It’s no secret that 2025 hasn’t started as Silicon Valley would have hoped. First, we had DeepSeek’s unexpected launch causing investors to re-evaluate America’s grip on the AI market. Now, we have tech giant leaders like Elon Musk and Sam Altman opening up about artificial intelligence’s new biggest challenge — ‘Peak AI’.
In simple terms, ‘Peak AI’ means that the world’s biggest AI brands are losing momentum (and investment) because they are running out of new data.
As Musk put it last month: “We’ve now exhausted the cumulative sum of human knowledge for AI training.”
But while ‘Peak AI’ presents an unprecedented challenge for the AI industry, it also presents a major opportunity for other industries — namely market research.
Why does ‘Peak AI’ matter?
In recent years AI models have developed rapidly, not only because of the scale of investment, but because there has been an abundance of information with which to train them.
AI companies have quite literally had a world of content at their feet, with text, images, videos and data sets —barring those that are copyright protected— scraped from the internet and used to train AI models. But now, they have run out. And without fresh, high-quality content to feed into AI models, artificial intelligence will struggle to evolve.
It’s a moment of reckoning for the AI industry: either AI’s growth slows, or the industry finds new sources of real-world insights.
Market research holds the key
With the internet exhausted, AI companies have no choice but to turn to the untapped market research industry to gain access to fresh data and new insights. And lucky for them, the industry is big enough to meet the demand — as of 2025, $130bn will be spent collecting, processing, and analysing market research data.
Looking at this data in basic principles, it’s an unbiased, representative, anonymous quantification of human behaviour and opinion. At its foundation, it’s a conversation coded into 1s and 0s: exactly what AI companies need to tackle this fundamental data crunch.
In fact, since hitting ‘Peak AI’ the demand for new insights has skyrocketed, with AI companies realising that recycled datasets are not going to cut it.
As such, data collection in the form of surveys, behavioural tracking and audience insights is about to become the lifeblood of AI’s next phase.
However, market research is antiquated and highly fragmented, with very little technology focus. Not to mention that the vast majority of market research data is still created and served to one (likely corporate) customer, who utilises a fraction of its capability, before consigning it to the digital shelf. In short, the market research industry is not reaching its full potential either.
The issue of ‘Peak AI’ presents a big opportunity for the market research industry to modernise and aggregate its data at scale in order to deliver for AI use-cases.
The rise of synthetic data
As I’ve alluded to, AI is only as good as the data it is trained on, so if we want AI to continue getting smarter, it needs to be able to access new, real-time, human-led data. This is especially important when you consider that AI doesn’t just consume data, it also generates it.
Synthetic data —AI created survey responses and predictive insights, which aim to mimic real-world human responses— is growing in popularity as more and more people turn to large language models for creative and strategic insights.
But, while synthetic data has the potential to supplement human-led research, it cannot replace it entirely. This is because AI-generated insights are based on existing patterns, meaning they lack the unpredictability, emotional nuance, and cultural shifts that come from real human input.
Injecting AI models with more real-time human insights from market research will not only go a ways to solving this issue, it will also create a feedback loop that benefits AI users and market research companies alike.
The research feedback loop
The feedback loop works like this: market research firms collect real-world insights through surveys, behavioural tracking, and qualitative research; AI models then use this data to refine their understanding of consumer behaviour, personal preferences, and emerging trends.
This information is then used by AI models to generate synthetic data, which researchers analyse and validate against real-world findings — improving both the research process and the AI itself.
Done correctly, this cycle can be incredibly powerful, especially as market research data is often global, it is unique in that it is totally representative —you know which demographic shared what view, something data scraped from the internet can’t do— it is constantly being collated, so AI always has the latest information, and it is 100% non personally identifiable information (Pii), which is essential for quick adoption.
In summary, AI-assisted research can enhance speed, scale, and predictive accuracy. But the key to making it work is ensuring that real human data remains at the core. Without new, unbiased, and continuously updated human insight, AI risks becoming an echo chamber — trapped in its own self-referential and biased loops, rather than evolving in meaningful ways.
A golden age for market research
While the AI industry may see ‘Peak AI’ as a problem, it could actually serve as a turning point. There is a clear fork in the road: either embrace the market research industry’s role in forwarding artificial intelligence or have a sub-par product that isn’t representative of what people are thinking now.
As for the market research company’s role, as much as it would like to present itself as a ‘ready to go’ option, the truth is that it needs to modernise, embrace new technology and aggregate its data effectively in order to seize the opportunity Peak AI represents.
Ultimately, in order to move beyond Peak AI, AI and market research companies need to work hand in hand, both stepping up to invest in new methods of capturing and analysing human insight.
Companies that embrace this shift—leveraging both real-world data and AI-powered analytics—will be the ones shaping the future of the technology.
