Welcome to the podcast. that has just hit unicorn status.
And, of course, there's a whole wave of inference startups that are showing basically where I think a lot of the AI money is actually gonna be flowing in 2026.
Plus, I wanna break down a new benchmark that shows why white collar jobs are actually not disappearing nearly as much as they were predicted in the past.
And again, a former Sequoia partner is betting that AI agents can finally fix calendar scheduling.
If you want to get into some interesting AI use cases,
This is the AI Chat podcast, which is a daily podcast covering the most important news and conversations in AI.
So let's get into the episode.
I think, according to reporting from the information they have basically, if you, by the way, if you don't have the information it's, I think, one of the best insider tech news.
So, you know, free plug to them.
But OpenAI.
They have a whole report on which says that OpenAI has appointed Brett Zoff to lead their enterprise sales efforts.
Zoph is pretty familiar inside of the company.
He previously was the vice president of post-training inference, before he left in 2024 to go co-found Thinking Machine Labs with former OpenAI CEO or CTO, Miriam Ratti.
So this, I mean, just in and of itself, this got a lot of headlines.
The fact that he left OpenAI to go found this company and then you know they raised like a billion dollars and less than a year later, before any products were shipped, he came back to OpenAI.
And what you know, it's like has this new role inside, which is kind of funny.
He came back last week.
And I think there's not like a lot of new like circumstances that we know about why he left Thinking Machine Labs.
So that's pretty unclear.
The timing is pretty notable.
Opening Eye right now has a ton of pressure in the enterprise market.
And I think this role is going to put him kind of at the center of their 2026 growth strategy.
I think, despite launching ChatGPT Enterprise earlier than a lot of the other competitors, OpenAI's market share has actually steadily declined.
I think not a lot of people know this, but there's some data that came out of Memlo Ventures that says that OpenAI's enterprise LLM usage has fallen roughly 50 in 2023 to about 27 by the end of 25, which means that you can look at some of their other competitors to see you know what else is happening in the industry.
Anthropic, for example, now leads has around 40%.
And Google's Gemini has it's been making a bunch of gains.
It's a lot slower but it is making some.
It's getting some ground there.
So I think this isn't, you know, lost on OpenAI's leadership.
Sam Altman has apparently kind of flagged the fact that OpenAI is growing as a big concern inside of the company.
There was kind of the famous, you know, red flag moment.
And the CFO, Sarah Fryer, also said the enterprise growth is a top priority this year for them.
They've also expanded their partnership with ServiceNow.
I think that kind of shows OpenAI is planning to fight a So this is going to be interesting.
The next thing I want to talk about is a company called LiveKit.
It's one of OpenAI's really key infrastructure partners and they have officially reached unicorn status.
So they are a real-time voice and video infrastructure company.
They power ChatGPT's voice mode.
They just raised $100 million at a $1 billion valuation.
The round was led by Index Ventures, Tier 1 VC.
They also had Alimeter and Redpoint and some other pre-existing investors that all jumped in on that.
But LiveKit essentially started as an open source project during the pandemic.
The founder, Russ Dessa and David Zhao.
They're focused on building interruption.
Free audio and video tools.
And then the business just sort of took off from there.
I think it's interesting.
It took off, especially when a lot of companies started asking for them to basically do it but manage it.
So they built a cloud based solution for the voice AI.
So today LiveKit's customers.
They have OpenAI XAI Salesforce Tesla, a bunch of emergency services, some mental health providers.
But I think the thing that's really interesting for me is that, as AI voice is becoming a lot more mainstream, the infrastructure layer is turning into a really valuable part of the stack.
And so I think the fact they were able to raise 100 million and of course they're fueling like opening eyes, voice mode and a lot of other, a lot of other players shows just how valuable this company and others like it will be.
Okay, so in that same theme, I want to talk about another company, which is called Infraac.
This is a company that is commercializing a lot of popular open source projects, but one in particular is called VLLM, and they have just raised 150 million in seed funding.
They have an $800 million valuation.
The round was co-led by A16Z and Lightspeed.
