Anton Tonev, CFA

Building AI Agents for real estate at @WeAreRexera | Ex portfolio manager at AQR Capital | Enjoy picking low hanging 🍉🍒| Investing and building.

[Anton Tonev, CFA @adtonev]

@alphaticaio @michaeljburry I just can’t read this. Even if it is true it’s written in the most AI garbage way possible.

[Praveen Neppalli @praveenTweets]

Agentic AI adoption is on fire at @Uber, and it's changing the way we build, not just in engineering, but across the entire company.

Today, 99% of our engineers use AI tools. More than 70% of pull requests are attributed to local or cloud agents. And our engineers have built 2,500+ agent skills across the software development lifecycle.
Those numbers are exciting, but they led us to a much bigger question:

How do we bring agentic AI beyond engineering?

Finance. Legal. Operations. Marketing. Customer Support. HR. Procurement.

These functions run on complex workflows that are often manual, highly nuanced, and spread across dozens of systems. You can't automate them effectively by looking at process diagrams or documentation. You have to understand how the work actually gets done.

So we created something called Agentic Pods.

The idea is simple.

We handpicked ~30 of our most AI-proficient engineers (people with deep knowledge of Uber's systems) and paired each of them with a domain expert from a business function.

Then we gave every pod just two weeks.
• Days 1 – 2: Shadow the expert. Observe every step. Document workflows. Ask questions. Build intuition.
• Day 3: Prioritize opportunities based on scale, repetition, business impact, and data availability.
• Days 4 – 5: Build a working agent alongside the person doing the job.
• Days 6 – 9: Validate with several others performing the same work. Does it generalize? Does it actually make their job better?
• Day 10: Ship.

In just the past two months, we've run 16 Agentic Pods across 16 different business functions.
• Capital allocation across 150 cities: 15 hours → 30 minutes.
• Financial pacing reports: 2 days → 10 minutes.
• Marketing web quality assurance: 2 weeks → 50 minutes.
• Support workflow creation: 9,000 manual workflows → self-service automation.

The productivity gains are impressive, but what surprised us most wasn't the speed.
• It was how quickly engineers embedded in unfamiliar domains uncovered opportunities that had been hiding in plain sight.
• The biggest wins rarely come from automating one task. They come from rethinking an entire workflow. Once you redesign the workflow around AI, you often eliminate handoffs, remove unnecessary approvals, replace legacy tooling, reduce vendor spend, and dramatically accelerate decision-making.
• The workflow becomes the unit of automation - not the individual task.
• The most impactful agent skills cut across teams, orgs, functions, tools, and systems.

The biggest lesson? The best AI opportunities are rarely visible from the outside.

You discover them by sitting next to the people doing the work, understanding every friction point, and building with them, not for them.

We're now forming a dedicated team to scale this further and go deeper. They'll deeply understand the work, redesign it from the ground up, and use AI to fundamentally change how the business operates.

It's exciting times!

[Anton Tonev, CFA @adtonev]

@businessbarista Claude

[Anton Tonev, CFA @adtonev]

@choffstein I don't know private credit, but it's funny (sad) how the highlighting screwed the responses you got :D

[Anton Tonev, CFA @adtonev]

@KobeissiLetter @Kalshi One should always be careful when the YC uninverts

[Anton Tonev, CFA @adtonev]

@thePalenimbus @Cernovich @octal 100% right - tech is generally winning the talent war because pay and perks are way better than the majority of funds. In fact doing software engineering in most HF makes no sense if you are actually good.

[Anton Tonev, CFA @adtonev]

@drgurner @F1 His performance has been terrible, Max is not gonna stay forever, winning means money, solid support is Bottas except for the last race in Abu Dhabi, Checo is non factor, and many more reasons :)

[Anton Tonev, CFA @adtonev]

@drgurner @F1 Well they lost the constructors championship :)

[Steve Hou @stevehou]

Wow, what prescience! Lee wrote this essay in 2012, but everything he says here feels like it was said this year!

[delian @zebulgar]

USA USA USA

[Anton Tonev, CFA @adtonev]

@gregisenberg 100% wrong on 2. Out sales and CSM teams benefit tremendously from simple meeting recordings with notes. Extremely easy to share intel across the organization after a client call.

[Anton Tonev, CFA @adtonev]

@Austen I asked my personal trainer the same question a few months ago. The Norwegian method was the answer or a variation of it.

Curious what prompted the question?

[Anton Tonev, CFA @adtonev]

@berman66 ChatGPT does exactly the same and faster. How do you think this can evolve so that people prefer to use it through Zapier? At least for me the Google Doc last step is not a killer as I can copy past the table from ChatGPT.

Maybe direct integration with HubSpot-kind of thing?

[Aaron Levie @levie]

One of the biggest open questions with AI is its impact on software business models over time. What seems to be under-appreciated about AI is how it can enable significant TAM expansion for a large number of categories over time when software can deliver outcomes, and not just enable existing work.

