Business Models
AI Product Building48 insights in this topic
48 insights
Context is the product, not the model
Anyone can call the API — differentiation comes from the data you access, skills you build, UX you design, and domain knowledge you encode
AI compresses the distance between idea and execution but not between good and bad judgment
When everyone can build anything, the differentiator stops being speed and starts being judgment — what to build, what to say no to, when to change course
Domain-specific skill libraries are the real agent moat, not core infrastructure
An elite team can replicate any agent's tool architecture in months, but accumulated domain workflows (LBO modeling, compliance, bankruptcy) represent years of domain expertise
Proprietary feedback loops create moats that widen with every interaction
When usage generates data that competitors cannot replicate — correction patterns, preference signals, domain-specific edge cases — the product improves faster than any new entrant can catch up
Sell the work, not the tool — the services budget dwarfs the software budget, and every model improvement helps whoever sells the outcome
Copilots sell tools to professionals; autopilots sell outcomes to end customers and access the vastly larger services TAM from day one
LLMs selectively destroy vertical software moats — 5 fall, 5 hold
Learned interfaces, custom workflows, public data access, talent scarcity, and bundling collapse under LLMs, while proprietary data, regulatory lock-in, network effects, transaction embedding, and system-of-record status remain defensible
When non-deterministic AI feeds a deterministic system, the deterministic layer governs — software that owns coordination and accountability keeps its place
When non-deterministic AI feeds into deterministic systems (databases, approvals, audit trails), the deterministic system governs; SaaS is that system
B2B becomes B2A — agents become the buyer
Software is increasingly consumed by agents, not humans; the agent recommends, the human approves
The system of work is the moat, not the model — the model is fungible underneath
A vertical app company wins by owning the surface where a company's work actually executes — data capture, workflow system of action, and governance — while each new model generation flows through underneath
Closed harnesses behind APIs create memory lock-in by design
When the harness lives behind a proprietary API, memory state and schema become invisible and non-portable — model providers are incentivized to push more of the harness behind their APIs
The context flywheel is a Day 90 moat — Day 0 comparisons are misleading
Point-in-time capability benchmarks miss the compounding advantage: on Day 0 a raw model matches your product, but by Day 90 accumulated context creates an unbridgeable gap
Revealed preferences trump stated preferences — track what users do, not what they say
Users' actual behavior (what they click, skip, edit, redo) is the ground truth for product decisions; stated preferences in surveys and interviews systematically mislead
Cap headcount, not compute — token spend per engineer replaces headcount as the scaling unit
At $1,000/month per engineer as table stakes, top engineers manage 20-30 agents simultaneously; R&D scales through compute investment, not hiring
Inference-time compute makes cost-per-outcome a choice — and that's the application layer's counterattack on the labs
No prior software had a dial where 10x more compute buys a better answer; a 10-second and a 10-minute query on the same model are different products at different prices. Margin depends on the system's judgment of where to spend tokens, not on model pricing — the lab wants to expand usage, the application wants to spend only where the outcome is worth it
Self-disruption follows the value chain downward — software companies must eat their own agent layer before someone else does
Intercom deliberately disrupted their software business with agents, and now disrupts their agent business with AI models, because value accrues to the model layer
The 80/99 gap is where AI products die — demo accuracy and production reliability are infinitely far apart
Getting an AI system from 80% demo accuracy to 99% production reliability requires fundamentally different engineering than the first 80% — most teams underestimate this gap by orders of magnitude
Model-market fit comes before product-market fit — without it, no amount of product excellence drives adoption
AI startups need a prerequisite layer beneath PMF: the capability threshold where models can actually satisfy market demands. Legal AI crossed it at 87% accuracy; finance AI at 56% hasn't — same demand, opposite outcomes.
Open harnesses with customer-owned databases are the antidote to model-provider lock-in
An open, model-agnostic harness that stores memory in a database you control (Postgres, Mongo, Redis) keeps both model choice and memory portable
When the system routes information, IC and DRI are the only roles left
Blomfield's org prescription for AI-native companies: no directors or VPs, everyone an individual contributor who brings working prototypes rather than decks, and a single directly responsible individual instead of a committee
The price of intelligence is the new organizing axis — labs, applications, and countries are all fighting to set it
The cost of intelligence is no longer an input to software but the axis around which companies, markets, and geopolitics reorganize; labs want usage routed through them, applications want to allocate intelligence better than labs, countries want it cheap enough to be national infrastructure
The UI moat collapses — API quality becomes the purchasing criterion
When agents are the primary users of software, beautiful dashboards stop mattering and API design becomes the competitive surface
Commodity work's terminal value is zero but structured expert judgment compounds indefinitely
Appen collapsed from $4.5B to $140M as LLMs displaced commodity annotation, while Scale AI reached $29B by owning expert alignment infrastructure — the market is bifurcating
Institutional slowness is the runway, not the threat
Tan inverts the standard AI-doom framing: the bureaucracy and cognitive limits of every company, institution and government are what give builders a two-decade window, and he says this openly against the Silicon Valley consensus that startups will replace incumbents
In AI the threat is layer migration, not a competitor — work relocates across layers when any variable moves
In SaaS a rival company killed you; the one exception was platform dependency, where a pricing or terms change could wipe you out. AI makes that exception the default — work migrates into the model, an open-weight alternative, the customer's data platform, an agent runtime, or the device itself
Routing across the whole model market — and absorbing every migration — is a defense the labs can't copy
