[ad_1]
“There is no AI without data, there is no AI without unstructured data, and there is no AI without unstructured data at scale,” he said. Chet KapoorChairman and CEO of data management company DataStax.
Kapoor was kicking off a conversation at TechCrunch Disrupt 2024 on “New Data Pipelines” in the context of modern AI applications, where he was joined by featherSa larcoPartner at VC firm NEA; and George FraserCEO of data integration platform Fivetran. While the chat covered multiple bases, such as the importance of data quality and the role of real-time data in generative AI, one of the most important takeaways was the importance of prioritizing product market fit at scale in what are still truly the early days of AI. The advice for companies looking to jump into the amazing world of generative AI is clear and straightforward – don’t be overly ambitious at first, and focus on incremental, practical progress. the reason? We’re really still figuring it all out.
“The most important thing about generative AI is that it all comes back to the people,” Kapoor said. “The SWAT teams that actually go out and build the first few projects — they’re not reading a manual; they’re writing the manual for how generative AI applications work.
Although data and AI go hand in hand, it can be easy to become overwhelmed by the sheer amount of data a company may have, some of which may be sensitive, subject to strict security, and may be stored across countless locations. Larco, who works with (and sits on the board of directors of) several startups across the B2C and B2B spectrum, suggested a simple but practical approach to unlocking real value in these early days.
“Work backwards on what you are trying to achieve – what are you trying to solve, and what data do you need?” Larco said. “Find that data wherever you find it, and then use it for that purpose.”
This is in contrast to trying to spread generative AI across the entire company from the beginning, throwing all their data at a large language model (LLM) and hoping it spits out the right thing in the end. This, according to Arco, potentially creates an inaccurate and costly mess. “Start small,” she said. “What we’re seeing is companies starting small, with internal applications, with very specific goals, and then finding data that aligns with what they’re trying to achieve.”
Fraser, who has led the “data movement” platform Fivetran since its inception 12 years ago, amassing big-name clients like OpenAI and Salesforce on the way, suggested that companies should focus narrowly on the real problems they face right now.
“Just solve the problems you have today; that’s the motto,” Fraser said. “The costs of innovation are always 99% in the things you built that didn’t work, not in the things that did work and that you wish you had planned to scale up in advance. Although these are issues we always think about in retrospect, they do not represent 99% of the cost to you.
Just as in the early days of the web and, more recently, the smartphone revolution, early applications and use cases for generative AI have shown glimpses of a powerful new AI-powered future. But so far, they haven’t necessarily changed the rules of the game.
“I call this the Angry Birds era of generative AI,” Kapoor said. “It hasn’t completely changed my life, and no one has done my laundry yet. This year, every organization I work with is putting something into production — small and internal, but putting it into production because they’re actually working out the kinks, around how to build teams To make that happen, next year is what I call the year of transformation, when people start using apps that actually start changing the course of the company they work for.
[ad_2]