Has AI innovation hit a wall?
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It feels like artificial intelligence (AI) has hit a plateau. The creators of AI models don't seem to be making progress as quickly as before. Many of the products they promised were overhyped and underdelivered, and consumers aren't quite sure what to do with generative AI beyond using it as a replacement for traditional search engines.
If it hasn't already, AI looks like it's beginning to exit its early-stage growth phase and enter a period of stagnation.
AI's explosive growth from 2022 to 2024
From November 2022 to the end of 2024, new developments in artificial intelligence occurred rapidly. ChatGPT launched in November 2022. Four months later, we got GPT-4. Two months after that, OpenAI added Code Interpreter and Advanced Data Analysis. At the same time, significant advancements took place in text-to-image and text-to-video generation. Advancements seemed to drop every 30 to 120 days at OpenAI, and their competitors seemed to be moving in lockstep, probably out of fear of falling behind if they did not keep pace.
With all of that wind in their sails, companies began making big promises: autonomous AI agents that could plan, reason, and complete complex tasks from end to end without a human in the loop. Creative AI that would replace marketers, designers, filmmakers, songwriters, and AI that would replace entire white-collar job categories. However, most of those promises still haven't materialized; if they have, they have been lackluster.
Why AI innovation is slowing down
The problem isn't just that AI agents or automated workforces were underdelivered; it's that these unimpressive products are the result of a much bigger problem. Innovation in the AI industry is slowing down, and the leading companies building these tools seem lost.
Not every product released between 2022 and 2024 was revolutionary. Many of the updates during this period probably went unused by everyday consumers. This is because most people still only use AI as an alternative for a search engine, or, as some people are beginning to call it, they are using AI as an answer engine, the next iteration of the search engine.
Although that is a valid use case, it's safe to say that tech giants have a much grander vision for AI. However, one thing that may be holding them back, and one reason that the more hyped-up products have struggled in the market, is due to a classic issue in highly technical industries: brilliant engineers sometimes end up building tools and products that only other brilliant engineers know how to leverage, but they forget to make the tools and products usable for the much larger population of their users that aren't brilliant engineers. In this case, that means general users, the audience that arguably made AI mainstream back in 2022.
However, even the stagnation in AI products is a trickle-down effect from an even bigger problem relating to how AI models are trained. The biggest AI labs have been obsessively improving their underlying models. At first, those improvements in their AI models made a big, noticeable difference from version to version. But now, we've reached the point of diminishing returns in model optimization. These days, each upgrade to an AI model seems less noticeable than the last. One of the leading theories behind this is that the AI labs are running out of high-quality, unique data on which to train their models. They have already scraped what we can assume to be the entire internet, so where will they go next for data, and how will the data they obtain differ from the data their competitors are trying to get their hands on?
Before hitting this wall, the formula for success in AI models was simple: feed large language models more internet data, and they get better. However, the internet is a finite resource, and many AI giants have exhausted it. On top of that, when everyone trains on the same data, no one can pull ahead. And if you can't get new, unique data, you can't keep making models significantly better by training data. That's the wall a lot of these companies have run into.
It's important to note, the incremental improvements being made to these models is still very important even though their returns are diminishing. Although these improvements are not as impactful as the improvements of the past, they still need to take place for the AI products of the future that we have been promised to deliver.
Where AI goes from here
So, how do we fix this problem? What's missing is attention to consumer demand at the product level. Consumers want AI products and tools that solve real problems in their lives, are intuitive, and can be used without having a STEM degree. Instead, they've received products that don't seem production-ready, like agents, with vague use cases and feel more like experiments than products. Products like this are clearly not built for anyone in particular; they're hard to use, and it might be because they've struggled to pick up adoption.
Until something changes, AI will likely get stuck in a holding pattern. Whether that breakthrough comes from better training data, new ways of interpreting existing data, or a standout consumer product that finally catches on, something will have to change.
From 2022 to 2024, AI seemed to leap ten steps forward every four months. But in 2025, it's only inching forward one small step at a time and much more infrequently.
Unfortunately, there's no quick fix here. However, focusing on a solid consumer-facing product could be low-hanging fruit. If tech giants spent less time chasing futuristic-sounding yet general-purpose AI products and more time delivering a narrow use-case, high-impact tool that people can use right out of the box, then they would see more success.
But in the long run, there will need to be some sort of major advancement that solves the data drought we are currently in, whether that be companies finding new, exclusive sources of training data or finding ways for models to make more of the data they already have.
In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek's coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI .
Watch: Artificial intelligence needs blockchain
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