AI Hype Meets Reality - Sun, Nov 30, 2025

Automation Technology

 

The AI hype media frenzy derives from monetary incentives. That’s normal- markets place big bets with capital inflows on advancing technologies poised to lower labour costs, decrease time-to-value and spin up entire new economies at scale. The long-term gains outweigh the outsized capital investments for corporate conglomerates and venture capitalists. But if you’re an old-school “money-in, money-out” type, this may prompt scepticism and several questions. Those of us who are building and developing with this technology are also a bit more grounded. I’ll speak for myself here - there are uncertainties related to generative AI performance and profitability. I like certainty, as do markets. I also like markets- it was my training ground. It’s understood that uncertainty is reasonable because, in reality, we’re just getting started. 

Chip technology faces headwinds- herein enters the uncertainty conversation. Next-gen AI chips will face a thermal management challenge. By 2030, increased temperatures will impact transistors, risking a system shutdown to prevent permanent damage. However, several solutions are underway: liquid cooling, lasers, oil, and diamonds (unlike those you’re likely accustomed to), which are proven to be expensive. 

With the aforementioned, data centre depreciation math enters the room. Simple competition from other ambitious players in the space can be expected as problem solving becomes perpetual. An expensive data centre can become outdated once a new chip cycle arrives. 

AI models also offer a practical example of Moore’s Law not being lawful- Large Language Models are outpacing hardware. Although the hardware has improved over time, LLMs are increasing in size, requiring longer training and more GPU clusters. The hardware is evidently not keeping up. 

Moreover, AI models are leaking into the internet experience. The human-centric web experience is morphing into the agentic web. Note the difference between AI agents and agentic AI here. AI search summaries are slowly replacing traditional links, and human content is becoming indistinguishable from “slop”.

We’re now preparing for a web experience where AI agents with agency have access to tools for browsing web pages and taking action on behalf of human users. Search engine optimisation or SEO is becoming AEO or GEO Generative Engine Optimisation- this will require new standards where human-readable keywords are swapped out for machine-readable vector databases on the back-end. Advertising for agents is next - Perplexity already has the ball in play. 

Simon Willison posted this on his website today- 

“I am increasingly worried about AI in the video game space in general. […]
I’m not sure that the CEOs and the people making the decisions at these sorts of companies
understand the difference between actual content and slop. […]

It’s exactly the same cryolab, it’s exactly the same robot factory place on all of these different planets. It’s like there’s so much to explore and nothing to find. […]

And what was in this contraband chest was a bunch of harvested organs. And I’m like, oh, wow. If this was an actual game that people cared about the making of, this would be something interesting - an interesting bit of environmental storytelling. […] But it’s not, because it’s just a cold, heartless, procedurally generated slop. […]

Like, the point of having a giant open world to explore isn’t the size of the world or the amount of stuff in it. It’s that all of that stuff, however much there is, was made by someone for a reason.”
— Felix Nolan, TikTok about AI and procedural generation in video games

Today marks ChatGPT’s 3rd anniversary, but generative AI is just getting started.
Since 2022, real-life use cases reveal a foundational trend: Productivity gains and cost savings. That isn’t to discount important developments such as weather forecasting, robotics in agriculture, combating fraud, and medical diagnosis. The former is simply a leading indicator for why those big bets were placed. Saving time and costs for important endeavours to solve life’s biggest challenges may just be the underlying why. 

AI is transitioning from hype to tangible business impact, augmenting human tasks, eliminating some, and requiring several upgrades in the AI supply chain. 

Meanwhile, obstacles in data privacy, security, ethical deployment and the need for human oversight remain viable to overcome.

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