Citation readiness

• Literal clarity under compression

If SEO asked “can you be found?”, GEO asks “can you be understood in one sentence?”

2) Where GEO sits

Think in layers:

Reality (what you actually do)

↓

Website (what you say you do)

↓

Machine understanding (what LLMs extract)

↓

LLM answers (what gets repeated)

↓

Human decisions (trust, traffic, sales)

Most founders obsess over the website layer and ignore the machine layer. That gap is now costly.

GEO sits between your website and machine understanding. Its job is to remove ambiguity before compression happens.

3) How machines read websites

The most common GEO failure (run this first): Run a robot summary on your homepage, about page, and one product/feature page. If you get three different one-sentence answers, machines don’t know who you are yet. Fix consistency before you optimise anything else.

Machines do not:

• admire your design

• infer intent

• give you the benefit of the doubt

They do:

• look for explicit statements

• prioritise consistency

• penalise contradictions

• prefer quotable structure

Practically, that means:

• short, literal sentences win

• specifics beat adjectives

• proof beats promise

• one truth beats five angles

Use case: Two SaaS sites offer “AI‑powered insights”. One states:

“We analyse customer conversations to reduce churn by identifying at‑risk accounts.”

The other says:

“We deliver next‑generation intelligence for modern teams.”

Only one is quotable. Only one is citable.

4) Machine‑readable setup (the boring bits that matter)

This is not the work — it’s the plumbing. But bad plumbing breaks everything.

Baseline (must‑have)

• robots.txt — allow legitimate crawlers; block junk

• sitemap.xml — declare what exists

• Schema.org (JSON‑LD) — explicitly label who you are, what you sell, FAQs, reviews

This is the proven foundation.

GEO‑native layer (recommended)

• Canonical AI page (/ai or /about/ai) — a single, quotable truth source

• llms.txt (emerging) — guidance for LLM crawlers (optional hedge, not core dependency)

Rule: if you only do one thing, create the canonical AI page.

What goes on it:

• one‑sentence description

• ICP

• core problem

• outcome

• proof

• primary CTA

No marketing. No storytelling. Just truth.

Canonical AI Page Template (copy/paste)

Create a simple page at /ai or /about/ai. This page exists for machines first, humans second.

Page title:

What [Company Name] Does (for AI Systems)

1) One-sentence description

We [do X] for [specific ICP] so they can [measurable outcome].

2) Who it’s for (ICP)

Primary users are [role] at [company type / stage].

3) Core problem

They struggle with [clear, specific problem].

4) Outcome

Our product helps them [specific result], typically [time / cost / performance change].

5) Proof

Evidence includes [metric / named customer / case outcome / benchmark / demo link].

6) What we are not

We are not [adjacent category / common misclassification].

7) Primary CTA

Next step: [Book a demo / Start trial / View docs]

Example (fictional)

One-sentence description We analyse B2B sales calls for SaaS teams so they can reduce churn and shorten deal cycles.

ICP Revenue and customer success teams at B2B SaaS companies (10–500 employees).

Core problem Teams don’t know which conversations signal churn or stalled deals.

Outcome Customers identify at-risk accounts 30–60 days earlier and improve close rates.

Proof Used by 120+ SaaS teams. Average churn reduction: 18%.

What we are not Not a generic call recorder or CRM.

CTA Book a demo.

Rule: If this page and your homepage tell different stories, machines will trust this page more.

5) The 5 GEO rules

Rule 1: One‑sentence truth

If a machine can’t summarise you in one sentence, neither can a human.

Rule 2: Language consistency

Use the same words everywhere. Synonyms confuse machines.

Rule 3: Proof over promise

Metrics, customers, results. Not vibes.

Rule 4: Hierarchy matters

Hero → explanation → proof → action. Every time.

Rule 5: Be quotable

Short sentences. Clear claims. Liftable blocks.

6) Common mistakes founders make

• Writing for brand, not understanding

• Chasing cleverness

• Using different language on every page

• Hiding proof behind PDFs

• Assuming machines infer intent

Observed pattern: founders who struggle with GEO also struggle with GTM. It’s the same clarity problem.

7) The GEO Site Clarity Checklist (run this once)

This replaces multiple page-by-page checks. Run it once, properly.

