Future of AI
AI Product Building66 insights in this topic
66 insights
Systems of record keep what was decided, not why — capture the reasoning at the moment a decision becomes binding
Systems store what happened but not why; capturing the reasoning behind decisions creates searchable precedent and a new system of record
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
When products and models converge, the moat is the institution — how a company attracts people, distributes authority and compounds judgment
When models improve fast, interfaces converge, and product velocity becomes cheap, the durable advantage moves to how a company attracts exceptional people, distributes authority, and compounds judgment over time
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 gains come from redesigning work around AI, not bolting AI onto human workflows
Like factory owners who first swapped waterwheels for steam engines and changed nothing else (modest gains), today's orgs bolt chatbots onto human-designed workflows — the explosion comes only when the work is redesigned around agents
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
Build for the model six months from now, not the model of today
AI product builders should target the capability frontier the model hasn't reached yet, because today's PMF gets leapfrogged when the next model ships
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
AI strategy is a self-rewriting equation — solving one constraint changes which constraint matters next
SaaS metrics were downstream of just two forces (distribution cost + switching cost); AI has many coupled variables — capability, cost, latency, deployment, regulation, talent — each decomposing into sub-curves, so the equation rewrites itself faster than any fixed playbook can track
Frontier companies absorb every useful agentic pattern into base products
If a workaround truly extends agent capabilities, OpenAI and Anthropic — the biggest power users of their own models — will build it in, making external dependencies temporary
Technology transitions create more of the 'dying' thing, not less
Every predicted death — mainframes, physical retail, traditional media — resulted in growth of both old and new; AI will create more software, not less
AI is steel for organizations — when software carries the context, human communication stops being the load-bearing wall
Before steel, buildings capped at six or seven floors because iron buckled under its own weight; AI that maintains context across workflows removes human communication (meetings, messages) as the structure that caps how far an org can scale before it degrades
Reason backward from an outcome you want to exist — it manufactures originality that absorbed problems can't
Absorbed problems hand you the conclusion without the reasoning, on a crowded racetrack; choosing an outcome you genuinely want and reasoning backward to the experiments drags you into territory no survey paper covers
You can offload a task, or even a job, but you can never offload your learning
The real opportunity isn't picking the best model — it's building a learning loop on top of models where the firm's accumulated learning, the one thing it can't outsource, compounds across people and AI
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.
Personal AGI is an asset you build, not a subscription you rent
Tan's distinction: a rented assistant resets when you close the tab and improves only when its vendor ships, while an agent on your own infrastructure reading memory you own gets better every day you use it
Permissioned inference is harder than permissioned retrieval — enterprise context graphs need reasoning-level access control
Controlling who sees data is solved; controlling whose history shapes reasoning for others is the unsolved trust layer enterprise context graphs require
AI automation amplifies demand for expert human judgment rather than replacing it
Pre-labeling cuts costs 100,000x for simple tasks, but projects that needed 500 contributors now need 100 doing far higher-value work at up to $200/hour
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
Sand vs Stone — if models double in capability tomorrow, what washes away and what remains?
