What to Build When Building Is Free

李笑来2026年的英文文章，暂无中文译文。

Source: https://lixiaolai.com/articles/2026-07-31/what-to-build-when-building-is-free

What to Build When Building Is Free

> By Li Xiaolai · 2026-07-31 · [Original link](https://lixiaolai.com/articles/2026-07-31/what-to-build-when-building-is-free)

*Cheap agentic labor didn't hand you an answer to "what should I make." It handed you radically cheaper search — and most people are still spending it in an armchair.*

There's a specific modern vexation: *"I can build almost anything now — so why can't I figure out what's worth building?"* The feeling is that abundance should make the choice easy. It does the opposite. This piece explains why, and hands you a method that replaces contemplation with cheap, falsifiable experiments.

It's the product of an adversarial process rather than one confident voice: a first-pass framework, a hard critique that broke several of its load-bearing claims, and dated web research to keep both honest. Where the evidence and the critique won, the original framework lost — and those corrections are the most useful part.

Start from first principles. When the marginal cost of producing a thing falls toward zero, its price gets competed toward zero too — *unless* something non-replicable sits between the buyer and the commodity. That much is standard economics, and it's why the instinct "building is cheap, so opportunity is everywhere" is backwards. Collapsing the cost of the *easy* part just relocates all the value to the parts that were always hard.

But here's the correction that matters: **AI made code and some cognitive labor cheap. It did not make the delivered outcome cheap.** The full production function behind any real business still includes understanding an undocumented workflow, getting access to systems and customers, integrating with ugly legacy infrastructure, handling exceptions, assuming liability, financing receivables, and changing how people work. None of that got cheaper. The opportunity is rarely "own a scarce asset." It's usually **"cross the last mile that everyone else refuses or fails to cross."**

The numbers make the gap concrete. **88%** of survey respondents report regular AI use in at least one business function — up from 78% a year earlier — while just **39%** report EBIT impact at the enterprise level (McKinsey, *The State of AI in 2025*, November 2025). Nearly everyone "using AI," a minority getting real profit from it — that is the whole game in one statistic. What separates the two isn't model access. It's workflow redesign, organizational change, and the unglamorous delivery work that closes the loop.

The question isn't **"what can I build?"** Building is the commodity now. The question is **"what transaction can I win, and what accumulates when I win it repeatedly?"**

The single biggest mistake in idea-selection is answering three different questions with one framework. They are not the same question, and they reward different markets, evidence, and levels of defensibility:

| The game | The real question | The wrong tool to import |
|---|---|---|
| **Cash flow** — e.g. $500K/yr owner earnings | Can I win a paid transaction, repeatably? | Venture "durable moat" requirements |
| **Durable company** | What compounds with each customer I serve? | "I need a moat before I start" |
| **Venture-scale** — power-law outlier | Can this become a category-defining monopoly? | Bootstrapper "just follow the invoices" |

A business can earn excellent owner-earnings for years on locality, convenience, relationships, operational excellence, market fragmentation, and plain customer inertia — *none* of which is a durable "moat," all of which pays a mortgage. Importing venture-scale durability standards into an ordinary cash business is how capable people talk themselves out of good money.

IMPORTANT

**Do this before anything else.** Write down your objective function: target annual earnings, acceptable time-to-first-revenue, capital at risk, maximum sales cycle, appetite for employees and operations, tolerance for regulation and liability. Without it, "what should I build?" is literally unanswerable — and specifying it eliminates most fashionable ideas on the spot.

Each of these is a plausible-sounding rule that survives right up until it meets the market. They're listed as *feels smart → actually*, because the seductive version is the one you'll reach for by default.

**Feels smart.** "No defensibility means a race to zero, so I shouldn't start without one."

**Actually.** Most early companies have no moat — they have a wedge and a hypothesis. Demanding a moat upfront makes you invent imaginary data moats, reject lucrative disequilibrium businesses, and over-build. The right pre-build question is *"if this works, what improves structurally with every customer, transaction, or month I operate?"* That's a compounding vector, not a moat you must already own.

**Feels smart.** "Sell the finished result instead of a seat and you capture the whole labor budget."

**Actually.** Outcome pricing changes *who bears the risk* — it doesn't abolish it. You inherit bad-input risk, scope ambiguity, rework, human exception handling, guarantees, working capital, and attribution disputes. It's high-margin only when the outcome is attributable, auditable, frequent, bounded, and cheap to insure. Otherwise it's disguised consulting wearing software multiples.

**Feels smart.** "Anyone can build a tool; the money is in delivering the result."

**Actually.** Wrong axis. The real distinction is **control point vs. replaceable participant.** A "tool" that becomes the system of record, the authorization layer, the ledger, or the place where work gets *approved* is the strongest seat in the value chain. An "outcome company" that calls someone else's API and hands over a deliverable may own nothing.