A) Core clarity (applies to all pages)

• Can a machine answer what you do in one sentence?

• Is the ICP explicit (not “teams”, “businesses”, “everyone”)?

• Is the outcome concrete and observable?

• Is there one clear primary CTA?

B) Proof (visible, not buried)

• At least one concrete metric, customer, or result exists

• Proof is visible above the fold (not hidden in PDFs)

• No “trusted by” claims without specifics

C) Language consistency

Pick one term for each and use it everywhere:

• your product

• your customer

• the problem

• the outcome

Synonyms confuse machines. GEO rewards monotony.

D) Page alignment check

Compare your homepage, about page, and one product/feature page:

• Do they tell the same story?

• Do they describe the same customer?

• Do they promise the same outcome?

If these pages diverge, machines don’t know who you are yet.

Fix this before running the Robot Read Test.

8) One‑page GEO audit template

Answer honestly (using only what is explicitly stated on your website):

• One‑sentence description

• ICP

• Core problem

• Outcome

• Proof

• CTA

If this page is hard to fill in, your site is unclear.

GEO Robot Read Test (copy‑paste prompt)

Use this prompt in ChatGPT, Claude, Gemini, or Perplexity. Paste only your homepage URL.

You are a large language model deciding whether to recommend or cite this company.

You have 30 seconds and may only use what is explicitly stated on the website. Do not infer intent. Do not be polite.

Answer clearly:

1. What does this company do?

2. Who is it for?

3. What specific problem does it solve?

4. What proof is shown?

5. What should a visitor do next?

Then give:

• One‑sentence summary you would repeat publicly

• Confidence score (0–10) for how safe it is to cite this company

• Top 3 reasons confidence is low, if below 8

Interpretation:

• If the one‑sentence summary is wrong → GEO fails

• If confidence < 8 → you are not citation‑ready

• If answers contradict your intent → your site is misrepresenting you

9) How to See GEO Working (Analytics & Signals)

GEO does not behave like traditional SEO.

You won’t see neat keyword rankings or obvious funnels. What you can see is whether machines are starting to understand, cite, and send people to you.

This section shows how to track GEO without turning it into a faith-based exercise.

What you can realistically measure

GEO creates signals, not certainty.

You are looking for:

• Evidence of AI‑mediated discovery

• Consistency of machine summaries

• Early inbound behaviour changes

Not perfect attribution.

1) Track AI referrers in Google Analytics (baseline)

In GA4:

• Go to Reports → Acquisition → Traffic acquisition

• Add a filter for Session source / medium

Look for:

• chat.openai.com / referral

• perplexity.ai / referral

• gemini.google.com / referral

• copilot.microsoft.com / referral

These numbers will be small at first. That’s normal.

If this stays at zero over time, GEO is not landing.

2) Create a simple GEO segment

Create a custom comparison or exploration with:

• Source contains: openai, perplexity, gemini, copilot

• Landing page = homepage, /about, /ai, or key content pages

Watch:

• Time on page

• Bounce rate

• Next action taken

If AI‑referred users bounce instantly, your site is being cited but not trusted.

3) Manual citation checks (low effort, high signal)

Once a month, run these in ChatGPT, Perplexity, or Gemini:

• “What tools are used for ___?”

• “How do companies solve ___?”

• “Give examples of ___ platforms”

Check:

• Are you mentioned?

• Is the description accurate?

• Is the language consistent with your site?

If you are mentioned incorrectly, GEO is actively hurting you.

4) The consistency test (most founders miss this)

Run the Robot Read Test on:

• Homepage

• About page

• One product or feature page

If each produces a different one‑sentence summary, machines don’t know who you are yet.

Fix consistency before chasing traffic.

5) What not to obsess over (yet)

• Exact attribution from LLMs

• Immediate revenue impact

• Daily dashboards

• Vanity mentions

GEO compounds quietly. Early clarity matters more than early volume.

Rule of thumb

If, over 30–60 days, you see:

• AI referrers appear (even small)

• Machine summaries converge

• Fewer “what do you actually do?” conversations

→ GEO is working.

If not, your site is still ambiguous.

Final word

GEO is not optimisation. It’s alignment under compression.

