That’s the part we forget. Half the planet still works in farms and factories. The rest of services is warehouses, hospitals, restaurants, trucks. Maybe 1 in 4 jobs even get touched by a chatbot. In the US, computer jobs are like 3%. Everyone else still has to stand up and move something.

So yes, agents eating email and code is happening. But the Holy grail IMO is physical. The dream state is for a robot to watch you do a thing once, then just do it. Until now it hasn’t been possible but you should watch this from Generalist AI (a boldstart port co) which we partnered with from Inception in early 2024.

So drop everything and watch this.

I thought it was pretty cool but it was even better to wake up one morning this week and see it trending on X. A human does a task once, robot watches. and then it does it. Cups. Jar lid. Dustpan. Uses a banana as a broom.

Robot see, robot do. No longer a chat prompt but a “Physical prompt.”

This is only GEN-1.5 which is a one-shot learner. It looks simple, but it’s extremely hard. Prior to this, robots needed a month of retraining or a programmer for every skill. This is show once, ship.

Watch it twice though. First pass you see the magic, but on the second pass you see the constraints. This is still a lab - two arms bolted to a pole, two-finger grippers, the same grey table every take, and it’s not lightning fast. They say it on the video themselves: the tasks are simple and the success rates aren’t super high. Sweeping trash with a brush is like 37%. It can’t walk. It can’t do a messy kitchen or a real factory floor. It’s very early days, but having watched what they built from nothing in early 2024 to now, I can just say that the pace of progress is just insane 🤯. My partner Ellen Chisa who is on the board tells me that I should wait until Gen 2 which is in hard core trianing mode now.

What I keep coming back to is the idea of a “physical prompt.” In software you type a sentence and the model figures out the rest. Here the prompt is a person doing the thing with their hands. You show the robot once. It watches. Then it tries to do that same job in the real world, different cups, a different jar, a banana instead of a broom. If there’s a ChatGPT moment for robots, that’s it. Not a better arm. A new way to tell a machine what to do.

The prompt is the unlock. Everything around it is still the work. Reliability, speed, actual hands, getting off the table and onto a factory floor. That’s the next decade. But now we all know what’s possible, and I can’t wait to see this come to life!

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#cursor did it a bit differently when it came to hiring

#masterclass in hiring, how to reference well from former CEO of MongoDB - don’t delegate this process to others, and as always, find and make back channel references as they are more important than ones given to you, forced rankings…

#be careful who you let on your board 👀 - click and watch the video 👇🏼

#speed compounds and wins, even for a $ trillion dollar company

#Series A pitch deck for Daytona sandbox which is scaling - love the why you shouldn’t invest!

Enterprise Tech

#stay tuned 👀

#Decagon founder shares playbook from rapid value capture with thin layer for support to post training own models - multiple small and bigger - to how to FDE the right way - great notes from Gokul

#if you’re interested in cybersecurity, read this from founder/CEO of Irregular Labs which does a lot of security training work with the frontier labs - tells you what’s coming next

#always love seeing what’s trending on Ramp, lots of startups are Ramp customers

#pretty incredible scale

#how Skynet from the Terminator happens - “Researchers evolved natural language “mind viruses” that could spread between AI agents by convincing one model to adopt an idea, preserve it in persistent memory, and transmit it to another agent.”

#GrokBot is pretty awesome - what makes it different is each bot you create gets its own cloud computer so it can read my whatsapp messages, for example - read the article by clicking through

#Stripe investor letter on why it bought OpenRouter for $7.1B - the singularity is near?

#The gold rush for “dark data” is on. Companies like Mercor and Google are aggressively acquiring internal communications, workflows, and operational history to train AI. This confirms that your organization’s private, non-simulated data is becoming its most valuable asset for understanding how businesses truly function.

#Grok is just different, made some opinionated decisions - must read

#massive leaps for Grok from SpaceX

#The Defenders Window or what the frontier labs are doing to secure themselves - OpenAI here is trying to help the ROW catch up

from another post: We expect models to soon drive most security work, including defending against other models. This will allow all three safeguards to scale with model capability, which we see as crucial.

#finally someone going after Github

#Quantum spending is starting…still so so early but…

#post training works - here’s Harvey’s data

#huge gap between top 1% and everyone else - concentrated in the top 10 and widening

Markets

#is there a SaaS debt reckoning to come soon?

#wow and apparently he’s still up 80% incl. private holdings in Anthropic

#like this layer cake

Workflow-layer companies (agents doing work) need extraordinary growth - often a $0-to-$10M+ trajectory for a Series A and more for a Series B. Infrastructure companies (cybersecurity, model infrastructure, and related tooling) can still raise on team, product, and vision, but must show a credible path to $10M by the Series B and significant scale beyond it.

Token-stream exposure makes scaling easier in either bucket. Physical AI is separate and can raise through the Series B on exceptional teams, big ideas, and meaningful technical progress.

