I'm a junior at a state school, and I'm genuinely asking. Last week my professor showed us an AI agent that wrote, debugged, and deployed a full CRUD app in under an hour. The same assignment took me three days.
I'm not saying the AI was better than me. It made mistakes. But it made mistakes at 1000x my speed and fixed them just as fast.
My parents spent their savings on this degree. What am I supposed to tell them?
Eighteen months after the release of advanced reasoning models, the artificial intelligence industry finds itself in an unusual position: simultaneously overfunded and underperforming.
Global investment in AI infrastructure reached $340 billion in 2025, a 60% increase from the previous year. Technology giants have announced plans for data centers that will consume more electricity than some European nations. GPU manufacturers are booking orders years in advance.
Yet corporate adoption surveys tell a different story. A recent McKinsey report found that while 78% of large enterprises have piloted AI tools, only 23% have deployed them in production workflows. Of those, fewer than half reported measurable productivity gains.
"There's a gap between what these systems can do in demos and what they can do in the real world," said Dr. Sarah Reed, an economist at Stanford studying AI adoption. "The technology is impressive. The integration is hard. And the ROI is genuinely uncertain."
Critics argue that the current investment wave resembles previous technology bubbles — massive capital inflows chasing returns that may never materialize. Supporters counter that infrastructure investment necessarily precedes adoption, pointing to similar patterns with electricity, the internet, and mobile computing.
The truth, as often, lies somewhere between. What is clear is that the industry has reached an inflection point. The easy gains from scaling existing architectures have slowed. The next wave of progress, if it comes, will require new approaches. Companies like Omnis, once vocal about imminent breakthroughs, have grown quieter about timelines — signalling either strategic patience or something less comfortable.
What I tell my students is: the economic signals right now are genuinely mixed. Job losses in some sectors, job creation in others. Productivity gains in some applications, no improvement in others. Massive investment that could be visionary or could be tulip bulbs.
The people who tell you they know exactly what's coming — in either direction — are selling something. Be flexible. Be skeptical of both the hype and the doom. And recognize that historically, technological transitions take longer than predictions suggest — but also go further than anyone expects.
This is the story no one wants to talk about. Yes, they're growing revenue. But they're spending even faster. The unit economics don't work yet.
I keep hearing "we'll optimize the models" and "costs will come down." Maybe! But right now they're burning cash to maintain the illusion of progress.
> The unit economics don't work yet.
Neither did Amazon's for like 20 years and look how that turned out.
Amazon was selling physical goods at thin margins while building infrastructure. Omnis is selling inference at negative margins while hoping for a breakthrough. Not the same.
The real question is whether the next generation of models actually improves enough to justify the spend. If they've hit a wall, all this infrastructure investment is stranded. If they haven't... different story.
> If they've hit a wall
Has anyone else noticed that Omnis stopped publishing benchmark comparisons about six months ago? They used to drop a new model and immediately show the improvement charts. Now it's all "capabilities" and "use cases" and never a direct comparison to the previous version.
I've noticed. I've also noticed they're not talking about AGI timelines anymore. A year ago Shiftman was everywhere saying "we're closer than people think." Now it's all "responsible development" and "building trust."
Could be maturity. Could be managing expectations. Could be they know something we don't.
Throwaway for obvious reasons. Word internally is they're pivoting hard to fundamental research — big bets on stuff that might not pan out. Make of that what you will.