Investing in the AI Revolution with GigaOm CTO Howard Holton (Part 1)
Investing in the AI Revolution with GigaOm CTO Howard Holton (Part 1)

Investing in the AI Revolution with GigaOm CTO Howard Holton (Part 1)

Alpha_ks

23 min
Business & Finance
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Paglalarawan

<p> </p> <p>The tech world moves fast, and no one wants to get left behind.</p> <p>Emerging technologies like generative AI, large language models, and open-source platforms have the potential to completely transform individual businesses or even entire industries. Those who embrace them will profit, while those who don&#39;t will become irrelevant.</p> <p>Yet a &quot;Hype Cycle&quot; also tends to accommodate new technologies. Several new movements in the tech world that were believed to be the Next Big Thing turned out not to be. 3D printing and NFTs are recent examples.</p> <p>How should forward-thinking and growth-minded investors separate out the game-changers from the flashes-in-the-pan? What new technologies are actually gaining momentum, and which will never live up to their expectations?</p> <p>To answer those questions, we&#39;ve brought in an expert. 7investing CEO Simon Erickson recently spoke with <a href="https://www.linkedin.com/in/howardholton/">Howard Holton</a>, the Chief Technology Officer of GigaOm. GigaOm brings the decision-making executives of progressive companies up-to-speed about emerging technologies and then helping to implement them across their organizations. (You can also  see last year&#39;s conversation with GigaOm CEO Ben Book <a href="https://7investing.com/articles/the-digital-transformation-is-underway-with-gigaom-ceo-ben-book/">here</a>.)</p> <p>In Part 1 of their conversation, Simon and Howard first addressed the status quo of generative AI. AI is being used for &#39;fun&#39; things today -- like creating lifelike images through MidJourney -- but even this requires significant computing power. Howard explains that innovative companies are already deploying AI at scale, but that they need appropriate data strategies and governance policies in order to maximize their success rate. This is similarly true for the flood of recent large language models; those that endure will require filters to curate the noisy flood of data from all across the internet is a way that is actually u

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