A vertical company picks the best model per sub-task across all vendors, absorbs eval/migration work on every upgrade, and sells the lowest cost for the exact intelligence each step needs
Own your skill files, because if you don't, your job becomes a skill file
Tan's political thesis: a skill file is your cognition extracted and made executable, and the same 40 files are either a career that compounds or an extraction that outlives you — the only variable is whose repo they sit in
The comfortable middle is over — software companies must either accelerate AI growth or rebuild for 40%+ margins
Growth-path companies ship AI-native products in 4-person pods with token-based pricing; margin-path companies flatten management, raise prices, and let low-value customers churn — anything in between faces multiple compression
Task horizon breaks seat-based pricing — usage scales with workflow depth × length, not headcount
Task horizon is the length dial: how long an AI works on its own before a human steps in. The unit shifted from the call to the workflow — agents run for hours, spawn sub-agents, and burn millions of tokens per decision path, so usage stops scaling with seats; multiply length by depth to get the token bill
Deliberately overspending on inference buys lead time, not efficiency
Tan runs agents at full strength — 800,000 to a million tokens loaded into a single request — and describes the result as getting to live in 2028 today; the spend is uneconomic per task and that is the point
The intelligence lives in the workflow, not the model — and a model can't simply read it
In a real vertical, the decisive logic (what to escalate, which rule wins, when a human signs off) lives in SOPs and operational experience; the agentic workflow encodes it and becomes the carrier's operating memory
If a problem improves directly with raw model capability, the labs will take it
The Yellow Brick Road test — work that gets better with every pre/post-training dollar (code, writing, images) belongs to the labs; work whose value comes from scaffolding is defensible
Guardrails aren't just safety — they're what the customer is paying for
Per-use-case, per-customer, continuously-audited governance is the product in a regulated vertical; becoming the compliance control plane is a moat a horizontal player can't credibly hold
LLM competition fragments markets from 3 incumbents to 300
When LLMs lower the cost of building vertical software, competition doesn't add one new entrant — it explodes combinatorially, explaining market repricing before revenue loss
The sovereignty test — can you swap out a generalist model without losing your 'company veteran' expertise?
A firm controls its IP only if it can switch the underlying generalist model while keeping the company-veteran expertise built into its learning system; that portability is the test of control and sovereignty in the AI era
AI won't destroy SaaS moats — it'll make the biggest ones even bigger
Enterprise SaaS consolidates rather than fragments: we could see 5-10 individual trillion-dollar SaaS companies. Moats are people, relationships, and enterprise integrations — not code. Cheaper AI-built software doesn't overcome distribution advantages.
The first enterprise-AI sale is a trust sale — buyers judge de-risking, not capability
Early AI deployments are bought on reliability, control, and whether the system can be trusted in real workflows; founders systematically over-index on demonstrating capability and under-index on de-risking the buyer
Vertical models beat frontier models in their domain — specialization wins on every metric
Intercom's Apex, a specialized customer service LLM, beat every frontier model including Anthropic and OpenAI on resolution rate, latency, hallucination rate, and cost
The data flywheel is a UX problem — only vertical workflow surfaces can capture the knowledge
Two stacked flywheels (across-customer pattern recognition + within-customer tacit rules) accrue only through workflow-specific capture surfaces that horizontal tools structurally cannot shape
A frontier without an ecosystem is not stable — if a few models capture all value, the political economy won't tolerate it
The priority must be building a frontier ecosystem, not just a frontier model, so value flows broadly across companies and industries; concentrating all returns in a few models that hollow out industries has no societal permission and is not a stable equilibrium
Knowing what to build is the scarce half, so agent leverage favors veteran founders
Tan reports a mega trend of founders in their late thirties and forties who have been around the block; the multiplier applies to whatever judgment it is given, so multiplying someone who already knows what to build can outperform a department at a large tech company
Every firm must build two capitals — human capital and token capital — and they compound together, not at each other's expense
Human capital is the knowledge, judgment, relationships, and pattern recognition of a firm's people; token capital is the AI capability it builds and owns — and human capital only becomes MORE valuable as token capital grows
Platform economics beat labor arbitrage — margins fund flywheels that body shops cannot
Scale AI's 50%+ gross margins fund ML pre-labeling and workflow optimization, creating a flywheel; Indian BPOs at 10-15% margins cannot invest in R&D and remain trapped competing on price
System or tool? Ask whether the customer would still need you if a lab shipped a direct competitor
Three tests for being safely off the Yellow Brick Road — tools-and-steps, system-vs-tool, and customer-P&L — with the system test (would they still need you?) as the sharpest discriminator
Where inference runs decides who captures margin, owns the context, and earns trust
Value won't all accrue to the cloud — inference moves to wherever it's cheapest without breaking the product: cloud for frontier reasoning, edge for latency, on-device for privacy. Privacy matters more than in SaaS because the model isn't just storing data, it's reasoning over the user's context, memory, code, and permissions
Latent demand is the strongest product signal — make the thing people already do easier
People will only do things they already do; you can't get them to do a new thing, but you can make their existing behavior frictionless
LLMs complete Aggregation Theory by collapsing the interface layer
Ben Thompson's framework reaches its final chapter: LLMs eliminate the interface layer that protected software suppliers, turning the entire web into a backend database where suppliers compete on data quality alone
Open source captures value through services, not software
Free software builds billion-dollar companies because the money is in support, cloud, and governance layers — not the code itself
Already-outsourced tasks are the autopilot wedge — vendor swap beats reorg
If work is already outsourced, budget exists, external delivery is accepted, and the buyer purchases outcomes — substitution is frictionless