Why AI Evals Companies Fell for the PLG Trap: The Inevitable Mistake
I remember sitting in a Sand Hill Road conference room in late 2022, watching a 25-year-old founder pitch his AI evaluation startup. He had the perfect slide deck: hockey-stick growth projections, massive market opportunity, and a product that "democratized AI evaluation for everyone."
Six months later, his company was struggling. Users loved the interface, but the evaluations were worthless. The models that scored 95% in his tool crashed spectacularly in production.
He wasn't stupid. He wasn't malicious. He was just following the playbook that had worked for every hot startup before him.
The PLG trap wasn't a mistake. It was inevitable.
The Seductive Promise of Easy Scaling
Picture this: You're a talented engineer in 2023. You've built something cool—maybe a better way to evaluate AI models. The market is exploding. Investors are throwing money at anything with "AI" in the name.
You look around and see the success stories:
Notion: Launched as a simple note-taking app, grew to 20M users without a sales team. Figma: Browser-based design tool that disrupted Adobe, raised $200M without enterprise sales. Linear: Issue tracking that spread through developer communities like wildfire.
These companies didn't need sales teams. They didn't need enterprise integrations. They built products so good that users converted themselves.
"Why not me?" you think. "If it worked for design tools and project management, why not AI evaluation?"
The logic was impeccable. The execution was catastrophic.
The Technical Naivety That Seemed Smart
I talked to the CTO of one of these AI evals companies last year. He was brilliant—PhD from Stanford, published papers on ML evaluation. But he made a fundamental mistake.
"We thought evaluation was just benchmarking," he told me. "Upload a model, run it against standard datasets, get a score. Simple."
He wasn't wrong about the simplicity. He was wrong about the relevance.
The dirty secret of AI evaluation: What works on a benchmark dataset has almost no correlation with what works in your actual deployment environment.
His tool would give a model 94% accuracy on ImageNet. In production, that same model would misclassify 40% of user-uploaded images because they were taken with phone cameras in real lighting conditions.
The benchmarks were clean, controlled, and irrelevant. The production data was messy, varied, and real.
But by the time they figured this out, they had already raised $15M and built a user base of 50,000 developers who expected the evaluations to be meaningful.
The Investor Pressure That Crushed Nuance
The funding environment in 2023-2024 was unlike anything I'd seen before. Every VC had the same script:
"How many users do you have?" "What's your monthly growth rate?" "When will you hit product-market fit?"
They didn't ask about accuracy. They didn't ask about deployment context. They asked about scale.
One founder told me his investor literally said: "We don't care if your product works perfectly. We care that it scales to millions of users."
The brutal math of startup funding: You need to show 10x growth to get the next round. PLG promised that growth. Enterprise sales didn't.
So companies built PLG products. They focused on user acquisition over evaluation quality. They optimized for viral spread over deployment success.
The result? A generation of AI tools that spread like wildfire but failed spectacularly when actually used.
The Path Dependency That Became a Prison
Once you commit to PLG, changing course becomes nearly impossible.
I watched one company try to pivot. They started with a simple evaluation dashboard. Users loved it—clean interface, fast results, easy to share.
But the evaluations were wrong. Customers complained. Support tickets piled up.
The founders knew they needed to add deployment context. They needed to understand latency requirements, infrastructure constraints, data distributions.
But their users expected self-service. Adding "consulting" felt like admitting failure.
Their investors expected PLG growth. Switching to services would kill the metrics.
Their team was optimized for product development. Adding domain experts would require a complete reorganization.
They were trapped by their own success.
The Market Timing That Was Perfectly Wrong
AI evals companies launched at the worst possible time: too early for enterprise adoption, too late for the hype cycle.
In 2023, most companies weren't deploying AI at scale. They were experimenting. They wanted quick evaluations of toy models, not rigorous assessments of production systems.
PLG was perfect for this environment. Let developers upload their GPT-2 fine-tunes and get instant feedback. Build a community. Create network effects.
But production AI is different. It has real consequences. Wrong evaluations lead to failed deployments, lost revenue, regulatory fines.
By the time enterprises started taking AI seriously in 2024, the PLG companies were locked into their model. They couldn't suddenly become deployment experts.
The Leadership Vacuum That Enabled It
Most AI evals companies were founded by product people, not AI people.
Don't get me wrong—these were talented individuals. They built great user experiences. They understood growth hacking. They knew how to build viral products.
But they didn't understand AI evaluation. They didn't know the difference between benchmark accuracy and production performance.
One founder told me: "I thought evaluation was like code quality tools. Run the linter, get a score. Simple."
It wasn't his fault. The market rewarded product skills over domain expertise. The best AI researchers were at OpenAI or Google. The people who could build products were building startups.
The result: Companies led by people who understood users but not the underlying technology they were evaluating.
The Alternative Reality That Could Have Been
Some companies got it right. They avoided the PLG trap by building differently.
Weights & Biases started as experiment tracking, not evaluation. They built relationships with ML teams first. They understood deployment context before they built evaluation tools.
Arize AI focused on production monitoring, not pre-deployment evaluation. They solved real production problems first, then added evaluation as a natural extension.
Fiddler AI built for model risk management, not developer productivity. They understood that evaluation wasn't about scores—it was about compliance and safety.
These companies didn't follow the PLG playbook. They built consultative relationships first, products second.
They understood that AI evaluation isn't a product. It's a service that requires deep understanding of how AI actually gets deployed.
The Reckoning That's Coming
The PLG illusion is starting to crack. I see it in the support tickets, the churn rates, the failed deployments.
Companies are realizing that the evaluation scores they trusted are meaningless. Models that scored 95% in the dashboard are failing at 60% in production.
The market is correcting. New companies are emerging that understand deployment context. They ask about latency requirements. They understand infrastructure constraints. They know about data drift.
The PLG companies have two choices:
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Double down: Add more features, more benchmarks, more integrations. Hope that quantity compensates for quality.
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Evolve: Admit that PLG was wrong for this market. Rebuild with deployment context at the core.
Most will choose option 1. It's easier. It doesn't require admitting failure. It keeps the growth metrics looking good.
But the reckoning is coming. When enterprises start demanding real evaluation—not pretty dashboards—the PLG companies will be exposed.
The Lesson for the Next Wave
If you're building an AI company today, learn from the evals mistake:
Not every product is PLG-compatible. Some technologies require human expertise, domain knowledge, and consultative relationships.
Market timing matters more than you think. Launching during hype cycles locks you into suboptimal models.
Domain expertise beats product skills. Understanding your users' problems is more important than building great UX.
Path dependency is real. Your first choices constrain your future options irreversibly.
Consulting + product beats pure product. The most successful AI companies offer both self-service and expert help.
The Inevitable Question
Was the PLG trap avoidable? Or was it inevitable given the market conditions, investor pressure, and timing?
I think it was inevitable. The market moved too fast. The incentives were too strong. The examples were too compelling.
But inevitability doesn't excuse failure. The companies that survive will be those that learn from this mistake.
They'll build evaluation systems that understand deployment reality, not just model accuracy.
They'll prioritize quality over quantity.
They'll choose substance over hype.
The PLG trap wasn't evil. It was seductive. And that's what made it so dangerous.
The question isn't whether AI evals companies fell for it. It's whether they'll have the courage to climb out.