Use AI as leverage, not as the product (yet)
Early on, the best use of AI is often internal: accelerating your own team's output across engineering, support, content, and operations so you move faster with fewer people. That leverage is available immediately and cheaply.
If AI is your product, resist the urge to build a custom model before you've proven anyone wants the thing. Wrap an existing model, ship it, and learn. You can always invest in a custom stack once demand is real.
- Speed up your own engineering, support, and content first
- Wrap existing models rather than training your own early
- Ship a thin product layer and learn from real users
- Keep infrastructure spend proportional to validated demand
- Treat model choice as swappable, not a permanent bet
Avoid over-investing before product-market fit
The expensive mistakes are premature: hiring a specialized ML team, building bespoke infrastructure, or committing to a stack before you know what users actually value. Every dollar spent there is a dollar not spent finding fit.
Stay flexible. Model prices fall and capabilities change fast, so architecture that lets you swap providers protects you. Optimize for learning speed now and for cost efficiency later, once volume justifies it.
- Don't hire a dedicated ML team before demand is proven
- Keep provider choice swappable as prices and models change
- Watch per-user AI costs so unit economics stay sane at scale
- Instrument usage so you know which AI features users actually value
- Defer custom model training until off-the-shelf clearly limits you
- Plan for cost optimization as a later-stage exercise, not a launch one
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Frequently asked questions
Should we build our own model to stand out to investors?
Usually not early. Investors care that you've found a real problem and users who want the solution. A custom model is a cost and a risk that rarely matters before traction. Build the differentiation once you've earned the right to.
How do we keep AI costs from wrecking our margins?
Instrument per-user AI spend from day one, choose a swappable provider architecture, and optimize prompts and model tiers once you have volume. We can help design this so scaling doesn't quietly destroy your unit economics.
What's the fastest way to add AI to our product?
Wrap an existing model behind a thin, well-designed layer and ship to real users quickly. A free consultation can help you scope the smallest version that proves the value before you invest further.