AI + a16z

a16z partners Guido Appenzeller and Matt Bornstein join Derrick Harris to discuss the state of the generative AI market, about 18 months after it really kicked into high gear with the release of ChatGPT — everything from the emergence of powerful open source LLMs to the excitement around AI-generated music.If there's one major lesson to learn, it's that although we've made some very impressive technological strides and companies are generating meaningful revenue, this is still a a very fluid space. As Matt puts it during the discussion:"For nearly all AI applications and most model providers,  growth is kind of a sawtooth pattern, meaning when there's a big new amazing thing announced, you see very fast growth.  And when it's been a while since the last release, growth kind of can flatten off. And you can imagine retention can be  all over the place, too . . ."I think every time we're in a flat period, people start to think, 'Oh, it's mature now,  the, the gold rush is over. What happens next?' But then a new spike almost always comes, or at least has over the last 18 months or so. So a lot of this depends on your time horizon, and I think we're still in this period of, like, if you think growth has slowed, wait a month  and see it change."Follow everyone on X:Guido AppenzellerMatt BornsteinDerrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

In this bonus episode, recorded live at our San Francisco office, security-startup founders Dean De Beer (Command Zero), Kevin Tian (Doppel), and Travis McPeak (Resourcely) share their thoughts on generative AI, as well as their experiences building with LLMs and dealing with LLM-based threats.Here's a sample of what Dean had to say about the myriad considerations when choosing, and operating, a large language model:"The more advanced your use case is, the more requirements you have, the more data you attach to it, the more complex your prompts — ll this is going to change your inference time. "I liken this to perceived waiting time for an elevator. There's data scientists at places like Otis that actually work on that problem. You know, no one wants to wait 45 seconds for an elevator, but taking the stairs will take them half an hour if they're going to the top floor of . . . something. Same thing here: If I can generate an outcome in 90 seconds, it's still too long from the user's perspective, even if them building out and figuring out the data and building that report [would have] took them four hours . . . two days."Follow everyone:Dean De BeerKevin TianTravis McPeakDerrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

In this episode of the AI + a16z podcast, a16z General Partner Zane Lackey and a16z Partner Joel de la Garza sit down with Derrick Harris to discuss how generative AI — LLMs, in particular — and foundation models could effect profound change in cybersecurity. After years of AI-washing by security vendors, they explain why the hype is legitimate this time as AI provides  a real opportunity to help security teams cut through the noise and automate away the types of drudgery that lead to mistakes."Often when you're running a security team, you're not only drowning in noise, but you're drowning in just the volume of things going on," Zane explains. "And so I think a lot of security teams are excited about, 'Can we utilize AI and LLMs to really take at least some of that off of our plate?'"I think it's still very much an open question of how far they go in helping us, but even taking some meaningful percentage off of our plate in terms of overall work is going to really help security teams overall."Follow everyone:Zane LackeyJoel de la GarzaDerrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

Socket Founder and CEO Feross Aboukhadijeh joins a16z's Joel de la Garza and Derrick Harris to discuss the open-source software supply chain. Feross and Joel share their thoughts and insights on topics ranging from the recent XZutils attack to how large language models can help overcome understaffed security teams and overwhelmed developers. Despite some increasingly sophisticated attacks making headlines and compromising countless systems, they're optimistic that LLMs, in particular, could be a turning point for security blue teams. As Feross sums up one possibility:"The way we think about gen AI on the defensive side is that it's not as good as a human looking at the code, but it's something. . . . Our challenge is that we want to scan all the open source code that exists out there. That is not something you can pay humans to do. That is not scalable at all. But, with the right techniques, with the right pre-filtering stages, you can actually put a lot of that stuff through LLMs and out the other side will pop a list of of risky packages."And then that's a much smaller number that you can have humans take a look at. And so we're using it as a tool . . . to find the needle in the haystack, what is worth looking at. It's not perfect, but it can help cut down on the noise and it can even make this problem tractable, which previously wasn't even tractable."More about Socket and  cybersecurity:SocketInvesting in SocketHiring a CISOFollow everyone :Feross AboukhadijehJoel de la GarzaDerrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

