Make sure to check out our new AI + a16z feed: https://link.chtbl.com/aiplusa16z   a16z General Partner Anjney Midha joins the podcast to discuss what's happening with hardware for artificial intelligence. Nvidia might have cornered the market on training workloads for now, but he believes there's a big opportunity at the inference layer — especially for wearable or similar devices that can become a natural part of our everyday interactions.  Here's one small passage that speaks to his larger thesis on where we're heading: "I think why we're seeing so many developers flock to Ollama is because there is a lot of demand from consumers to interact with language models in private ways. And that means that they're going to have to figure out how to get the models to run locally without ever leaving without ever the user's context, and data leaving the user's device. And that's going to result, I think, in a renaissance of new kinds of chips that are capable of handling massive workloads of inference on device. "We are yet to see those unlocked, but the good news is that open source models are phenomenal at unlocking efficiency.  The open source language model ecosystem is just so ravenous." More from Anjney: The Quest for AGI: Q*, Self-Play, and Synthetic Data Making the Most of Open Source AI Safety in Numbers: Keeping AI Open Investing in Luma AI Follow everyone on X: Anjney Midha Derrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.   Stay Updated:  Find a16z on Twitter: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Subscribe on your favorite podcast app: https://a16z.simplecast.com/ Follow our host: https://twitter.com/stephsmithio Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

Human nature fears the unknown, and with the rapid progress of AI, concerns naturally arise. Uncanny robocalls, data breaches, and misinformation floods are among the worries. But what about security in the era of large language models? In this episode, we hear from security leaders at OpenAI, Anthropic, and Google DeepMind. Matt Knight, Head of Security at OpenAI, Jason Clinton, CISO at Anthropic, and Vijay Bolina, CISO at Google DeepMind, are joined by Joel de la Garza, operating partner at a16z and former chief security officer at Box and Citigroup. Together, they explore how large language models impact security, including changes in offense and defense strategies, misuse by nation-state actors, prompt engineering, and more. In this changing environment, how do LLMs transform security dynamics? Let's uncover the answers.   Resources: Find Joel on LinkedIn: https://www.linkedin.com/in/3448827723723234/ Find Vijay Bolina on Twitter: https://twitter.com/vijaybolina Find Jason Clinton on Twitter: https://twitter.com/JasonDClinton Find Matt Knight  on Twitter: https://twitter.com/embeddedsec   Stay Updated:  Find a16z on Twitter: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Subscribe on your favorite podcast app: https://a16z.simplecast.com/ Follow our host: https://twitter.com/stephsmithio Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.