Environmental changes have always been catalysts for evolutionary shifts. The rise of Large Language Models (LLMs) like ChatGPT has ignited the birth of a new technological ecosystem. But, as with any seismic change, the initial response is often wildly exaggerated, driven by those who don't fully grasp the nuances—a phenomenon we now dub the AI Hype. For those of us who've seen such waves before, it was clear from the start: this was a hype cycle, and like all hype cycles, it had to run its course. Now, the signs are undeniable—the hype is cooling down. But what's next for AI?
No technology, no matter how revolutionary, can thrive without delivering real value. And here's the truth: we haven’t yet cracked the code on generating consistent value for enterprises from AI. The next phase of AI's evolution won't be about shiny new algorithms or eye-catching demos; it will be about the nitty-gritty work of building standards, developing techniques, and creating frameworks that enable AI and ML to integrate into the fabric of business seamlessly and safely. We've begun to see pockets of success and early experiments that hint at the financial benefits AI/ML can offer. But as the hype bubble bursts, there's always a danger that the technology itself will be blamed for the inevitable disappointments—rather than the non-specialists who inflated expectations to begin with.
The reality is clear: AI/ML works. It has proven, valuable applications and should not be discarded just because it was overhyped. Now, more than ever, it’s time to rally behind standards, support open-source initiatives, and back companies that are focused on streamlining the adoption of AI and ML. This is how we accelerate the next phase of AI's evolution, turning what was once hype into sustainable, real-world impact.
Top comments (1)
Agree with the core argument — the next phase being about standards and frameworks rather than demos is the right call, and it's already visible in infrastructure decisions, not just model capability. One place I'd add to the "standards" list: environmental accounting. As AI moves from hype to embedded infrastructure, the actual resource cost per inference — carbon, water, dollars — becomes a real operating input for router and deployment decisions, not just a sustainability footnote. Right now that accounting is inconsistent enough that identical infrastructure can produce wildly different reported numbers depending on methodology, which is the same "non-specialists inflating or distorting the picture" problem you're describing, just showing up in resource claims instead of capability claims. We're working on this at CarbonLayer — happy to compare notes if useful. Good post, appreciate the framing.