**Feels smart.** "Build in public, grow a following, and you'll always have distribution."

**Actually.** Most audiences are *rented* from platforms, weakly identified, price-sensitive, and attached to a personality rather than a product. Build-in-public is a channel tactic that works when your buyer lives in public networks (founders, devs, creators) and is nearly useless for hospital billing teams or municipal procurement. The durable unit isn't reach — it's a repeatable acquisition system: *trigger → reachable buyer → credible message → conversion → retention/referral loop.*

**Feels smart.** "Find existing spend and insert yourself — demand is already proven."

**Actually.** A strong default for cash flow, but it favors substitution and underweights expansion. Existing spend validates a budget while anchoring you to the old workflow and inviting competitors. Cheap AI can also *create* demand — "software for one," pervasive customization, new consumer behavior. Existing demand is the higher-probability route to cash; new categories are the higher-variance route to outliers. Don't confuse the two games.

**Feels smart.** "Start with the domain, audience, or niche you already have an edge in."

**Actually.** A fine starting heuristic that becomes a cage if you treat current access as destiny. Advantages can be *manufactured*: a year of obsessive specialization, acquiring a small incumbent, taking a frontline operating job, partnering with a licensed operator, or just doing the work manually until the hidden structure becomes visible. Access should lower your search cost, not define your search boundary.

A related trap: treating famous frameworks as one unified doctrine. They do different jobs. Thiel is about power-law venture returns; Helmer's 7 Powers explains *persistent* returns after a business system forms; Hormozi is offer construction; indie-hacker practice is low-capital search; roll-ups are about acquisition price and working capital. None is a general algorithm for what to build — each is a tool indexed to the game you chose above.

The real gift of cheap agentic labor is not cheaper *building* — it's cheaper **search**. So don't *choose* an idea from the armchair. Make three candidate markets bid for your commitment with evidence. This one process discovers demand, distribution, feasibility, exception cost, and possible defensibility together — instead of trying to reason each out in advance.

1. **Define the game.** The objective function from the previous section — cash-flow or venture target, 12-month revenue goal, max investment, max sales cycle, operational complexity you'll accept. This alone eliminates most fashionable opportunities.
2. **Find transaction-shaped problems.** Look for a workflow with an observable trigger, a named owner, a measurable completion state, frequent recurrence, expensive failure, obtainable inputs, and *detectable errors.* Weak: "improve marketing." Strong: "respond to every qualified commercial-roofing lead within three minutes, produce a compliant estimate, and book the inspection."
3. **Sell three paid concierge offers.** For each candidate, deliver the outcome with humans + agents *before* building the product. Accept only strong evidence — money, system access, data, or a binding operational commitment. Reject weak evidence: compliments, survey enthusiasm, waitlists, non-binding letters of intent.
4. **Instrument the ugly work.** Track outreach → meeting → paid-pilot conversion, time-to-close, and gross profit *after all human review*. Above all, measure the exception set — the metric that quietly kills most "AI replaces labor" theses.
5. **Choose on four gates, then build only the bottleneck. Pull:** does the buyer commit unusually fast? **Economics:** is margin attractive after exceptions, rework, and liability? **Repeatability:** can the same trigger, buyer, offer, and channel win the next ten? **Compounding:** does delivery accumulate data, integrations, trust, referrals, or workflow control? Then build only the bottleneck repeated delivery reveals — nothing more.

The clock supports this. **20%** of 2025 Stripe Atlas startups charged a customer within 30 days, against **8%** in 2020 — time-to-revenue is compressing (Stripe Atlas, year in review).

The decisive number for any agent business is *not* benchmark accuracy. It's the **cost and consequence of the residual exception set** — the cases the agent doesn't cleanly handle.

An agent that handles 95% of cases is wonderful if the other 5% are obvious and cheap to catch. It's worthless if those failures are silent, catastrophic, or need a senior specialist to diagnose. So track exceptions per transaction, minutes of human review, rework rate, false-negative cost, the skill level required to escalate, liability exposure — and, most importantly, **whether exceptions decline as volume grows.** If they don't, your "software" business is a staffing business with a worse balance sheet.

NOTE

**Why this matters more in 2026 than it looks.** The retention and margin data below aren't abstract market weather — they're the aggregate shadow of thousands of businesses whose exception economics never converged. Silent failures show up first as churn, then as margin, long after the demo looked perfect.

Price collapses to marginal cost only under restrictive conditions: homogeneous products, observable quality, cheap comparison, cheap switching, free and instant entry, unconstrained capacity, no market power, and demand that doesn't expand when price falls. Real markets violate several at once. Software already had near-zero replication cost, and Bloomberg, Adobe, and Salesforce never became free — customers pay for reliable capability, integration, and organizational legitimacy, not copied bits.

