Welcome Back - Featured Keynotes

Transcribed by qwen3-asr-1.7b with a schedule prompt. Possible prompt artifacts, names heard more often here than in the unprompted Voice Memos transcript (Qwen vs Voice Memos): Tenex (2 vs 0), Coatue (1 vs 0). Check them against the audio before quoting.

00:00:00 — Closing Keynote Block Welcome & Partner Shoutouts

How's it going? How's the energy? Well, welcome back for the closing keynote block here at AI Engineer New York 2026. I'm Alex Lieberman, co-founder, co-making partner at Tenex, a Flyte partner for Fortune 500 businesses, and we are most of the way through the first session today, and hopefully, it's been filled with incredible conversation, which I'm going to share a little bit about some of the conversations I've had offstage in a minute. But hopefully that the mix of the programming, the conversation we've had amongst everyone has been really fruitful today.

Now, before we hop into the final block of the day, let's just take a moment again to give a round of applause to our partners who made this entire event possible. So let's start with our presenting sponsor, Arize. Let's give a shout out to our guys. Yeah, guys. Next up, we have our platinum sponsors, Oracle, Sonar, WorkOS, and Exa. Let's give a shout out to them, and then two more. We have full sponsors. I love them all. They're all my favorite children. Can't read them all off right now, but thank you so much, our silver sponsors, and to all of our supporting partners, who make this possible. Thank everyone. Yeah, I don't know if that was the first person who said they have the most energy, but that person has a lot of energy.

00:01:35 — Talks as Surface Area for Edge Conversations

So before we hop into the next block, one thing that I just wanted to share, and I'm sure a lot of you are already feeling this already, is not only is the energy palpable across the room in between all the sessions, but what you realize when you come to AI engineer is that the content is incredible. The speakers that are curated are incredible. The depth by which they talk about the work they're doing is incredible, but this is also just surface area that acts as a starting point for conversation amongst everyone in the room, and so for me, not only has it been incredible to stand in the back of the room at various points of the day and just listen to these incredible speakers who are truly living in Edge City, but then to use all of these conversations, all these talks as surface area to have conversations with folks.

00:02:29 — The Enterprise AI Diffusion Gap Across Four Milestones

So, a few conversations that come to mind that I've had throughout the day because of the content — one was a conversation with Switz, one of the people behind this incredible event. We're talking about just the diffusion gap that exists in enterprise AI right now. Just some fun stats for you all. We talked to hundreds of big enterprises at Tenex across financial services, healthcare, industrials, etc., and the way I think about diffusion of AI across these organizations is almost in milestones of a race. So the first big milestone at an enterprise is just adopting a general purpose LLM. Out of curiosity, of the what's called hundred firms we talked to, what percentage of those firms do you think have adopted and rolled out an LLM to all their employees? Someone's screaming out a number.

Nine percent. I think I heard seventeen percent. So specific. Ninety percent. So ninety percent of the enterprises we work with have rolled out general-purpose LLM. Under that is people actually getting trained in how to use the technology, how to actually use these surfaces in their work — that's like sixty percent. Next up is coding agents, agents that are actually used within engineering and product workflows — that's around 50% of all the enterprises we've been in, and then the final one where we are truly still day one is agents actually working in non-engineering workloads in an organization, that is around five percent right now. So going back to what I said earlier, this community really is at its — even the topics that are being talked about, we are going to continue to see those play out over the next 12, 24, 36 months. I've had a variety of conversations like this throughout the day: To Fusion Gap, open source versus fine-tuned models, how hard it is to hire great AI engineers at scale.

And so, as we go about the rest of today, but also as you think about the conference in general, use the content as a starting point to have interesting conversations at the edge. With that, I would love to introduce you to our next talk, which is with Frank Long, the head of AI at Coatue. He's going to be talking about a hedge fund as a function. Let's welcome Frank.