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ML/AI company building predictive models for enterprise customer behavior. $8M ARR, profitable, steady growth. Had 3-year track record of 92% prediction accuracy with traditional ML methods.
My Role: Board Member & CTO
In mid-2023, team became obsessed with implementing large language models and transformers. Everyone was talking about ChatGPT, so we decided our next generation product should use LLMs. We hired deep learning researcher from top AI lab, allocated $3M budget, committed half of engineering to the project. Business argument: "customers will want AI," "LLMs can do more with less training data," "transformers are the future." No customers asked for it. We didn't conduct market research. The lead researcher assured us LLM approach would be 10x better than traditional ML. Convinced board (including me) that this was existential—stay with old ML and we'd be obsolete in 2 years.
Built LLM-based system took 18 months. Results: 67% accuracy on same test cases. Worse than our old model. We debugged for 3 months—model was hallucinating, producing confident wrong answers. Switched to hybrid approach (LLM with traditional ML guardrails): 89% accuracy. Still worse than original 92%. Cost the company $4M in engineering spend with zero revenue benefit. Meanwhile, our core business slowed—best engineers were on AI project. Customer churn accelerated because product updates stopped. Lost 2 major customers who needed reliability over "AI-ness." Revenue dropped 30%. Stock value tanked. Investors demanded explanation. We eventually killed the LLM project, returned to original ML approach, but 8 months of customer momentum was lost. Total impact: $14M in lost revenue + opportunity cost.
Team proposes LLM pivot
Board approves LLM project, $3M allocated
Deep learning researcher hired, team starts building
First LLM prototype shows promise in early tests
Full LLM system ready for evaluation
Accuracy tests show 67% - worse than baseline
Hybrid approach achieves 89% - still short
First major customer churns due to lack of updates
LLM project killed, return to core ML
"Chasing technology hype kills companies that have product-market fit. Ground truth (customer data) beats researcher theory."
$4M direct spend, $10M in lost revenue and contract value.
Felt stupid. Was convinced by hype and researcher credentials instead of data.
CTO lost credibility with engineering team. Investors questioned board judgment. Some customers switched to competitors.