Right now the dominant business model in SaaS is a per seat model, which inevitably means that the total number of seats you can sell is limited to the number of employees in the organization that are relevant for your particular software. Legal software is roughly capped by the size of the legal team, audit software is capped by the size of the audit team, and so on. The implication of this is that the customer generally *already* has to have not only a need for your solution, but also the existing headcount in the organization to become users of your software. Incidentally, this is often why so many SaaS products tend to go after horizontal productivity categories, because this maximizes the number of potential users you have access to in an organization.

AI flips this on its head, especially with the power of AI Agents, and you get a new form of “outcome-as-a-service”. When AI is actually doing the work within the software itself, you're no longer constrained by the number of employees inside the organization to use the actual software. The software is quite literally bringing along the work with it and delivering a particular business outcome. It's clear the full potential of this playing out is not fully understood, as this represents a massive transformation of software as an industry. 

When you are no longer constrained by the size of a team or department to use your software, markets are no longer arbitrarily capped in size. In this new era, software that powers a legal workflow actually brings the equivalent of legal knowledge work along with it, and software for audits brings the equivalent of audit work with it. All of a sudden small businesses, under-resourced teams in large enterprises, and all new geographies begin to open to up as markets. AI will enable otherwise niche categories of software to become much larger, and already large categories of software to become even bigger.

This transformation is similar to what we've seen in other markets where a new innovation has unlocked the size of a market well beyond its original demand. For instance, most investors and economists would have thought the size of Uber or Lyft's market was the size of the existing Taxi market, when in fact the market size was orders of magnitude larger once the shape of the product changed to make the offering easier to consume. We’ve seen this effect time and time again in areas like SaaS, cloud computing, a variety of mobile categories, and more.

We’re only in the earliest of stages of figuring out what this all means for the future of the software business model. Clearly all new variables of monetization will need to emerge when you start to pay software vendors for outcomes as opposed to just the software itself. But inevitably, when you remove the existing dominant constraint of enterprise software, TAM expansion will follow.

[Aaron Levie @levie]

Once a decade or so in tech we get an industry-realigning platform shift, and today we're fortunate enough to all be living through the AI wave. When these moments occur, you have a brief period of a few years where the winners and losers will emerge in almost every category, which means you're essentially signing up to be going 24/7 during this window.

When we first started Box right at the beginning of the cloud wave, for the first couple of years until we could at least feel like we had reached a point of "survival", we worked nonstop. Coding, selling, marketing, partnering, fundraising, hiring, pivoting, launching, everything. Sure, looking back we could have avoided 16-18 hour days by avoiding the time wasted on what would ultimately be useless work, bad decisions, or strategic misadventures. If we could go back and delete them from our calendars, we'd get all that time back.  

But, it's impossible to know in the moment which is the wasted time and effort. The good decisions came as a result of pivots from the bad ones, and the misadventures only taught us how to be smarter in the future. There's basically no way we could've "worked smarter not harder" and achieved any similar outcome. And perhaps most importantly, we had a blast doing it -- sure it was straining, but we certainly wouldn't have worked that hard if we weren't at least *mostly* having fun.

Now back to AI. There's probably more change happening at an even faster pace in AI than some of the biggest prior platform shifts like cloud and mobile. In comparison to cloud and mobile, there are billions of people that instantly can use AI so the markets are larger to start; there are more major platform providers competing for prime positions in the market; and there is a far higher rate of breakthroughs that advance the state of the art.

So when you're in the middle of one of these windows like we're in with AI, the only thing you can do is just crank. It will by no means ensure success, but it will definitely improve your odds, which generally are quite low to begin with. So take every advantage you can get. And you’re not having fun doing it, you probably shouldn’t!

[Clifford Asness @CliffordAsness]

Can’t wait for Tucker Carlson’s Christmas special “Live From the Wonderful Russian Prison System” https://t.co/tNup6Yr58I

[Clifford Asness @CliffordAsness]

Yes that’s exactly it. Too scholarly.

Not that it was a super-long unhinged revanchist evil fantasy.

The debasement of these RINOs (yes they are the RINOs) kissing the ass of our avowed enemy, a murdering evil maniac, is stomach turning.

“But but but Ukraine isn’t so great either.”

Save the bull**** response fool I said it for you.

[Clifford Asness @CliffordAsness]

Wow! The dictator, murderer, criminal, and avowed enemy of the USA is open to keeping his stolen gains.

This changes everything.

Republicans pretending to be tough guys in all things save where tough guys are needed are sickening.

[Clifford Asness @CliffordAsness]

Yes if only we were more like the KGB then we and not the USSR would’ve won the Cold War.

[Anton Tonev, CFA @adtonev]

@BrentBeshore @thogge Real friends and deal friends is a good way to put it too. Great article overall. https://t.co/2rGqYZwSNi