In this episode, though, we’re traveling back in time to distant — in AI years, at least — past of 2020. Because amid all the news over the past 18 or so months, it’s easy to forget that generative AI — and LLMs, in particular — have been around for a while. OpenAI released its GPT-2 paper in late 2018, which excited the AI research community, and in 2020 made GPT-3 (as well as other capabilities) publicly available for the first time via its API. This episode dates back to that point in time (it was published in July 2020), when GPT-3 piqued the interest of the broader developer community and people really started testing what was possible.And although it doesn’t predict the precambrian explosion of multimodal models, regulatory and copyright debate, and entrepreneurial activity that would hit a couple of years later — and who could have? — it does set the table for some of the bigger — and still unanswered — questions about what tools like LLMs actually mean from a business perspective. And, perhaps more importantly, what they ultimately mean for how we define intelligence.So set your wayback machine to the seemingly long ago summer of 2020 and enjoy a16z’s Sonal Chokshi and Frank Chen discussing the advent of commercially available LLMs. Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

Pinecone Founder and CEO Edo Liberty joins a16z's Satish Talluri and Derrick Harris to discuss the promises, challenges, and opportunities for vector databases and retrieval augmented generation (RAG). He also shares insights and highlights from a decades-long career in machine learning, which includes stints running research teams at both Yahoo and Amazon Web Services.Because he's been at this a long time,  and despite its utility, Edo understands that RAG — like most of today's popular AI concepts — is still very much a progress:"I think RAG  today is where transformers were in 2017. It's clunky and weird and hard to get right. And it  has a lot of sharp edges, but it already does something amazing. Sometimes, most of the time, the very early adopters and the very advanced users are already picking it up and running with it and lovingly deal with all the sharp edges ..."Making progress on RAG, making progress on information retrieval, and making progress on making AI more knowledgeable and less hallucinatory and more dependable, is a complete greenfield today. There's an infinite amount of innovation that will have to go into it."More about Pinecone and RAG:Investing in PineconeRetrieval Augmented Generation (RAG)Emerging Architectures for LLM ApplicationsFollow everyone on X:Edo LibertySatish TalluriDerrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

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 DataMaking the Most of Open Source AISafety in Numbers: Keeping AI OpenInvesting in Luma AIFollow everyone on X:Anjney MidhaDerrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

Naveen Rao, vice president of generative AI at Databricks, joins a16z's Matt Bornstein and Derrick Harris to discuss enterprise usage of LLMs and generative AI. Naveen is particularly knowledgeable about the space, having spent years building AI chips first at Qualcomm and then as the founder of AI chip startup Nervana Systems back in 2014. Intel acquired Nervana in 2016.After a stint at Intel, Rao re-emerged with MosaicML in 2021. This time, he focused on the software side of things, helping customers train their own LLMs, and also fine-tune foundation models, on top of an optimized tech stack. Databricks acquired Mosaic in July of 2023.This discussion covers the gamut of generative AI topics — from basic theory to specialized chips — to  although we focus on how the enterprise LLM market is shaping up. Naveen also shares his thoughts on why he prefers finally being part of the technology in-crowd, even if it means he can’t escape talking about AI outside of work.More information:LLMs at DatabricksMosaic ResearchMore AI content from a16zFollow everyone on X:Naveen RaoMatt BornsteinDerrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

There are few terms in the world of AI — if any — that invoke more of a reaction than a simple four-letter word: Open. Whether it’s industry debates over business models and the actual definition of open, or the US government actively discussing how to regulate open models, seemingly everyone has an opinion on what it means for AI models to be open. The good, the bad, and the ugly.But to be fair, there’s good reason for this. In a world where many developers have come to expect open source tools at every level of the stack, the idea of powerful models locked behind enterprise licenses and corporate ethics can be disconcerting — especially for a technology as game-changing as AI promises to be. It’s a matter of who has the ability to innovate in the space, and whose release schedules and guardrails they’re beholden to.This is why, back in February, a16z convened a panel of experts to discuss the state — and future — of open source AI models.Featuring:Jim Zemlin (Executive Director, Linux Foundation)Mitchell Baker (Executive Chair, Mozilla Corp.)Percy Liang (Associate Professor, Stanford; Cofounder, Together AI)Anjney Midha (General Partner, a16z)Derrick Harris (Editorial Partner, a16z) Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

The AI + a16z podcast captures our thinking on AI across a broad swath of areas, from the infrastructure that powers today’s foundation models to how specific tools, like LLMs, are reshaping the hiring process. Looking forward, you can expect to hear about a list of topics that includes the latest advances in generative AI, cybersecurity, and the emerging stack of tools for building and running LLMs.Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.