LLM-wrapper startups and their venture capital backers are betting big on a shaky foundation. The assumption that inference costs will keep falling indefinitely is creating a dangerous blind spot. Let’s break down why this gamble could backfire—and what you can do about it.
Why This Matters Now
Startups building on third-party large language models (LLMs) often operate with razor-thin margins. Many assume that the cost of running these models will drop fast enough to sustain profitability. But this overlooks the growing complexity of user demands. As users expect more advanced outputs—like generating entire business plans or producing high-quality creative content—the number of tokens processed per session skyrockets.
Key Insight: The cost of simple queries might fall, but complex tasks require exponentially more resources.
The Two Scenarios You Need to Understand
Predicting future AI usage is tricky, but there are two likely outcomes:
- Cheap Inference Wins: If costs plummet to near-zero, everyone benefits—but this is far from guaranteed.
- Scaling Complexity Takes Over: As users demand deeper reasoning and longer sessions, costs could spike instead of dropping.
Neither scenario guarantees survival for companies banking solely on shrinking COGS.
Where Startups Go Wrong
Many startups depend too heavily on external models without investing in differentiation. They focus on reselling or wrapping existing tech rather than innovating. This approach works only if:
- Inference remains dirt cheap.
- User expectations stay low.
But history shows us that user expectations always rise. When they do, startups stuck at the mercy of third-party pricing find themselves in trouble.
Actionable Tips to Avoid the Trap
- Diversify Your Tech Stack: Don’t put all your eggs in one basket. Explore multiple LLM providers or even build proprietary components.
- Focus on Value Creation: Offer unique features or insights that go beyond what raw LLM output provides.
- Monitor User Trends: Keep an eye on how your audience uses your product. Are they asking for more complex outputs? Plan accordingly.
- Invest in Efficiency: Optimize your workflows to reduce token consumption where possible, such as caching results or pre-processing inputs.
- Prepare for Higher Costs: Model your financials assuming inference costs won’t drop significantly. Stress-test your business plan against rising expenses.
What Is Next?
If you’re running an LLM-wrapper startup or considering investing in one, take a hard look at your reliance on cheap inference. The market is shifting faster than many realize. By diversifying your strategy and focusing on sustainable value creation, you can avoid being caught off guard when the winds change.
Remember: Surviving in AI isn’t just about riding the wave—it’s about steering through the storm.