VLLM and a bunch of similar projects are focusing on inference or essentially the process of running models really efficiently in production.
I think right now, as we're seeing kind of a shift in AI from these sort of big training breakthroughs to a lot more real-world deployment, investors are putting a lot of money into the tools that make models faster and cheaper and more scalable.
So Infrax Story, I think, is very close to another recent spin-out which is called Radiax Arc, which also commercialized SG Lang.
Both of those projects originated from UC Berkeley's AI research ecosystem and I think they show a pretty big trend in the industry, which is that training is going to definitely grab headlines, but inference is where a lot of the real business value is actually being created.
I mean, the fact that they were able to raise $800 million, I think, goes to prove this.
So Now for one of the most important research stories in AI right now and I think this is one that cuts through a lot of the hype that we see there's a new benchmark from Merkur.
And I think the results were really impressive or very interesting in any case.
So even the best performing models struggled to get more than about 25% of the questions right.
Gemini 3 Flash was actually the leader here.
It did 24%.
Right behind it was GPT 5.2 at 23%.
I think a lot of the others were closer to 18%.
The biggest failure point was not just kind of like reasoning and isolation, but it was actually understanding operating across multiple domains.
So having real knowledge, and I think all of this requires pulling information from emails or documents or internal policies, and collaboration tools right like Slack and Google Drive.
And with all like the complexity of tying all that together, I think today's agent models are still really struggling to maintain coherence across all of those environments.
And I think the implication to that is actually really big.
Basically, AI is moving, improving a lot faster, but it's not yet ready to actually replace a lot of the high value professional roles inside of companies.
So I think, for now anyways, the models look a lot like, a lot less like autonomous workers and probably more like interns who need heavy supervision.
This is where I've found them.
And you definitely can get these AI models to do one particular task very well and save you time on it.
But it's not like it's going to go take over a full role yet, yet keyword yet.
Okay finally today, a former Sequoia partner is betting that AI agents can finally fix one of work's most heated problems, which is scheduling.
This one I thought was kind of a funny story.
Essentially Kazi Kimiji, who spent about six years at Sequoia Capital.
He just launched an AI calendar startup.
It's called BlockKit.
They just raised $5 million seed round, which was led by Sequoia.
So, you know, I'm sure they wanted to get him to go start that and thought it was a great idea.
But unlike a bunch of tools like Calendly, basically what it is doing it's not relying on links or kind of manual coordination.
Instead, they have AI agents that are negotiating directly with each other to schedule meetings.
It's kind of like this meme I recently or this post I recently saw on X, which someone said I'll have my cloud, contact your cloud and get back to you.
And I just thought that was kind of funny because today, like that was really what's happening.
These AI agents you just have, like a calendar agent, and it's going to go look at your calendar and it's going to find a good time.
And theirs is also going to do that and negotiate back and forth.
And then they pick a time and neither of you actually have to talk about it which, to be fair.
Who likes having to send an email that's like I'm available from this time to this time, on Wednesday to Thursday, and even dropping a link?
Right.
It's just it's just energy and time.
So I think this is really cool.
People want to, you know, book a time with each other. have the agents do it.
That sounds great.
Let's get on a call.
And they say, yeah, that sounds good.
And all of a sudden the agents do everything else.
That sounds phenomenal to me.
What's interesting is They do all of this but they take into account a bunch of preferences priorities tone flexibility, and they take all these customized stuff and pick times on the calendar and get it all figured out for you.
And what's interesting is, BlockKit's agents can be invoked directly on email or Slack, and they're also trained on user-specific rules, like which meetings are movable, which ones are non-negotiable and even how to interrupt the urgency of an email based on its closing.
So all of that to me is very interesting.
They're already being used by more than 200 companies.
Brex Together, AI and a bunch of other top VC firms are using it.
I think this is a really good example of how AIs are actually going to save us time, reshape a lot of daily workflows.
And while they may not replace entire jobs today, entire roles completely today, I think they are speeding up and automating a lot of stuff that's happening.
That is all that I have for you today.
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