Future of AI
AI Product Building36 insights in this topic
36 insights
Decision traces are the missing data layer — a trillion-dollar gap
Systems store what happened but not why; capturing the reasoning behind decisions creates searchable precedent and a new system of record
B2B becomes B2A — agents become the buyer
Software is increasingly consumed by agents, not humans; the agent recommends, the human approves
SaaS survives as the governance and coordination layer — determinism still rules
When non-deterministic AI feeds into deterministic systems (databases, approvals, audit trails), the deterministic system governs; SaaS is that system
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
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
Sell the work, not the tool — model improvements compound for services, against software
If you sell the tool, you race the model; if you sell the outcome, every model improvement makes your service faster, cheaper, and harder to compete with
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
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
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.
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
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
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
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
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
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
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
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 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
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
Autopilots capture the work budget — six dollars in services for every one in software
Copilots sell tools to professionals; autopilots sell outcomes to end customers and access the vastly larger services TAM from day one
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
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
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
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
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
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
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
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
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
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
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