The AI Bubble: Is It Bursting or Just Getting Started?
Everyone has a take on whether the AI bubble is bursting. Ours is grounded in dozens of real implementations. Here's what we actually see.
The AI bubble is not bursting. But it is deflating for companies that haven't built anything concrete yet. After dozens of AI projects with companies between 50 and 500 employees, our conclusion is this: the hype is real, the value is real — they just aren't in the same place.
Technology analysts are calling it a 'Trough of Disillusionment.' Investors are pulling back from AI startups without revenue. Journalists are writing the twentieth 'Is AI dead?' piece. Meanwhile, we and our clients keep shipping systems that work.
The dot-com comparison doesn't hold.
The most-cited comparison is to the dot-com crash of 2000. It is tempting but structurally wrong.
In 2000, the majority of internet companies had no revenue model. Pets.com burned investor capital on TV ads while selling dog food below cost. The infrastructure was real — the internet worked — but the business models were fiction.
AI is different. Nvidia reported $130 billion in revenue in 2025, driven by real demand for compute from production AI systems. API usage across Anthropic, OpenAI, and Google Gemini doubles every quarter. These are not promises of future revenue — they are paying customers running AI in live workflows today.
The distinction matters: in 2000, business models ran ahead of infrastructure. Today, the debate about valuations is running behind infrastructure that has been in production for months.
Where the AI bubble hype is genuinely overblown.
That does not mean every corner of the AI market is healthy. Three categories we are skeptical about:
- AI startups with high valuations and no coherent business model beyond 're-selling OpenAI API calls with a 30% margin.' That is a reseller business, not an AI company.
- AGI timelines. Anyone telling you AGI arrives before 2028 does not know. The uncertainty is fundamental, not a matter of degree. Confident predictions about fundamentally uncertain developments are hype, not analysis.
- Productivity promises that do not hold at the organizational level. McKinsey estimates AI can automate 70% of knowledge work tasks. That is accurate at the task level. It says nothing about how quickly organizations actually restructure those tasks. That pace is consistently slower than the headline numbers suggest.
The deeper issue: too many companies are buying AI tools without a concrete problem to solve. Purchasing a license feels like taking action. Building something that works is harder.
What we actually see with real clients.
In practice, we see two types of organizations.
| Approach | Outcome | Adoption after 6 months |
|---|---|---|
| Specific use case: document processing, quote analysis, lead scoring | Measurable ROI, payback period 3-8 months | 70-90% — tool is embedded in daily workflow |
| Broad platform: enterprise AI license without a defined purpose | Unclear — no baseline was set | 5-10% — employees don't know what to use it for |
The difference is not the technology. It is the approach. Companies that get ROI from AI start with one tightly scoped problem, measure the impact, and scale from there. Companies that treat the tool as the solution rather than the means end up paying for something they do not use.
Concrete examples: a construction firm that automated contract review processed the same volume with two people instead of four. A professional services company that used AI to summarize client communications freed up 35 minutes per advisor per day for actual client work. No magic — just a well-defined problem matched to the right tool.
The winning strategy: build, don't speculate.
While the market debates bubble versus no bubble, the organizations that keep building are pulling ahead. Not everything at once. Not the most ambitious AI project on the roadmap. One concrete problem — with a business case you can explain in two slides to your leadership team.
Our consistent approach: define the pain first, then choose the technology — never the other way around. An AI agent for document processing in construction has a different architecture than an AI tool for customer service in the energy sector. That choice comes from the use case, not from hype around a specific model or platform.
“The AI bubble is partly bursting. Valuations of AI startups without business models will correct. Some platforms dominant today will not exist in five years. That is normal market dynamics.”— Productized Team, 2026
But the underlying technology — the models, the infrastructure, the use cases already working in production — is not going anywhere. According to Stanford's AI Index 2025, the cost of AI inference has dropped by a factor of 40 in two years. For companies building now, that is not a threat. It is a structural advantage over competitors waiting for the market to 'stabilize.'
Want to know which AI project makes sense for your organization? Describe the process you want to tackle — we will give you an honest assessment, not a sales pitch.