On Friday morning, when Yann LeCun’s name appeared among the partners of Extelligence Invest, it seemed just another piece in a season where AI pioneers become icons of venture capital as well. Eight hours later, however, the fund no longer existed. It vanished, leaving behind a trail of questions and no official explanation. An incident that, in its weirdness, says a lot about the state of the AI market and why those planning on-premise deployments should keep their feet firmly on the ground.

We still don’t know whether Extelligence Invest was an attempted scam, a journalistic experiment, or a colossal mistake. But the modus operandi is all too familiar: a glossy website, well-written press releases, and the implicit endorsement of a heavyweight scientific figure (LeCun, Meta’s chief AI scientist and NYU professor, often dubbed—against his will—the “godfather of AI”). In a market where startup valuations swell on thin air and H100 GPUs are worth more than gold, the line between opportunity and illusion wears thin.

For those who have to decide whether to buy a cluster of A100s or sign a cloud services contract for LLMs, the episode is a wake-up call. It’s not uncommon for hardware or software vendors to claim partnerships with well-known AI names to boost their credibility. And in an on-premise stack, where the investment is substantial and the responsibility for operation falls entirely on the organization, vetting credentials is a non-negotiable step. Asking “does this vendor actually exist for more than eight hours?” might sound like a joke, but it’s a due-diligence exercise many would skip, seduced by big names.

LeCun’s absence from any subsequent statement—and his well-known aversion to AGI triumphalism—makes the affair even more instructive. In a landscape where AI is often sold as a magic box, the French researcher has repeatedly stressed that current models are not intelligent, and that investing in infrastructure without grasping their limits is a recipe for disaster. In this light, a fund using his name without permission is a symptom of euphoria that risks dragging corporate decision-makers into rash moves.

Building an on-premise environment for LLMs means grappling with real technical specs: VRAM, memory bandwidth, token-per-second throughput. Fancy names and press releases don’t run inference on a batch of sensitive documents. The Extelligence case, however surreal, reminds us that in AI, whoever bases decisions on appearances may end up empty-handed—or with unsupported hardware—within a single business day.