Have We Already Reached Peak AI?
Source: Alexandra Koch on Pixabay
When it comes to the topic of artificial intelligence, I don’t have any special insight. I’m nowhere near the cutting edge of the science and business involved in it. In fact, there are any number of people more qualified to write this article than I am. I’m merely a writer and content strategist who has been writing about AI in one capacity or another for just over three years, and in a focused way for only about a year and a half.
Moreover, what I’m about to say here could easily be proven dead wrong. Not even the people closest to the technology know what the future holds and it could only be a matter of months or even weeks before the next breathtaking breakthrough in AI occurs. And if that proves to be the case, I’ll be the first to admit I got it wrong—and you’ll read it right here on this blog.
But from where I’m sitting right now in mid 2026, it feels as though the AI tools that were supposed to transform everything and improve at an exponential rate have really failed to do so.
I first encountered ChatGPT in early 2023, when I co-wrote an article on the platform for a web development company I was doing work for at the time. At the time, I’d never seen anything like it before—nobody had. At this point, AI played a minimal part in my creative process. It wasn’t until over a year later, when I joined an agency as digital PR and content manager, that I began using ChatGPT and other tools on a regular basis and became a true believer in an AI-enhanced future.
That job unfortunately didn’t last very long and I quickly found myself looking for new work. I found AI to be a mixed bag in my job hunt; it was a good tool for fine-tuning resumes and cover letters but completely useless for finding jobs to apply for—virtually everything it pulled was either out of date or wholly inappropriate. In my current full-time job, I use AI to a limited degree. As a writer, my pride gets in the way of my using it for first drafts, but I find it very useful for adjusting writing for tone and style.
In other words, I’ve come full circle with AI: from initially skeptical that it would do much to an AI fanboy and now back to being somewhat AI averse. And when I talk to my colleagues about it, everyone seems to feel the same way about it, that it’s a useful tool but still a sloppy substitute for human-generated writing. Invariably there’s a mix of pride and anxiety over future job security in the mix, but for the time being at least, AI remains one of many tools my colleagues and I have access to, much like social media platforms, spell checkers, or even computers themselves.
Where’s The Exponential Improvement?
Looking back at my first ever writings on AI, what strikes me the most is how little the tools have changed over the past three years. Yes, they’re somewhat better, but from a practical standpoint they’re still blunt instruments.
AI still consistently makes stuff up. It still pulls sources that are out of date or not quite appropriate. ChatGPT, Claude, Copilot, and the rest are still basically glorified search engines that give you the answers you’re looking for somewhat faster than Google, albeit with greater latitude for mistakes. The writing they produce still feels soulless and purely functional and it still takes gargantuan effort from the prompter to get them to generate images that don’t have spelling mistakes and other goofs in them.
From the beginning, we were promised “exponential improvement” when it came to AI—OpenAI CEO Sam Altman famously promised this back in 2021. This, we were told, would not be like innovations of the past in that we would see improvements over the course of months rather than years and decades. Yet three years on, OpenAI still can’t get ChatGPT to not hallucinate. I have no knowledge of the science behind this work, but this alone strikes me as evidence that making progress in this field is really hard for the people involved, and that there are no obvious answers.
I’m hardly the only person to notice that improvements in AI seem to have slowed to a crawl. In an article in The New Yorker last August, renowned computer scientist and author Cal Newport notes that progress in large language models appears to have stalled, as exemplified by GPT-5, despite the staggering sums of money being plowed into AI research. Both GPT-5 and Anthropic’s Opus 4.1 perform slightly better on industry benchmarks than their predecessors (benchmarks that have been criticized by many experts), current models appear to have reached a ceiling of roughly 75% accuracy with current methods of making progress.
So why are we no longer making progress? Three factors may be contributing to a potential plateau:
Data Limitations: It could be as simple as AI running out of data to scrape from the web, academic sources, and social media. Experts have noted that the supply of high-quality, diverse data is finite, and training on AI-generated or recycled data could lead to “model collapse,” where performance stagnates or declines.
Compute and Energy Constraints: Scaling AI models requires enormous computational resources, GPUs, and energy, and some critics are arguing that these physical and economic limits may be slowing the pace of improvement.
Benchmark Saturation: While newer models perform slightly better on industry benchmarks, the practical utility for businesses and real-world applications is not dramatically increasing, suggesting diminishing returns from current approaches.
When it comes to AI, the only thing that seems to be unlimited is the hype coming from the billionaires at the helm of the industry. Altman continues to insist that artificial general intelligence (AGI) is just around the corner, putting him at odds with a majority of researchers in the field, who argue that it is far from imminent. Nor does there seem to be any end to the funds being channelled into OpenAI and other companies despite the numerous Cassandras warning of an AI bubble that some say will dwarf the dotcom bubble of the late 1990s.
Of course, I could be completely wrong and it’s only a matter of months before the next quantum leap in AI technology occurs. But given the apparent chasm between the words of the AI evangelists and the reality on the ground, I have my doubts.
Limitations—Physical, Economic, Environmental, and Political
To say AI is taxing on the planet is to understate the situation considerably. Studies show that in 2025, AI’s carbon emissions were equivalent to the entire city of New York, while its consumption of freshwater resources exceeded global consumption of bottled water. This impact is only going to increase; it’s estimated that by 2034, the data centers that power AI are expected to consume as much energy as the entire country of India, currently home to a population of 1.48 billion or 17.8% of humanity.
On the flip side of this coin, you have pie-in-the-sky promises from the likes of Sam Altman that AI will solve all the world’s environmental problems.
At the current rate of scaling, it’s estimated that global data centre capacity will need to triple by 2030, an investment expected to cost nearly $7 trillion. In real terms, this translates to roughly 3,000 new data centres worldwide, placing enormous strain on electricity and water supplies around the globe. These data centres are immensely unpopular, as they not only drain regions of resources but also generate very little local employment relative to the tax subsidies they typically receive, with data centres typically requiring no more than 20 to 50 on-site staff members.
