Robby Allen

The new barbell is to be purely human-maxxing or purely token-maxxing, but nothing in between. The between is the worst. Understanding and practicing this separation is not trivial, requires real reflection and purposeful reorganization of life, often going against deeply set habits. I’ve been going to the library in the morning for deep human work from 9am to 12pm (namely reading, thinking, and writing), then gym, then I try to be psychotically machinic from around 2pm to 5pm. I can’t always stick to this but I’m finding the more I separate the two states of mind with different physical spaces and times of day, the better things work and feel. If you’re trying to use AI for anything while you’re also trying to do anything meaningfully human, I have generally found that it’s a terribly confused and frustrating feeling and it produces poor, inefficient results on both.

@samdblond Boom! Congrats @samdblond

We're announcing our $50M Series B led by Benchmark. Founders Fund and Human Capital are tripling down in the round. We gave Benchmark a sneak preview of our upcoming GA release. They saw the future of sales. We had a term sheet 2 hours later. The future of sales is Monaco.

@vrexec When did you feel "post-economic" enough to make the move? And how do you balance the pull between builder and traveler?

@_RobbyAllen They’re both under eight… They go to local public schools… I think before their early teens

Ask me anything... I'm on a long walk American living in Europe (NL). From Jersey. Married w/ two kids Worked a lot of jobs in my life.. Wall Street, trading, data, consulting, marketing, construction, emergency medicine Work for myself now... consulting, advising, investing. Travel a lot Always trying to balance life and work... some stretches lean more one way or the other.

BREAKING: New poll shows @DanielLurie is the most popular American mayor. Key findings: - 74% of SF voters approve of him - Majorities support his handling of public safety, downtown revitalization, neighborhood cleanliness - He earns support from across the political spectrum

The only companies firing people b/c AI makes them so wildly productive also share these attributes: - over-hired during COVID - are market share losers - have giant capex spend What a delightfully curious coincidence

The NBA has declined more than any major sport in recent years. Analytics have effectively solved the game, driving a playing style that’s repetitive, less interesting to watch, and contributes to player injuries. By the end of the season, it’s a Hunger Games outcome (survival over winning) where the team with the uninjured players wins. Gambling influence and intentional team tanking have made the integrity of the sport increasingly questionable. These issues have been obvious for years, and management has done little to address them. Instead Adam Silver, the NBA commissioner, is rewarded with a board chair position at Duke - which underscores the lack of merit and competence behind a declining institution.

I've seen a few AI 'platform' RFPs from enterprises lately. Head scratcher. 3-4 month process. By then it's out of date — two or three model releases, products that didn't exist when they started. Then a 6-month rollout. Tells me who the winners and losers will be straight away.

If you feel like Anthropic is going after every enterprise software market and that the big SaaS enterprise platforms like Salesforce, ServiceNow and Workday are toast, you are wrong. This simplistic thinking fundamentally misunderstands the difference between an AI Agent and the Enterprise Platform. Let me explain: > An AI agent executes tasks. An enterprise platform defines, orchestrates, and gives the agent context to execute that task. > An AI agent has access. An enterprise platform governs agent permissions. > An AI agent can act. An enterprise platform can audit, control, and enforce. > An AI agent may go rogue. An enterprise platform guarantees compliance deterministically. > An AI agent is powerful in isolation. An enterprise platform is powerful in coordination across teams and business units. Furthermore, an enterprise platform can be multi-model, multi-cloud, and multi-integration. It is future proof for the customer in a dynamic market. CIOs buy Enterprise Platforms and will continue to do so, as long as those platform deeply integrate AI Agents within deterministic, governed, auditable, business processes.

@owroot Same here. No quick cuts, slow panning, long pauses with no words. Totally different product than the modern shows that are designed to jack your kid's dopamine through the roof.

My kids watch extremely minimal tv/movies but they have watched Little Bear and Kipper the Dog as we are closing in on the last hours of a long car trip. They are both from the 90s and they are so slow and relaxing. The VOs, the music, the animation - all of it is basically as gentle as you get in tv form. I believe a lot of kids shows are totally psychotic and make their brains more addled than they need to be. I don't feel that with Little Bear or Kipper the Dog though. They don't annoy me when I hear them. They feel like 1997 PBS and that's basically the least brain frying option and the best you can get.

Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise. Some quick takeaways: * Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow. * Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated. * Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs). * Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these. * Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs. * Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy. * Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems. * Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been. One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise. This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.

Land and expand

@knbrmurph watched Devers miss a hit and run call and bounce a ball off his glove at 1B on Sunday. He does not seem to be locked in.

wasting a one out triple by arreaz with strikeouts by chapman and devers is straight up bad ball

From The Score Will Take Care of Itself:

Mythos is very powerful, and should feel terrifying. I am proud of our approach to responsibly preview it with cyber defenders, rather than generally releasing it into the wild. Model card here: www-cdn.anthropic.com/53566bf5440a10…

@johncoogan @sama Congrats to the whole team!

TBPN has been acquired by OpenAI! The show is staying the same and we’ll continue to go live at 11am pacific every weekday. This is a full circle moment for me as I’ve worked with @sama for well over a decade. He funded my first company in 2013. Then helped us fix a serious logjam during a critical funding round a few years later. When I took my second company through YC, he was president at the time, and then when I joined Founders Fund, the first deal I saw in motion was the post-ChatGPT round in late 2022. And as we started growing TBPN last year, he was the very first lab lead to join the show. Thank you to everyone that has been a part of TBPN until now. The last year has been the most fun and rewarding part of my career and we’re excited to have more resources than ever going forward.

Just so everyone understands the accounting game in the AI/Cloud-space here is a very *simple* example: 1) Amazon “invests” $50B in OpenAI. Amazon records: - Cash $50B + Investment Asset $50B 2) OpenAI uses $50B to buy AWS services. Amazon records: + Sales $50B + Cash $50B Amazon is net $0 cash, but now has $50B in sales and $50B investment asset. Great deal! Thats why we see all the majors doing it, and at scale. It is a way to convert their dormant cash pile into revenue, which Wall Street loves. Most ppl dont realize what is happening. The revenue of these large tech companies is increasing dependent on them being able to self-fund their own sales.

Easiest business arbitrage: the risk/reward of a well-written cold email is so wildly favorable, and yet so few people will actually send one

Coatue just put a number on what we’ve been seeing at seed for 3 years. Software = $0.2T market. Services-as-software = $5.5T. 25x. The shift is from selling tools (per-seat) to selling work (per-output). This is why the best vertical AI companies don’t compete with software incumbents. They’re compete with expensive service providers, BPOs and high turnover labor. A $2K/month AI agent replacing an $80K/year agency is the new business model.