{"id":9240,"date":"2026-01-06T18:47:01","date_gmt":"2026-01-06T18:47:01","guid":{"rendered":"https:\/\/www.markwilson.co.uk\/blog\/?p=9240"},"modified":"2026-01-13T22:40:51","modified_gmt":"2026-01-13T22:40:51","slug":"2026-anti-prediction-generative-ai","status":"publish","type":"post","link":"https:\/\/www.markwilson.co.uk\/blog\/2026\/01\/2026-anti-prediction-generative-ai.htm","title":{"rendered":"My 2026 anti-prediction: we won’t see an endless rise in generative AI"},"content":{"rendered":"\n

It\u2019s the start of the year and everyone is writing their predictions. I\u2019ve written a few for Node4<\/a> that will make their way onto the company socials \u2014 and in industry press, no doubt \u2014 but here\u2019s one I\u2019m publishing for myself:<\/p>\n\n\n\n

I think 2026 will be the year when tech companies quietly start to scale back on generative AI<\/strong>.<\/p>Mark Wilson, 6 January 2026<\/cite><\/blockquote><\/figure>\n\n\n\n

Over Christmas I was talking to a family member who, like many people, is convinced that AI \u2014 by which they really mean chatbots, copilots and agents \u2014 will just keep becoming more dominant.<\/p>\n\n\n\n

I\u2019m not so sure. And I\u2019m comfortable putting that on record now. But I don’t mean it’s all going away. Let me explain…<\/p>\n\n\n\n

Where the money comes from<\/strong><\/h2>\n\n\n\n

The biggest tech firms in the world are still pouring tens of billions into AI infrastructure. GPUs, custom silicon, data centres, power contracts, talent. That money has to come from somewhere. The uncomfortable truth is that many of the high-profile layoffs we\u2019ve seen over the last two years aren\u2019t about \u201cAI replacing people\u201d. They\u2019re about reducing operating costs to fund AI investment. Humans out. CapEx in.<\/p>\n\n\n\n

That works for a while. But shareholders don\u2019t accept \u201ctrust us, it\u2019ll pay off eventually\u201d indefinitely. At some point, the question becomes very simple: where is the sustainable revenue that justifies this level of spend?<\/strong><\/p>\n\n\n\n

A land-grab without a business model<\/strong><\/h2>\n\n\n\n

Every hyperscaler and major platform vendor has invested as if generative AI is a winner-takes-most market. Own the models. Own the data. Own the developer ecosystem. Own the distribution. The logic is clear: if a viable business model emerges, they want the biggest possible slice of the pie.<\/p>\n\n\n\n

The problem is that the pie still hasn\u2019t really materialised. We have impressive demos, widespread experimentation, and plenty of productivity anecdotes \u2014 but not many clear, repeatable use cases that consistently deliver real returns. Right now, it feels less like a gold rush and more like a game of chicken. Everyone keeps spending because they\u2019re terrified of being the first to blink.<\/p>\n\n\n\n

Eventually, someone will.<\/p>\n\n\n\n

Slowing progress in the models themselves<\/strong><\/h2>\n\n\n\n

Another reason I\u2019m sceptical is the pace of improvement itself. A lot of early excitement was based on the idea that bigger models would always mean better models. But that assumption is starting to wobble. Increasing amounts of AI-generated content are now fed back into new training datasets. Models learning from the outputs of other models.<\/p>\n\n\n\n

There is growing evidence that this can actually make them worse over time \u2014 less diverse, less accurate, more prone to error. Researchers call this model collapse<\/em>. Whatever the name, it\u2019s a reminder that data quality is finite<\/strong>, and simply scaling doesn\u2019t guarantee progress.<\/p>\n\n\n\n

A noticeable shift in tone<\/strong><\/h2>\n\n\n\n

I also find it interesting how the tone has shifted. Not just from AI replacing humans to AI augmenting humans, but something broader. And I don’t mean the AI evangelists vs. the AI doomsayers back-and-forth that I see on LinkedIn every day either…<\/p>\n\n\n\n

A year or two ago, large language models were positioned as the future of AI. The centre of gravity. The thing everything else would orbit around. Listen carefully now, and the message from tech leaders is more cautious. LLMs are still important, but they\u2019re increasingly framed as one tool among many.<\/p>\n\n\n\n