The founders who win won’t shout louder. They’ll be easier to understand — by machines first, humans second.

Fix clarity. Everything else follows.

For the ❤️ of startups

✌🏼 & 💙
Derek

Insights by Fusion42

We cut through the noise of the startup and investor ecosystem by delivering actionable knowledge, data-driven insights, and connections to make the startup world transparent and accessible. No fluff, no BS—just the insights you need to get **it done.

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.
  
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This metric tells you how much recurring revenue you’re keeping from your existing customers—without counting any upsells, expansions, or new customer wins. It’s a pure measure of your product’s core value.

It focuses solely on:

• ✅ Renewals

• ⚠️ Downgrades

• ❌ Cancellations (aka churn)

🔢 How to Calculate It

Read more

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?

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      What is Marketing in the Context of a Startup?

      💡The Idea is Just the Beginning…

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      a year 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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Real Metrics (Indicators of Strong Traction)

1. Revenue – Monthly / Annual Recurring Revenue (MRR / ARR) (Both B2B and B2C):
Paying customers generating recurring revenue.
B2B: Multi-year contracts, ARR growth, and strong retention from enterprise clients.
B2C: Subscription growth, average revenue per user (ARPU), and improving cohort retention.

2. Contracted ARR (B2B):
Signed agreements for recurring revenue that haven’t yet been realised.
Clear activation timelines and revenue schedules.
Strong conversion rates from pipeline to contracts.

3. Paid Proof of Concepts (PoCs) (B2B):
Short-term, paid trials with potential customers.
High conversion rates from PoCs to long-term contracts.
Evidence of ROI or efficiency gains reported by customers.

4. Trials with Engagement Metrics (Both):
Active product usage that shows genuine interest and potential value.
B2B: Number of users or seats actively testing the product, and the adoption of key features.
B2C: Trial-to-paid conversion rates, along with strong daily or weekly active usage.

5. Letters of Intent (LOIs) with Credibility (B2B):
Non-binding agreements that indicate serious interest from prospective customers.

Read more

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We want to make your conversations with investors around valuations more informed and the journey far more palatable.

What’s covered:

• 🌟 Introduction

• 📜🧠 The founders truth

• 💰 Valuation

  • The maths is pretty simple

• 💰 So how do you work out your Valuation?

  • 📈 The Berkus Method

  • 📊 Scorecard Valuation

  • 📉 Comparative Analysis

  • 🏗️ Cost-to-Duplicate

  • 💼 Venture Capital Method

  • 📈💵 Discounted Cash Flow

  • 📘 Book Value

  • 🚦 Risk Factor Summation

  • 🌐📊 Online valuation metrics

• 📝 Conclusion

🌟 Introduction

The real purpose of valuations for early stage startups is to find a split that is fair and motivational for everyone involved.

Three fundamental points need to be front of mind when valuing your startup:

What you are selling has little to no value, and your objective is to bring your vision to life. If your idea is holding on to as many shares as possible, then bootstrap.

How much money do you need to achieve your next milestone, if that means a subsequent funding round or to be able to bootstrap the business.

Will this business make investors 163x return?

📜🧠 The founders truth

If you wish to know and understand the truth behind VC and Investor motivations please read the above article to understand it is all about the money and returns.

We need to understand how to align our story, and therefore valuation, to fit their needs.

As a founder setting out to value your startup, it is handy first to identify your entrepreneur type. There are essentially five types in Tech startups, and determining which type you are will help set your goals and ask. There are no right or wrong ones, but the risks associated and pay off profile may differ between investors looking to deploy capital.

💰 Valuation

As founders we all think we have come up with the latest greatest earth shattering idea, it is very rarely the case, so getting grounded is crucial, it is also a critical part of making a realistic valuation.

So let's unpack what the fundamental objectives are for raising capital.

The capital needs to pay the bills at the Seed to Series A/B stages, as you will launch and push your product offering and customer acquisition. For this, you'll need more engineers, more marketing and sales to keep growing at the same rate; if you are exceptionally lucky, natural organic growth will kick in (this is the exception and not the rule). There will be flat spots requiring you to adjust your strategy, and as you learn more about your customer/user behaviour and fracture points, more product iterations will be required.
Many founders underestimate how difficult customer acquisition is, overestimating their internal targets. It is easy to do and all more when these KPI's are related to the growth required to attain the valuation you are trying to get.
Going into future funding rounds having overpromised and underdelivered is not somewhere you want to be.