So the honest thesis is narrower than "everything races to zero": **generic capability commoditizes; delivered, trusted, context-specific performance does not automatically follow.** But the evidence genuinely cuts both ways, and pretending otherwise would be dishonest.

**The skeptic's exhibit — AI-native retention is brutal.** Revenue retention measures how much of last year's revenue survives; higher is healthier.

| Cohort | Revenue retention |
|---|---|
| AI-native — gross | 40% |
| AI-native — net | 48% |
| B2B SaaS benchmark — net | 82% |

ChartMogul's 2025 SaaS Retention Report covers roughly 2,700 B2B SaaS, 600 B2C SaaS, and 200 AI-native companies, all above $250K ARR. AI-native median gross retention climbed from 27% in January 2025 to 40% in September — improving fast, but still far below the SaaS norm.

**Margins vary enormously by cohort.** There is no single "AI margin."

| Cohort of high-growth AI companies | Gross margin |
|---|---|
| "Supernova" (fastest growers, agent/compute-heavy) | \~25%, often negative |
| "Shooting Star" (more SaaS-like) | \~60% |
| LLM-native vertical AI (Bessemer's cohort) | \~65% |

The first two cohorts and their margins are Bessemer's own, from *State of AI 2025*; the \~65% is Bessemer's aggregate for LLM-native vertical AI companies founded 2019 or later, reported in its vertical-AI thesis. One caveat in the spirit of the calibration note below: the tempting explanation — that the spread tracks how inference-heavy the work is — is my reading, not Bessemer's. They report the margins and the cohort definitions; they do not attribute the gap to inference intensity. The related fact that *is* directly reported: AI coding tools ran zero-to-negative margins until they moved to usage-based pricing (TechCrunch, Aug 2025). High revenue-per-employee is not the same as high gross margin.

Set against that skeptic case is a real optimist case. Enterprises spent an estimated **$19B on generative-AI applications versus $18B on infrastructure** in 2025, with application *startups* out-earning incumbents by roughly two-to-one (Menlo Ventures — an interested party, but the direction contradicts "all value collapses to the model layer"). Wrappers demonstrably become real businesses: Cursor went from \~$1M to roughly $2B ARR in about two years; Harvey from \~$100M to \~$300M; Sierra from zero to $150M+. The value in those cases lives in distribution, workflow, and brand — not in the commodity API they all call.

| Skeptic case (retention favors it) | Optimist case (revenue & valuation favor it) |
|---|---|
| AI-native retention of 40% gross / 48% net vs. 82% net for SaaS (ChartMogul) | $19B app spend > $18B infra; app startups \~2:1 over incumbents (Menlo) |
| Switching costs, network effects, intangible assets, and efficient scale show "almost no predictive power" against AI risk; only physical cost advantage still protects (Helfert / Westwood, in Morningstar, Feb 2026) | Cursor, Harvey, Sierra — wrappers on commodity APIs become billion-dollar businesses |
| "No inherent endemic moat in the tech stack" (Martin Casado, a16z) | Casado's own update: "brand is emerging as a real moat"; distribution often beats the AI itself |

**Genuinely unresolved:** whether AI-native low retention is a life-stage artifact that matures out (gross retention *did* climb from 27% to 40% between January and September 2025) or a structural verdict on substitutable products. No multi-year cohort data settles it yet. What plausibly survives commoditization: the control point, the last mile of exceptions and liability, atoms-and-regulated operations, and a data-feedback loop that *measurably* widens a lead — the last being the most over-asserted, least-proven moat of all.

WARNING

**Calibration note.** The viral figures — "80% of AI wrappers fail by 2026," "OpenAI cannibalized 200+ funded startups," "65% churn in 90 days" — recur across blogs without traceable primary sources. Treat them as folklore, not evidence. The numbers cited in this article are sourced and dated; these three are deliberately excluded.

"How do the pros think about this" has no single answer, because they're openly split. The useful move is to see the fault lines and place your own bet knowingly:

| Fault line | Most likely resolution |
|---|---|
| **Apps vs. models/incumbents** capture the value | Both. Generic horizontal wrappers get crushed; apps survive where they own write-access, workflow redesign, liability, or proprietary feedback. |
| **Agents replace SaaS** vs. strengthen systems of record | UIs and routine actions migrate to agents; systems of record persist. The prize is becoming the *system of action with permission to write back.* |
| **Services-as-software** vs. high-margin SaaS | Services-as-software wins in narrow, auditable workflows with falling exception rates; disappoints in ambiguous knowledge work where quality is subjective and failures surface late. |
| **Existing demand** vs. new-category vision | Existing spend = higher-probability cash; new categories = the biggest outliers but demand real tolerance for timing risk. Different games — don't blend the advice. |
| **Moat-first** vs. speed-first | Speed-first, *with a compounding hypothesis.* "Moat before product" is premature; "growth with no path to power" is equally naive. |
| **Pure software** vs. atoms & regulated markets | Best risk-adjusted bets are often hybrids where AI materially improves a physical or regulated operation — the plumbing *operator* with better utilization, not "AI software for plumbers." Pure software keeps the biggest upside but faces the fastest cloning. |

Stop trying to identify a permanently defensible **idea.** Identify a transaction you can win **now**, prove its full-stack economics **after** exceptions and liability, and ask whether winning it repeatedly **causes** a stronger position to accumulate.

