AI Companies Are Reportedly Struggling to Come Up With New and Better Products

Artificial intelligence (AI) might be running into a cul-de-sac thanks to data shortages and technological constraints, according to a report from Bloomberg.

It’s almost three years since generative AI burst onto the scene in the form of chatbots, AI image generators, and music generators. Such a large leap forward caused many people to wonder what’s next.

So too did excited tech companies and their shareholders but Bloomberg reports that Google, OpenAI, and Anthropic are all struggling to build more advanced AI.

According to Bloomberg’s sources, the model OpenAI is working on, Orion, is not hitting internal expectations. Similarly, Google’s newest iteration of Gemini is also not much better than the previous one. Anthropic has also delayed the release of its Claude model.

One of the reasons cited is that “it’s become increasingly difficult to find new, untapped sources of high-quality, human-made training data that can be used to build more advanced AI systems.”

This is truly fascinating. It’s well-known that AI companies ripped virtually all available data across the open web to build various models. It is safe to bet that almost all photos online have been taken for AI training purposes.

It’s something that I wrote about back in June 2023 in which I pointed out that AI image generators can’t survive without fresh photography.

Bloomberg says that with all of the open web scraped, tech companies are having a hard time plugging the gap. Some of them are turning to AI images but researchers have found that this method has limitations. One study revealed that AI trained on computer-generated material turns to mush.

All of this puts a dampener on the dream of artificial general intelligence (AGI). This refers to supposed AI systems that are more intelligent than humans. OpenAI and Anthropic have both previously stated that AGI is close.

“The AGI bubble is bursting a little bit,” Margaret Mitchell, chief ethics scientist at AI startup Hugging Face, tells Bloomberg. Mitchells says that AI companies will need to take “different training approaches” for AGI to be achieved.

“It is less about quantity and more about quality and diversity of data,” adds Lila Tretikov, head of AI strategy at New Enterprise Associates and former deputy chief technology officer at Microsoft. “We can generate quantity synthetically, yet we struggle to get unique, high-quality datasets without human guidance.”