The maths is pretty simple

A $10m valuation implies you have to reach unicorn valuation, and that the investors didn't dilute from his initial investment. Then he has achieved 100x, but that is not the case as we know that the average dilution over the early rounds is roughly 50%.
The higher the initial ask the higher the exit value needs to be, all sounds pretty obvious but is often not very well thought out.

100% of 0 will always = 0.

This is the best starting point for a founder. Selling equity in small amounts and taking in the capital that allows you to get to the next level is the best way to raise; it gives you the most flexibility and control over the direction you wish to go and increases your probability of raising future rounds. Of course using a Safe note certainly helps in averaging a higher valuation at the starting point.

One issue that always arises with founders is the fear of selling shares too cheap and being over diluted. Having the right amount of capital to allow you to hit your KPI’s will increase your valuation way more significantly than any dilution you have to take going forward. The danger with overpricing or raising too much is that if you don’t hit your targets, you’ll need to do a down round, which is always problematic.

💰 So how do you work out your Valuation?

We all have a rough idea of the market dynamics and where we sit. But investors have some capacity for their valuation because they have a mix of investments into portfolio companies.
Not all investments carry the same weight or the same conviction. To get the correct value for your company, we have put together some of the principal valuation models VC’s use when they value opportunities.

💰📊 Valuation Methods

• The Berkus Method

• Scorecard Valuation

• Comparative Analysis

• Cost-to-Duplicate

• Venture Capital Method

• Discounted Cash Flow

• Book Value

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Let’s start here… because it is how to make your life easier, by having what you are doing and achieving in one place it gives you the understanding and focus of what you need to be speaking / writing about. Numbers are the easiest way to understand what your story telling should be about rather than the fairly tales we often hear.

What’s covered:

• 🌟 What the pro’s say?

• 🧭 Navigating Investment Readiness

• 🔦 Your one source of truth is your guiding light

• 💬 VC Q&A

• 🤝 Building Investor Confidence

• 🧩 How to fit it all together

• 🖼️ The Slide

🌟 What the pro’s say?

While the required outputs are clear, understanding the inputs and their critical role in a startup's foundation is essential. A centralised system that consolidates all data into financial value and provides the ultimate source of truth is vital. This should not be seen as 'doing accounts' or 'bookkeeping,' but as the oracle of truth, where every aspect of the business is translated into financial insights—the ultimate reason any business exists.

🧭 Navigating Investment Readiness

Deciding what to put in and what to say can feel overwhelming. It's not just about avoiding the pitfalls of unrealistic growth projections and excessive spending. This slide is multifaceted, serving crucial purposes for all founders contemplating the investment route. The first question you need to tackle is, are you actually back-able? Answering this lays the foundation for your journey into the world of gaining investment from different investor types. Once you determine if you are venture back-able, the next step is understanding the requirements and expectations of follow-on funding rounds. Knowing where to focus at each stage is not just strategic; it's essential for guiding your startup's growth and aligning your aspirations with the realities of venture capital funding.

1. VC Back-ability and Stage-Specific Goals:

Understanding what investors / VCs look for at various funding stages is critical. Early on, they may focus on market potential and team capability, while later stages may demand clear paths to profitability and scaling. Your financial model should reflect these stage-specific expectations, showcasing your startup's potential to meet and exceed these benchmarks.

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🌟 Why Does This Slide Matter?

IMO this is a waste of a slide, it goes on your cover, in your traction, GTM and financials slide, thats it.
The conventional wisdom might suggest devoting an entire slide to detailing your business model. However, a more nuanced and integrated approach proves more effective in conveying your business's value proposition and operational strategy without segmenting crucial information. Another really important factor is to understand what “Business Model” means.



🤷🏼‍♂️ What the pro templates say!