Framework for evaluating AI product durability: context flywheels and domain expertise are stone; model workarounds and clever engineering are sand
A better model makes your library worth more, not obsolete — a smarter reader extracts more from the same books
Tan's answer to the bitter-lesson objection: each model release is a free upgrade to a workforce you already own, because the differentiator moves toward context precisely as the weights commoditize
Agent trust transfers from human credibility — colleagues adopt agents operated by people they trust
When a human's agent consistently performs well, other team members inherit that trust and willingly depend on the agent, creating a credibility chain
Building in AI is running a trading book — you're long some curves, short others, and exposed to correlations that break when they matter
Value in AI is never captured once and defended; it's continuously repriced and relocated. Durable companies know which assumptions they're long and which they're short, choose which variables to bet on, know which can kill them, and build to recover faster than a wrong bet can compound
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
Don't be the discriminator — be the patron, not the judge
Taste (selecting from AI output) is the function that gets automated first; participating in creation through friction and will is what endures
Engineering is no longer the junior partner — at the frontier, research and engineering have fused
The researcher who can build the harness, the eval, and the data pipeline is the one whose hypotheses actually get tested; everyone else waits in a queue. The split between 'has ideas' and 'can run them' has collapsed into one role
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
The intelligence-to-judgement ratio determines which professions AI automates first
Intelligence work (complex but rule-based) is already automatable; judgement (experience, taste, intuition) remains human — software engineering crossed the threshold first
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
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
AI's self-improvement loop means each generation builds the next one faster
GPT-5.3-Codex was instrumental in creating itself — recursive improvement compresses timelines and explains why building for obsolescence is the only safe strategy
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
Humans move to the edge where the model touches reality — a company brain that can't touch the world is just a database
Quoting Jack Dorsey, Blomfield puts people at the perimeter doing what models cannot — intuition, trust, ethical and existential calls — while the routing of information moves into the system
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
Malleable software — a tiny core that writes its own plugins — replaces fixed-feature applications
Instead of adapting your workflow to the tool, the tool observes your workflow and extends itself to match it
New technology first imitates the medium it replaces — the transition form hides the final form
Early phone calls were telegram-terse, early movies were filmed stage plays, and today's AI is a chatbot mimicking a search box; McLuhan's 'driving into the future via the rearview window' is why we mistake the imitation phase for the destination
Agents eat your system of record — the rigid app was the constraint, not the schema
When agents can clone your entire CRM in seconds and become the real interface, the SaaS product becomes a dumb write endpoint. Data moats evaporate because agents eliminate the rigid app that demanded rigid schemas.
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.
Technical knowledge can become a liability when working with AI
Experts get stuck on implementation details while novices describe outcomes and ship faster
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
Your first subfield is an accident of timing — wander across several before you settle, because breadth is insurance
Pay tuition in interpretability, evals, rl, and systems before deciding where you live; somewhere is a corner where your specific weirdness is an unfair advantage. Subfields all saturate, usually right after they peak on twitter, and breadth is what carries you through the transition
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
A clear public explanation is a genuine contribution and an unfakeable credential
Fields choke on undigested ideas, so distilling something hard into a clear explanation is real work, not a service job — and a body of public writing doubles as the strongest credential you can hold, because it's an unfakeable sample of how you think
Deputies and Sheriffs — distributed agent teams with hierarchical authority replace centralized software
Individual employees train specialized 'Deputy' agents while organizational 'Sheriff' agents manage permissions, rules, and onboarding across the team
Every role codes when implementation cost drops to zero — the generalist builder replaces the specialist engineer
When AI handles implementation, the title 'software engineer' gives way to generalist builders who code, write specs, design, and talk to users
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
The first-draft pattern is the killer app for long-horizon agents — agents produce, humans review
Long-horizon agents produce comprehensive first drafts (PRs, analyses, reports) that humans verify — this is where the 10x productivity gain actually lives
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
Personal software grows through relationship, not configuration
Unlike traditional SaaS where users adapt to the tool, personal software agents grow personality and skills in response to their user through ongoing interaction
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
Agents running the platform vs. agents on the platform — the operator shift changes what software must be
The shift from agents as features inside your product to agents as operators running your product — passengers become pilots
AI erases the API line — the side that takes orders can now negotiate
Venkatesh Rao's rule was to fight your way above the API line, because below it you do what the API tells you; Tan argues agents dissolve the line, citing a YC company that shipped a bug-report endpoint built for agents rather than humans, which replies in real time with a triage decision and a workaround
Enterprise agents need deterministic structure while startups need autonomous loops — same models, different harnesses
Enterprises need deterministic graph-based harnesses for predictability; startups benefit from autonomous loop agents — the harness choice, not the model, determines reliability
Don't presume the tradeoff — do all of it and force reality to show you where the real constraint is
Anthropic's cultural belief that tradeoffs are not real: instead of reasonably ranking priorities against each other in advance, attempt everything and let reality — not your own assumption — reveal what actually can't be done
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
Software abundance unlocks entire categories of applications that never existed
Software has always been more expensive than we can afford; when AI drops costs 10-20x, previously unviable software becomes economically possible
Stronger models expand the verification gap, not close it
More capable models increase the deployment surface and raise the stakes of failures, making verification infrastructure more valuable rather than less
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