“There is no AI without data, there is no AI without unstructured data, and there is no AI without large-scale unstructured data,” said Chet Kapoor, chairman and CEO of DataStax, a data management company.
Kapoor kicked off a conversation about “new data pipelines” in the context of modern AI applications at TechCrunch Disrupt 2024, where he was joined by Vanessa Larco, partner at VC firm NEA. and George Fraser, CEO of data integration platform Fivetran. The chat covered a lot of ground, including the importance of data quality and the role of real-time data in generative AI, but one of the biggest takeaways was the importance of prioritizing product-market fit over scale while still in the early stages. of AI. My advice for companies looking to dive into the dizzying world of generative AI is simple. Don’t be overly ambitious at first, but focus on practical, gradual progress. Why? We’re still figuring it all out.
“The most important thing about generative AI is that it all depends on people,” said Kapoor. “The SWAT teams that actually start and build the first few projects don’t read manuals. They are writing manuals on how to do generative AI apps.”
It’s true that data and AI go hand in hand, but it can be easy to feel overwhelmed by the sheer volume of data your company has. Some data may be sensitive, highly protected, and even stored in numerous locations. Larco, who has worked with and sits on the boards of numerous startups across the B2C and B2B spectrum, suggests a simple, yet practical approach to creating real value early on.
“Work backwards from what you are trying to accomplish: What are you trying to solve and what data do you need?” Larko said. “Find the data, wherever it is, and use it for this purpose.”
This is in contrast to applying generative AI across the company from the beginning, throwing all the data into a large language model (LLM) and hoping the right results come out at the end. According to Larco, this is likely to lead to inaccurate and costly confusion. “Start small.” she said “What we’re seeing is companies starting small with internal applications, with very specific goals, and then looking for data that matches what they want to achieve.”
Fraser, who has led ‘data movement’ platform Fivetran since its founding 12 years ago and has secured high-profile clients such as OpenAI and Salesforce, suggested that companies should focus only on the real problems they are currently facing.
“Only solve the problem you are currently experiencing. That’s the mantra.” Fraser said. “99% of the cost of innovation comes from not doing the right thing, rather than planning the scale in advance and doing it as planned. “Although it’s something we always think about in retrospect, it’s not 99% of the costs you bear.”
Like the early days of the web and, more recently, the smartphone revolution, the early applications and use cases of generative AI showed a powerful new future powered by AI. But so far, they haven’t necessarily been game-changing.
“I call this the Angry Birds era of generative AI,” said Kapoor. “It doesn’t completely change my life. No one is doing my laundry yet. Every company I work with this year is small and putting something into production internally. Because you’re actually working on how to solve a problem and build a team to make it happen. Next year is what I call the year of change. “People will start using apps that will really start to change the trajectory of the companies they work for.”