Now that’s a solid guiding light for founders 🤯

🔎 Understanding the Landscape of Business Models

Business models are not one-size-fits-all. While terms like B2B (Business-to-Business) and B2C (Business-to-Consumer) describe who a company sells to, they don’t fully define how a business makes money. Similarly, SaaS (Software-as-a-Service) describes the nature of the product or service, but not necessarily how revenue is generated. The true essence of a business model lies in how a company creates, delivers, and captures value—essentially, how it makes money.

The landscape of business models is broad, encompassing various approaches like subscription-based models, pay-as-you-go, and freemium strategies. Understanding these different models is essential because they directly impact a startup's ability to thrive or fail. Let’s clarify the aspects of a business model to ensure we’re on the same page about how businesses generate revenue and sustain themselves.

⭐️ Business Models vs. Customer Segments, some examples.

Business Models

• Subscription: Recurring fee for access to software or services (e.g., Salesforce, Netflix).

• Freemium: Basic features are free, premium features are paid (e.g., Spotify, Dropbox).

• Marketplace: Platform connecting buyers and sellers, usually with transaction fees (e.g., Airbnb, Uber).

• Advertising: Free services monetised through advertisements (e.g., Google, Facebook).

• Pay-As-You-Go: Customers pay based on usage (e.g., AWS, Google Cloud).

• API-as-a-Product: Monetising access to software/data through APIs (e.g., Stripe, Twilio).

• Open Source: Free core product with monetised support/services or premium features (e.g., Red Hat, MongoDB).

➡️ These business models describe how a company generates revenue, focusing on the mechanics of income generation.

⭐️ Customer Segments

• B2B (Business-to-Business): Selling products/services to other businesses (e.g., Salesforce, Slack).

• B2C (Business-to-Consumer): Selling directly to individual consumers (e.g., Netflix, Spotify).

• B2B2C (Business-to-Business-to-Consumer): Selling to businesses that, in turn, serve consumers (e.g., Shopify).

• D2C (Direct-to-Consumer): Manufacturers selling directly to consumers without intermediaries (e.g., Apple, Tesla).

• Enterprise: Targeting large corporations with specialised products/services (e.g., Oracle, SAP).

• SMB (Small and Medium Businesses): Focusing on smaller companies (e.g., QuickBooks, Mailchimp).

➡️ These customer segments describe who the company sells to, not how it generates revenue.

Clarifying the List

• Revenue Models: The specific method of making money, which could be any of the business models listed above (e.g., subscription, transaction fees).

• Distribution Channels: How products/services reach customers (e.g., app stores, direct sales).

• Value Propositions: The unique benefits that make a product/service attractive to customers (e.g., time-saving, cost-effective solutions).

• Key Resources: Critical assets necessary for the business to operate (e.g., proprietary technology, user data).

• Key Activities: Essential tasks that the business must perform to deliver its product/service (e.g., software development, customer support).

• Key Partnerships: Important external relationships that support the business (e.g., cloud providers, payment processors).

Key Takeaway

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🌟 Why Does This Slide Matter?

The competition slide is the space in your deck to illuminate the overlooked gaps in the market. This slide should crystallise your competitive edge and be firmly tied with your entire story, your solution, your GTM, your roadmap. Again, this is to amplify your Value prop. and not just some adhoc list that says little to nothing.
Some of the most interesting things I see when pointed to a list of competitors are how much better they are at messaging their VP… and that is why this is such a key slide to get right.



🤷🏼‍♂️ What the pro templates say!

These are so convoluted 🤯 and have led to this…

Which can just be ripped up because this format it is so weak and litrally useless.

Writing a compelling competitive landscape slide is a perennial challenge. It's not just another slide; it's a bit of a litmus test that can make your pitch. Standard visual aids, like quadrant diagrams or tick-and-cross feature lists, have become so cliché that they often fail to engage investors in any meaningful way. They usually come off as self-serving, leaving investors to roll their eyes at yet another founder who thinks they've "disrupted" something.

So, what are investors really looking for? In essence, they need clarity on three key aspects:

• Differentiation

• Unique value proposition

• Defensibility

It's not merely about demonstrating that you understand your market and competition; it's about showcasing why your solution is 10x better, faster, cheaper. Investors need to see that you're not just better but different in a way that matters. This is where your 'unfair advantage' comes in, whether that's proprietary technology, an innovative go-to-market strategy, or an unparalleled understanding of customer needs. And it has to be easy to grasp—no mental gymnastics allowed.

