The 7 Laws of Shipping AI Products in 2025

Patterns from the frontier, drawn from dozens of teams building on GPT-4o, Claude 3.5, Gemini 2.5, and Groq.

Across many interviews with AI builders, the same seven patterns keep emerging. Think of them as field-tested heuristics for anyone working with frontier models.

For a decade we built tools to analyze. The next decade belongs to tools that act. As the fat brain eats the backend, the scarce asset isn’t infra—it’s judgment about what interventions users actually want, and the labeled feedback loops that prove they worked.

Here’s the list.

Note: These aren’t stone-carved commandments. They’re the most consistent things I’ve heard. Treat each one as a hypothesis—run a quick spike, gather metrics, and keep only what numbers confirm.

Everything in prompt, nothing in RAG

Last year we stuffed PDFs into vector DBs and prayed the RAG pipeline didn’t 500. This year million-token windows plus sub-$0.20/M-token pricing on the cheapest models let teams jam ledgers, BOM*, or lifetime health records into a single call. Retrieval plumbing vanishes.

Example: Peek loads 24 months of transactions on day 1—no chunking, no sync jobs.

Why it matters

• Idea → prototype in days, not quarters.

• Engineers shift effort from infra wrangling to UX polish.

*BOM (Bill of Materials): the master parts list for a hardware product, used for cost, sourcing, and compliance.

Dashboards out, do-boards in

Seeing is cheap; doing is defensible.

Why it matters

When software leapfrogs “read” and goes straight to “do’, a few things happen:

1. Compounding loops save time and make models smarter by feeding proprietary action data

2. Outcome pricing (charge on savings, not seats) cracks “saturated” markets wide open.

Backend = thin shell, model = brain

Instead of a heavy rules engine on the server, the model is the business logic.

fetchData → callModel(tool_calls=True) → pushResult

That’s the stack. Rules engines, cron ETLs, and micro-service mazes shrink to a controller.

Example

Two-person team at Chronicle shipped “URL-to-deck” in four weeks; legacy incumbents still wrestling templating code.

Why it matters

• Tiny teams out-iterate decade-old incumbents.

• Legacy tech debt flips from moat to millstone.

Push beats pull

Always-on agents ping you the moment something changes. Always-on agents alert you the second something changes; dashboards, cron jobs and weekly exports feel ancient next to event-driven “heads-up” UX.

Example: Yutori Scouts monitor dozens of sites and alert only on change events.

Why it matters

• Products earn retention without hijacking attention.

• “Set it and forget it” UX widens the funnel to non-power users.

Sub-second = human-paced convos

Groq Llama’s first token ≈ 300 ms; GPT-4o ≈ 350 ms—inside the 250-400 ms “feels instant” band (i.e. the “conversation ok” threshold).

Why it matters

• Voice agents, live coaching, and customer support cross the uncanny valley.

• Latency, not accuracy, becomes the next competitive lever—expect on-device inference to surge.

Router is the new load balancer

Stacks juggle GPT-4o for accuracy, Llama-3 8B for cost, Claude for long context. The switchboard trades pennies for quality on every call.

Why it matters

• Single-model moats evaporate; orchestration logic + feedback data become the edge.

• Expect a wave of “Twilio-for-LLMs” startups abstracting multi-model routing, retries, and fallbacks.

Reliability Layer = Eval + Memory + Trust

LLMs can think; we still can’t grade or remember their work.

Why it matters

• Turnkey memory will spark a Cambrian explosion of “remembers-you” products. It’s also likely to erase another infra layer.

• Whoever nails plug-and-play eval + compliance logs for LLM output will sit at the choke-point for every regulated use-case—finance, health, legal.

The macro picture: vertical collapse

Layers that looked like backend complexity in 2024—RAG, ETL, business logic—are collapsing into the model. New layers—routing, eval, memory—float higher up the stack.

• Builder: hunt for workflows where where read → do.

• Investor: price upside, not MAUs. Tomorrow’s moats are (outcome) data and trust

Software finally learned to close its own loops. It’s time to redesign our products—and our business models—accordingly.

Closing checklist

Treat each of these patterns as a hypothesis:

1. Spike: load your dataset into one prompt or wire up a router for a week.

2. Instrument: log token spend, errors, user-accepted actions.

3. Decide: keep what the metrics confirm, discard the rest.

Building something that hits one of these laws? DM me. I want to see it. Investors wanting to compare notes, welcome too.