The future of AI might rely on getting your Slack DMs.
It’s not just your DMs, to be clear. It’s internal memos, transaction records, contracts, and even that onboarding guide you probably didn’t read when you started your job.
Basically, if it’s work-related and has been documented, AI companies are interested. The rationale is straightforward: If AI tools are going to automate work, they need a deep understanding of what the work is.
BI did a deep dive on AI companies’ race to scoop up as much data as possible in its series “The AI Data Grab.”
BI’s Charles Rollet, Tom Carter, and Stephen Council first unpacked the wild market that’s sprung up for this data. Things move quickly, including “exploding offers.”
Others are figuring out how to leverage their databases. Law firms, which often have detailed archives, are a great example. BI’s Melia Robinson has more on that.
And sometimes it’s about actually creating the data yourself. That’s the case in robotics, writes BI’s Rya Jetha. Companies are shelling out big to have people record everyday tasks like cooking and cleaning.
AI’s data push could renew interest in a space that’s already navigated an info-hungry industry.
Alternative data, unstructured datasets such as satellite imagery and card transactions, became a hot commodity on Wall Street in the mid-2010s.
These non-traditional datasets are supposed to give investors an edge. Want to know if Home Depot will beat earnings estimates? Analyze satellite images of its parking lots to see whether foot traffic is rising or declining.
When I covered the space closely with my colleague Bradley Saacks, the data providers often highlighted that the real market opportunity was selling into big corporates.
Hedge funds might be rich, but there are only so many of them to go around. The market also has a ceiling. If too many people are trading off the same datasets, you lose your potential edge.
AI’s data needs can fill the gap alt-data providers hoped corporates would. And unlike hedge funds interested in real-time, market-relevant data, these firms benefit from a wider, deeper catalog.
Some players already see an opportunity. When former Point72 data wizard Kirk McKeown launched a data startup last year, he told Bradley it aimed to package data in an easily accessible way for AI models to scoop up.
It’s promising, but there are no guarantees. Alt data faced real growing pains as providers struggled to scale their offerings while maintaining data quality. AI companies’ needs might differ, but their expectations won’t be any easier. And in some cases, money managers might find the most useful data in their own backyard.
In the meantime, follow Bradley here for more coverage on how alt-data providers are navigating the AI era. And keep an eye out for a hedge-fund-focused newsletter from him later this year.
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