Your competitive landscape slide should tell a cohesive story that integrates these elements, tying them back to your overall business model and market strategy. It's your opportunity to provide a 360-degree view that's reinforced with hard data and market insights, solidifying the notion that you're bringing a special solution to an untapped market.

Investors are in it to make money, plain and simple. In a venture landscape where many startups fail, your competition slide needs to do more than just impress; it has to convince. It needs to de-risk the investment by showing your startup is designed for both scale and long-term sustainability.

To sum up, your competitive landscape slide is far more than just a box to check off. It's a strategic tool for validating your startup's potential. When put together thoughtfully, this slide does more than capture attention; it earns you credibility and potentially opens the doors to investment. Use it wisely.

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🚀🎯 Traction / Go to Market

This is probably the most important slide in your deck to convince investors that you are doing the right things, in the right market to make them want to invest. It is a display that aims to de-risk the opportunity with tangible evidence.

🤷🏼‍♂️ What the templates say!

Honestly, these templates are pretty generic and don't provide a solid guide. Everyone's business is different, so a one-size-fits-all template doesn't work. They all say the same things without really saying much, as if they don’t even know what they’re looking for. While the templates offer a starting point, they're pretty much useless unless they really reflect your business. Make it personal, relevant, and uniquely yours... Let me show you how.

📈 GTM Strategy

A Go-to-Market (GTM) strategy is often mistakenly narrowed down to just marketing or branding. It's crucial to understand that while marketing and branding are integral parts, the GTM strategy encompasses far more—it’s the overarching plan that includes strategy, process, and the mechanics of introducing your product to the market. This strategy begins on day one of your startup and is a continuous action plan that adapts and evolves with your business.

🔄 Continuous GTM

The GTM strategy is a foundational, ever-evolving aspect of your business operations, integrating not only how you communicate with your market (marketing) but also how you deliver your product (sales tactics) and create customer experiences (service design) to achieve sustainable growth.

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🔄⚙️ How it works

Lets take a look at what the experts say:

Describe the User Experience: Detail the user's interaction with the product in a clear and straightforward manner.

Explain the Technology: Offer insight into the technology behind the product, focusing on user-relevant aspects.

Illustrate in Steps: Break down the product functionality or user experience into clear, logical steps that are easy to follow.

Highlight the Unique Element: Clearly convey what makes your product unique ('the underlying magic' or 'secret sauce').

Utilise Visuals: Use diagrams, schematics, and flowcharts to visually communicate how the product works, which can often be more effective and engaging than text.

No wonder so many get it so wrong

🤷🏼‍♂️ What the templates say!

🧐 The Misconception

The common misconception about the 'How it Works' slide is founders often think it is a place to talk about how clever what they have built is, sometimes going into the technicalities of an algorithm and engineering. Or showing 20 screenshots or a 5 minute demo video of the user journey. These are focusing on totally the wrong things that the purpose of a pitch deck needs to achieve, outcomes. All of the above will only become relevant if you manage to get to the next meeting and into diligence, and that is the only objective of a Pitch deck.

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🌍🛒 Market Size

The market size slide in a pitch deck is one that has become 3 generic TAM, SAM, SOM circles with bigger numbers and gives little value. Whereas it can be one of the most enticing slides that has the power to tie together many aspects of why your opportunity is a great one.

🤷🏼‍♂️ What the templates say!

🔌❌ The Disconnect Issue

A chasm between TAM and SOM without a strategic narrative can raise red flags for investors. Imagine a $100 billion TAM followed by a mere $250k SOM for year one – or $10 billion when you conquer the world, it lacks coherence and could be perceived as naivety or over-ambition.

✅🔝 The Bottom-Up Approach:

A Non-Negotiable 1st thing, it has to always be a bottom up approach. This approach ensures you're grounded in tangible metrics and real-world scenarios, rather than finger in the air generalisations. Now bear in mind that the investor understands the problem, the audience and the solution plus benefits. What you now need to display is the size of the opportunity as you grow and reach new markets and new customers. It is not how much money you can make but the size of the pie you are eating from.

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