There\u2019s more talk about smaller, domain-specific models. About optimisation rather than scale. About decision intelligence, automation, computer vision, edge AI, and good old-fashioned applied machine learning. In other words: AI that quietly does a job well, rather than AI that chats convincingly about it.<\/p>\n\n\n\n

That feels less like hype, and more like a course correction.<\/p>\n\n\n\n

A gradual change in direction<\/strong><\/h2>\n\n\n\n

I don\u2019t know whether this ends in a classic \u201cbubble burst\u201d. I’m a technologist not an economist. What feels likely to me is a gradual change in direction. Investment doesn\u2019t stop, but it becomes harder to justify. Projects get cut. Timelines stretch. Expectations reset. Some bets quietly fail.<\/p>\n\n\n\n

But there will be consequences. You can\u2019t pour this much capital into something with limited realised outcomes and expect it to disappear without a trace.<\/p>\n\n\n\n

The natural resource crunch<\/strong><\/h2>\n\n\n\n

Then there\u2019s the crucial element that should be worrying everyone: natural resources.<\/p>\n\n\n\n

Generative AI isn\u2019t just expensive in financial terms. It\u2019s expensive in physical ones. Energy, cooling, water, land, grid capacity. Even today\u2019s hyperscale cloud providers are struggling in some regions. Power connections delayed. Capacity constrained. Grids already full.<\/p>\n\n\n\n

Water often gets mentioned \u2014 sometimes unfairly, because many data centres operate in closed-loop systems rather than constantly consuming new supply \u2014 but it still forms part of a broader environmental footprint that can\u2019t be ignored.<\/p>\n\n\n\n

You can\u2019t scale AI workloads indefinitely if the electricity and supporting infrastructure simply aren\u2019t there. And while long-term solutions exist \u2014 nuclear, renewables, grid modernisation \u2014 they don\u2019t move at the speed of venture capital or quarterly earnings calls.<\/p>\n\n\n\n

The problems AI can\u2019t solve<\/strong><\/h2>\n\n\n\n

There are other headwinds too.<\/p>\n\n\n\n

Regulation is tightening, not loosening. Data quality remains a mess. Hallucinations are still a thing, however politely we rename them. The cost of inference hasn\u2019t fallen as fast as many hoped. And for most organisations, the hardest problems are still boring ones: messy processes, poor data, unclear ownership, and a lack of change management.<\/p>\n\n\n\n

AI doesn\u2019t fix those. It amplifies them.<\/p>\n\n\n\n

A more realistic 2026<\/strong><\/h2>\n\n\n\n

So no, I don\u2019t think (generative) AI is \u201cgoing away\u201d. That would be daft. But I do think 2026 might be the year when generative AI stops being treated as the inevitable centrepiece of every tech strategy<\/strong>.<\/p>\n\n\n\n

Less breathless hype. Fewer moonshots. More realism.<\/p>\n\n\n\n

And perhaps, finally, a shift from how impressive is this model?<\/em> to what problem does this actually solve, at a cost we can justify, in a world with finite resources?<\/em><\/p>\n\n\n\n

I\u2019m happy to be wrong. But if I am, someone needs to explain where the money, power, and patience are all coming from \u2014 and for how long.<\/p>\n\n\n\n

Featured image: created by ChatGPT<\/em><\/em>.<\/p>\n","protected":false},"excerpt":{"rendered":"

It\u2019s the start of the year and everyone is writing their predictions. I\u2019ve written a few for Node4 that will make their way onto the company socials \u2014 and in industry press, no doubt \u2014 but here\u2019s one I\u2019m publishing for myself: I think 2026 will be the year when tech companies quietly start to … Continue reading My 2026 anti-prediction: we won’t see an endless rise in generative AI<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":9242,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[218],"tags":[646,238,639,841,883,882],"class_list":["post-9240","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology","tag-artificial-intelligence","tag-datacentre-design","tag-digital-transformation","tag-generative-ai","tag-natural-resources","tag-predictions"],"yoast_head":"\nMy 2026 anti-prediction: we won't see an endless rise in generative AI - markwilson.it<\/title>\n<meta name=\"description\" content=\"Why 2026 won\u2019t bring an endless rise in generative AI. 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