You're an AI Engineer. Are You Building a Model or a Wrapper? (73% of Startups Have Their Answer)
I recently came across an analysis that should be mandatory reading for anyone working in or funding the current wave of "AI-first" companies.
The report reverse-engineered 200 funded AI startups and found a shocking number: 73% are misleading the public about their core technology. They aren't running novel, proprietary models; they are wrappers built around third-party APIs like OpenAI and Claude.
The core number is what matters, and after doing a quick check, this claim is highly believable and widely supported by reports across the tech industry. Many companies that market "proprietary AI" are essentially building user interfaces on top of existing foundational models.
The Reality of "AI Engineering" Today
The title "AI Engineer" suggests complex model training and architecture. But the reality in many of these funded startups is far simpler—and more honest:
- API Management: We spend time dealing with rate limits, latency, API version changes, and failure modes from external services.
- Data Piping: We build robust pipelines to clean, structure, and funnel data into the third-party LLMs.
- Prompt Tuning: Our innovation is often in optimizing the prompt engineering layer, not the model layer.
This work is complex and requires solid service integration. However, it is fundamentally engineering an application around an external model, not building a proprietary one.
The Honesty Gap
The original article exposes the immense gap between the marketing narrative and the technical truth.
For the 73% of companies exposed:
- They secure multi-million dollar valuations while their core intellectual property is leased.
- They use vague marketing terms to hide that their technology is dependent on external providers.
The engineering team is doing honest work, but the process is being misrepresented by founders to attract funding. This is a crucial distinction.
The current AI gold rush is driven by hype. This article provides the receipts. For a detailed breakdown of how the deception was exposed, you need to read the original analysis. Go check out the original post.