Is the AI agent bubble real?

In detail

The demo-to-production gap

The tell of a bubble is the distance between what’s demonstrated and what’s deployed. Agent demos are spectacular and cheap to produce. An agent that runs unattended, handles edge cases, and doesn’t fail silently is a different and much harder thing. Most of the noise is on the demo side of that gap.

Guests and analysts put rough numbers on it. Lyzr AI CEO Siva Surendira said in episode 21 at 9:18 that “95% of all AI work today done by enterprises remain as proof of concepts”; he does not cite a source for the figure in that passage. In June 2025, Gartner predicted that over 40% of agentic AI projects will be canceled by the end of 2027, described most current agentic projects as early-stage experiments or proofs of concept driven by hype, and estimated that only about 130 of the thousands of vendors marketing agentic AI offer real agentic capabilities.

Why agent projects stall

A common pattern: the prototype works, then the team hits the unglamorous parts: evaluation, error handling, the cost of every model call, debugging a non-deterministic system, and data that isn’t as clean as the demo’s. None of that shows up in a launch video, and it’s where much of the budget and the timeline go. A large fraction never make it past that wall.

Spot AI’s Kelly Vaughn drew the line in episode 15 at 23:18: there is “a big difference between building a prototype, a minimum viable product and a minimum shippable product,” and code past the prototype has to meet production standards for security and scalability. Liquid AI’s Maxime Labonne said in episode 43 at 16:12 that he is “a bit skeptical about the success of agents so far,” because agents are “a engineering problem as much as a machine learning problem,” and the field has mostly worked on the machine learning half.

So is it a bubble?

In the sense that expectations ran ahead of reliability, yes. The capability is real and improving, but the hype priced agents as if the hard engineering were already solved. The teams that come out ahead pick a narrow, valuable problem instead of chasing the most autonomous demo, and do the boring reliability work to make one agent dependable.

Yujian Tang, who started the r/AI_Agents subreddit, put the money side in episode 51 at 8:59, published in March 2026. He traces investment from MLOps to LLM ops and then to tools that make building AI easier, cites a LinkedIn post of his on AI spending being up about 44% from a year earlier, and says “it does feel like a bubble.” He compares it to the dot-com crash, after which the internet kept growing. He won’t predict a similar crash, but says he “would not be surprised” by one. Episode 51 is sponsored by Galileo, the Season 3 presenting sponsor; Conor thanks Galileo at 0:21.

Disclosure: Conor worked at Galileo when episode 21 was recorded: he mentions “something we just released with Galileo” at 32:09 and plugs galileo.ai at 36:57.