Profit comes first from solving and selling. Defensibility is a *consequence* of repeated solving — not an admission ticket you need before you begin. Cheap agents didn't answer "what should I build." They made the search radically cheaper, and the professional response is to spend that gift on falsifiable paid experiments instead of contemplating harder.

1. Write your objective function in one paragraph. Which of the three games are you playing?
2. List three transaction-shaped problems you can already reach a buyer for.
3. For the strongest one, write the concierge offer and ask a real person for money, access, or a binding commitment this week.
4. Deliver it by hand with agents in the loop. Instrument the exception set from the first transaction.
5. Only after pull + economics + repeatability hold, build the one bottleneck that repeated delivery exposed.

A first-pass framework was written, then stress-tested against an independent adversarial critique that broke several of its claims — the "name the moat first," "outcome = high margin," and "tool = weak" reversals all came from that pass. Both were then checked against dated web research across three angles: business models, defensibility, and concrete playbooks. Where evidence contradicted the framework, the framework was corrected. Figures are attributed and dated; three widely-circulated statistics were excluded for lacking traceable primary sources.

Before publication, every load-bearing figure was read back against its primary source rather than trusted from a summary. The McKinsey adoption and EBIT numbers, the ChartMogul retention figures and sample composition, the Bessemer cohort margins, and the Menlo application-versus-infrastructure spending all match their sources and have been rewritten here in the sources' own terms. That pass changed two things: a margin row originally labeled "inference-light vertical AI" turned out to be Bessemer's aggregate vertical-AI cohort, and the inference-intensity explanation turned out to be mine rather than theirs. Both are now marked as such. The Morningstar moats claim also checks out, in its author's own words: Adrian Helfert, chief investment officer of multi-asset strategies at Westwood, scored 460 S&P 500 companies on moat strength and AI risk and found that four of the five classic pillars — switching costs, network effects, intangible assets, efficient scale — "have almost no predictive power in today's AI environment," leaving only physical cost advantage. Worth knowing before you lean on it: that study was, at the time of the interview, still forthcoming on SSRN. It is the single most striking anti-moat number in this article and it rests on work not yet public — which is exactly the sort of thing a reader deserves to be told rather than left to discover.

This is the same discipline I described in No Verification, No Claim: in a field with no mechanical check, the only honest defense is to say plainly which numbers are sourced, which are folklore, and which questions remain open. Everything here is reasoning under exactly that constraint — argued, sourced, and dated, but unverified in the strong sense.

**Business models & pricing**

- Sequoia — Services: The New Software (Mar 2026)
- Foundation Capital — The $4.6T Services-as-Software Opportunity: Year One (Jul 2026)
- a16z — Software is Eating Labor (2025)
- Bessemer — The State of AI 2025 & The future of AI is vertical
- Menlo Ventures — 2025: The State of Generative AI in the Enterprise
- TechCrunch — High costs and thin margins threatening AI coding startups (Aug 2025)

**Defensibility & moats**

- ChartMogul — SaaS Retention Report: The AI Churn Wave (2025)
- Morningstar — Why This Fund Manager Says AI Threatens to Destroy Company Moats (interview with Adrian Helfert of Westwood, 27 February 2026; the underlying study was forthcoming on SSRN at the time)
- a16z — Where Value Will Accrue in AI (Casado & Wang)
- Y Combinator — The 7 Most Powerful Moats for AI Startups
- NFX — AI defensibility & Why AI is bigger than SaaS

**Playbooks, data & operations**

- McKinsey QuantumBlack — The State of AI in 2025: Agents, innovation, and transformation (November 2025 global survey; the 88% and 39% figures above are quoted from its key findings)
- Stripe — Stripe Atlas startups in 2025: year in review
- Sapphire Ventures — The biggest vertical AI markets are hiding in plain sight
- Levels.io — Indie hackers build AI factories but have no money or traffic
- Forbes — Every Company Is Now an AI Wrapper, So GTM Is the New Moat (Jun 2026)

Investor essays cited here are directional and often self-interested — a fund describing the category it invests in. TAM figures such as "$4.6T services" or "$13T labor" are framings, not measured revenue. Retention, margin, and spend figures are the more load-bearing evidence and are dated to their source. Treat everything as of mid-2026; this is a fast-moving field.