AI is a race to zero, what does that mean for data?

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If you believe that AI is a race to the bottom or a race to zero, what does that mean for data? AI is everywhere, but nowhere. For as much hype as we hear, it isn’t in your face all day everyday. I do believe AI will be like cloud computing, it will be part of every company’s tech stack. If this is true, what does that mean for data and the value of data?

I think if AI is everywhere, then data will need to be everywhere. Data will be needed for continuing to train models so that AI has the latest relevant data. The use case will continue to expand from niche AI models for specific industries to AI focused on images and voice. The list goes on and on.

If data is everywhere, does it become more or less valuable? That is a good question that I debate a lot. I think demand-based consumption of data will make data more accessible, and a fair price will be found. CarbonArc for example has built the consumption-based marketplace to make this all possible and their founder has seen the demand for this from clients firsthand after working at a data-hungry investment firm for over a decade. Data owners, data vendors, and data collection companies will ultimately see increases in revenue. Those who are agile, meet their clients where they are, and have flexible pricing models will win.

Having a flexible pricing model could be demand-based, consumption-based, or a combination of different pricing models. This is what Streamft.com is out to solve for the finance teams and is the sponsor of this newsletter today. With the demand of data growing and the amount of data growing, structuring data will become more important. Also of importance will be web scraping capabilities. I have said this in the past, but more and more companies are realizing the need for web scraping capabilities. If not in-house, then a trusted partner that can web scrape in a compliant and efficient manner. Scraping is one piece, doing it in a compliant manner, abiding by robots.txt or a firm’s internal rules of the road, while also structuring the data before delivery will become one of the larger demands over the next few years.

There are a lot of web scraping tools and firms and the quality and price vary tremendously. I have seen a lot of companies try to do this in-house with a small team or with a few off-the-shelf tools and ultimately decide it is too hard. I think AI will and is making web scraping more efficient. The reality is many companies are all scraping the same data. I think more and more websites will realize this and will look to monetize their data.

If we think about websites that are scraped like Best Buy, eBay, Alibaba, StockX, Amazon, etc. - Do we wake up one day and see these firms begin to offer their data via API? Rather than fight the scraping, why not turn this into a revenue generator? Or partner with a firm web scraping to help them think about the best way to monetize their data. I think we will see this happen in the next few years. Likely it will be firms struggling to find new revenue sources, but over time more firms will realize this is a good opportunity to not only monetize, but learn how and why firms want access to the data.

AI is a race to zero and the VC community is throwing money at it non-stop, the question is who will see the data opportunity and throw money at building a bigger and bigger unique data moat?