Sunday Scaries: Why is your AI startup DOA? Surface area matters.
Build the system that runs the business. Everything else is table stakes.
The bar has moved. Founders who win start with an unapologetically big surface area, own the workflow, commit to outcomes with SLAs, and compose a data moat from day one. If your idea can be replaced by a better prompt or a different checkbox in someone else’s UI, it’s already dead on arrival. Build the thing that actually runs the business.
The biggest frustration I have with founders these days is their lack of recognition around the profound impact of AI on startups. The paradigm has shifted: it’s no longer “start small and iterate to a big idea,” it’s “start with a big idea and execute on it from day one.” This week alone, I’ve met four startups developing AI-driven quantity take-off solutions for estimating materials and project costs.
Here are concrete ways startups should be built now: what to build, how to scope it, and what day one execution looks like in the AI era. The throughline: stop building thin wrappers around models. Build systems, workflows, and moats that enable compounding learning and operating leverage.
Build full-stack, workflow-native products, not features
Old way: “We’ll ship an AI copilot for X as a Chrome extension and figure out the rest.”
New way: “We own the core workflow end to end, with AI embedded at each step.”
Example: Instead of an “AI RFP summarizer,” build the entire RFP OS: ingest (all formats), normalize, stakeholder routing, policy and knowledge grounding, drafting, redlining, approvals, vendor portal, analytics. AI is the engine inside a product customers live in six hours a day, not a one-off button.
Design for 10× surface area, not 10% efficiency
Ask: If the model were free and near perfect, what new work would customers attempt that they couldn’t before?
Example: Sales orgs currently personalize about 20 emails per rep per day. A modern startup should enable about 2,000 per day with compliance, brand control, channel selection, live A/B governance, and attribution. You’re selling outcomes (meetings booked, pipeline created), not “email assist.”
Make data your center of gravity
Foundation models are a commodity. Your proprietary, structured, continuously labeled data is not.
Example: A construction claims startup auto-extracts events from plans, RFIs, schedules, and site photos, then back-tests against millions of historical claims to price risk. Every case resolved improves the underwriting model. Data network effects beat feature parity.
Ship agents with guardrails, not chatbots with vibes
Agents own outcomes in bounded domains and carry state across tools.
Example: An “AP Agent” that:
Reads invoices from email, normalizes vendors, checks contracts, validates line items against POs.
Routes anomalies to a human, posts to the ERP, schedules payments, maintains audit logs, explains exceptions.
Guarantees: no duplicate payments, SLA on cycle time, SOC 2-ready audit trail.
You sell the cost per invoice closed, not seats.
Bake in system-level advantages: reliability, latency, cost
Architect from day one for:
Retrieval and tool-use grounding (RAG, function calling, constraints).
Hybrid inference (open and closed models, cost-aware routing, on-device where possible).
Evaluation harnesses (goldens, red teams, shadow prod, auto-regression tests).
Example: A medical scribe startup guarantees under 150 ms perceived latency via prefetching context, speculative decoding, and streaming. It backs claims with continuous quality evaluations scored against clinician feedback with active learning loops.
Move from demo ware to SLA ware
Put your promise in writing: accuracy thresholds, uptime, turnaround, cost per outcome.
Example: Contract analytics that commits to “over 98% clause-detection recall on our supported templates, under 2 minutes per 100 pages, and $X per contract reviewed,” with transparent dashboards and chargebacks when you miss.
Go after “unsoftware’d” workflows, not just replacing keyboards
Target processes that never had good software because the UX or complexity barrier was too high.
Example: Local government permitting. Build the whole desk: intake, dynamic requirements, code reasoning, template generation, applicant chat, inspector scheduling, appeals. AI eats the long tail of edge cases that killed prior SaaS attempts.
Pair AI with hardware or real-world actuation
Bits-to-atoms loops are still underpenetrated and defensible.
Example: A micro-fulfillment robotics startup where perception, path planning, and exception handling run on-model, while the moat is the integrated WMS, simulator, and ops playbook. Your price per order picked with uptime SLAs, not robot units.
Turn expertise into continuously improving playbooks
Capture expert decision trees. Use models to propose and humans to confirm. Close the loop.
Example: Tax credit optimization for manufacturers: an agent drafts claims, cites law, requests missing docs, simulates audit risk, files electronically, and tracks recoveries. Each audit tightens the policy. The moat is the playbook plus the outcomes dataset.
Build go to market as a product
Orchestrate an AI-native GTM: automated prospecting, persona-aware demos with synthetic data, self-serve onboarding bots, usage-based pricing aligned to business value.
Example: A security tool where a demo agent ingests a redacted log bundle and produces a tailored risk report in the browser, then sets up a trial that runs on the prospect’s environment with guardrails. Sales cycles compress from months to days.
Vertical stacks beat horizontal APIs (unless you own the loop)
If you’re horizontal, deliver non-substitutable values such as compliance, reliability, evaluations, finops, and data advantages.
Otherwise, go vertical and own outcomes in domains with messy data, regulation, and high stakes. Insurance, healthcare revenue cycle, logistics exceptions, environmental compliance, and defense maintenance are all ripe.
Obsess over unit economics from day one
Cost-aware inference routing (small models for rote, big models for rare).
Caching, deduplication, and canonicalization to reduce tokens.
Asynchronous workflows where latency isn’t valued.
Price per outcome (document closed, claim paid, issue resolved) with margin targets baked into the product.
Build with compliance as a feature, not a checkbox
First-class support for PII handling, data residency, consent, audit trails, policy grounding, model cards, and incident response.
Example: A healthcare intake agent that is HIPAA-native, includes real-time PHI redaction and provider-of-record attestation, and exports a full audit timeline.
Ship in public, learn in private
Public: velocity, community, transparent roadmap.
Private: rigorous evaluations, customer-specific fine-tunes, and data stewardship that compounds your moat.
Example: Open source the non-core platform pieces (evaluation harness, redaction, prompt router) to attract developers and harden quality. Keep domain datasets and playbooks proprietary.
Team composition: fewer generalists, more operators
Early hires who can own an entire function with AI leverage:
An ops engineer who writes SQL, builds Airflow DAGs, designs prompts, and speaks to customers.
A policy PM who encodes domain rules into validators.
Infra that can run finops and reliability.
What this looks like when done well
Construction change-order platform
Day one: ingest PDFs, plans, photos. Auto-extract quantities. Price changes. Flag spec conflicts. Auto-draft COs. Route for approvals. Track outcomes.
Pricing: sell on “COs resolved per week” with margin guarantees.
Clinical coding agent
Day one: integrate with the EHR. Process charts. Propose codes with citations. Handle payer-specific edits. Submit claims. Reconcile denials. Learn from feedback.
Pricing: price per claim closed.
Procurement copilot turned control tower
Day one: parse vendor catalogs, normalize SKUs, auto-negotiate within policy, generate POs, track delivery, reconcile invoices.
Commitments: guarantee savings and cycle-time reductions with a revenue share model.
Build the system that runs the business. Everything else is table stakes.
Spot-on. The shift from 'demo ware' to 'SLA ware' separates real AI businesses from hobby projects. Building systems that run the business requires deep operational understanding.