The upshot of all this is that communities are increasingly galvanizing against proposed data centre builds. As of late 2025, at least 16 data centre projects in the US, worth a combined $64 billion, have been blocked or delayed by communities that would be impacted by them. Here in Alberta, a proposal to build the country’s largest data centre, which would consume as much electricity as the entire city of Edmonton, in the town of Olds is facing fierce local opposition, while the Kevin O’Leary-backed Wonder Valley data centre project proposed for the Grande Prairie region is also facing considerable backlash.
In an era of deep-seated political polarization, the data centre issue has been one of the few issues that spans the political divide; in the US, it’s an issue that residents of red and blue states seem to agree on—despite the Trump administration’s fixation of securing American preeminence in AI. This issue also reflects broad concern over AI generally. According to a recent Gallup poll, six in ten Americans distrust AI and only nine percent of respondents believe the government should prioritize developing AI capabilities as quickly as possible, even if it means reducing rules for AI safety and data security.
Assuming the fever dreams of Sam Altman and his peers are within the realm of possibility, they’re going to have to overcome increasingly bitter opposition to building the sort of infrastructure it’s going to take to realize their visions.
What About China?
When they fail to sell the public on the potential benefits of unleashed AI, evangelists of the technology generally turn to the same fearful trope: if we in the west don’t achieve AI supremacy, China will, and then all is lost.
This fear is far from groundless. As early as 2017, Beijing launched a national strategy with the stated goal of making China the world leader in AI by 2030, an ambition that has been backed by enormous state-guided investment. While the vast majority of experts agree that China still lags behind the United States when it comes to AI, many argue that it is closing the gap and some experts do take the notion of Chinese supremacy in AI seriously. What that would mean for western democracies is unclear, although the prospect of artificial general intelligence wielded by the Chinese Communist Party is scary for all sorts of reasons.
Moreover, the Chinese public appear to be far more bullish on AI than their counterparts in the west, with 95% of people surveyed feeling “optimistic about AI development” and 91% feeling "relaxed" about the technology.
Here’s the thing, though. While Chinese government and industry leaders don’t have to deal with direct popular backlash like in the west under the CCP’s authoritarian system—if indeed such sentiment ends up brewing there as well, it’s clear that Chinese officials share with their western counterparts many of the same worries about AI. The Chinese government is clearly concerned about the corrosive impact of AI on society, including the problem of AI addiction, and has already put in place limitations on public AI use, including shutting down chatbots during national exam periods.
China has also sought to protect workers from AI encroachment in a way that no western country has thus far attempted by outright banning companies from laying off workers and replacing them with AI. It’s difficult to imagine the US government taking such action, popular though it would probably be with the voting public.
China’s ruling communists have long struck a deal with the country’s people: you keep your nose out of politics and we’ll continue to deliver a rising quality of life. In this, the CCP has been remarkably successful, and their policies toward AI very much reflect this. Above everything else, China’s rulers fear public unrest of the sort that has toppled imperial and republican Chinese regimes in the past and threatened to do once again in the late eighties. As such, it appears that the CCP is very cognizant of AI’s potential to engender social discord and is taking concrete steps to prevent this. Unlike the US, China’s approach to AI seems to involve a lot of precautionary guardrails—guardrails that could ultimately hinder its goal of AI supremacy.
Despite the fear many industry experts have voiced about China overtaking the west in AI, there appears to be no reason to think that Chinese AI experts are any closer to achieving AGI than their western counterparts. China faces the same limitations as western countries when it comes to computing and energy resources, while its inventors struggle with the same problems as the west’s AI innovators—China’s signature LLM DeepSeek is even worse than ChatGPT when it comes to hallucinations. That and many observers have remarked that China lacks the sort of entrepreneurial risk taking that has driven US AI development.
According to Justin Lin, technical lead for Chinese tech giant Alibaba’s Qwen AI models, the chances of China overtaking the US in AI within the next three to five years is “less than 20 percent,” adding that he thinks 20 percent is “very optimistic.”
Would Peak AI Be Such a Bad Thing?
The United States is facing midterm elections this year, and all polls suggest the results will be disastrous for Trump’s Republicans—the people who have loudly advocated for advancing AI at all costs. It appears clear as well that AI will play a big role in the election and that candidates standing for protection of workers and against unrestrained data centre development will do well. Meanwhile, even with the vast sums of money being funneled into the industry, progress appears to be stalling.
Again, I could easily be dead wrong, but I have a feeling that we could be fast reaching peak AI.
Would that be such a bad thing? I don’t think so. The AI revolution has already given us powerful tools for solving the world’s problems. Narrow AI (as opposed to AGI) is already solving problems in fields ranging from mathematics to medicine, while at the same time making creatives like me a whole lot more efficient and productive. Do we really need an artificial general intelligence—of the sort that could well kill us all, according to a growing voice of experts—to solve humanity’s problems? It seems like an enormous gamble to take with potentially disastrous consequences.
The 1818 novel Frankenstein by Mary Shelley warned humanity about the dangers of playing in God’s domain. The tech billionaires at the helm of AI development in the west appear to be throwing this warning to the wind in pursuit of godlike status as creators of super-beings. I personally don’t think this is a future we should be eager to embrace, if it’s even possible given scientific, economic, and environmental constraints. I myself don’t see this future coming anytime soon—if for no other reason because I don’t think the public will support it and I don’t think our authoritarian counterparts in China will make the tradeoffs necessary to realize it.
In the meantime, I’ll continue to be polite to my chatbots. In the case of a robot apocalypse, I want to be known to be one of the good ones.