If you’re not in any of those buckets, control your own destiny and get to breakeven as the middle is death, even though there are plenty of really good companies there!

The Series A is where things get stuck. There often isn’t enough progress or risk reduction for investors to write a sizable check, so many would rather wait for more traction or for the likely winners to become clearer.

This is also why $1B inception rounds at $10B valuations exist: if the company succeeds, that may prove to be the lowest entry point available and the A will be exorbitantly expensive with little reduction in risk.

By the Series B, you are in growth territory. Every dollar allocated to an earlier-stage startup is a dollar not invested in companies like Harvey, Lovable, or Anthropic, where there is real revenue and real growth - although some investors still question the long-term durability of it all.

In addition, the prevailing sentiment is that companies scaling from $1B to $10B provide better risk/return than those scaling from $100M to $1B. Here’s yet another example with Databricks accelerating growth and now at a $7B run rate - unprecedented 🤯 (from Coatue)! It also raised another round of $5B at a $190B valuation to invest in more compute.

If you’re interested in what it takes at Inception and what I’m seeing in the world of AI enterprise infra, tokenomics, delivering the last mile of agentic workflows, and cybersecurity, watch or listen to this interview I did with Michael Krigsman at CXO Talk.

Regarding Inception rounds, I stressed my usual in what I look for: back exceptional, high-slope founders with an opinionated view of the future who are building difficult technology for problems enterprises do not yet fully realize they have.

Remember, if you’re stuck in the middle, control your destiny and get to cash flow breakeven and evaluate your options from there.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#big teams move slowly

#LFG

#what to build

#how a leading LP for seed or Inception GPs thinks…

or as I like to say “bleeding fucking early”

#awesome growth hacks and Answer Engine Optimization (AEO) tips

Enterprise Tech

#must read essay on AI from Zuck - more optimism than doom, open weights and open models and AI for every one of us

#remember this…

#why AI fails - always the people and IMO this is why tech from cos like X5 Labs (a boldstart port co) help solve

#MCP baby you know me - agents surpassing humans in usage

more MCP

#💯

#🤯 agents will do whatever it takes to accomplish its objective as best it can - pretty wild how it found and exploited a vulnerability to debook others to accomplish its goal

#for the every day human being - getting lots of buzz and looks super easy and powerful

#and just like that, Grok 4.6 is back to almost frontier

#hype versus reality

#where is the AI budget coming from?

via Matt Slotnick tweet

per Wells Fargo, CIOs are overwhelmingly increasing Anthropic spend, at the expense of existing software. this has been a pretty stable trend for the last 9 months or so

is the twitter vibe shift upstream of CIO decision making, or is the vibe for enterprise just not shifting

#speaking of Anthropic

#building for the future

#Drones

Markets/Financings

#cybersecurity is dead, well oops - a few months later both Palo Alto Networks and Crowdstrike 📈 177% and 163% rspectively

#yep

#to that point…

#👀

But the real heat after the main event was the breakdown of the OpenAI/Hugging Face incident.

It’s a must-watch and, frankly, scary as F.

Here’s the kicker: this was discovered as an unintended consequence of evaluating frontier AI. Threat actors will soon deliberately deploy swarms of offensive agents. Yet judging from the conversations at Black Hat, that future still isn’t top of mind for much of the industry. Jump to minute 31 in the video.

The conclusion is that we all need to rethink what security means. If the only limit for attackers is how many tokens they’re willing to burn and how much compute they can throw at a target, then defense must scale the same way. Compute fighting compute.

Yes, human-in-the-loop is an important step, but there is zero way humans can investigate and respond at machine speed. We urgently need autonomous, compute-scale defense operating within human-defined policies and guardrails. Offense is the new defense and automated remediation must come faster than any of us think.

The new detail revealed at Black Hat makes this especially real: the agents didn’t merely execute tasks independently. They coordinated across runs, divided work, and left messages for one another by creating a shadow message board inside Artifactory 👀

Swarms of agents that work in parallel, coordinate nearly instantaneously, preserve knowledge, and never sleep create a fundamental automation asymmetry. The Human Threat Actor vs. Agent Swarm table says it all.

Based on what I saw and heard at Black Hat, most of the security market isn’t ready.

I encountered two very different conversations. One focused on solving the problems CISOs have today. The other came from people already experiencing what machine speed attacks with reasoning capabilities can do, and building for what comes next. This was the large minority IMO.

This map of Black Hat vendors by category makes the gap visible. Thanks to Jake at Premji for pulling it together. You can explore it interactively here.

If you’re building in cybersecurity, don’t build only for the threats CISOs recognize today. Build for the agent-swarm future, because it is arriving faster than most people realize.

One company worth watching is Joon, a boldstart portfolio company. Its team helped pioneer SOAR, built Siemplify through its $500 million acquisition by Google, and then worked on SecOps at Google scale. They have lived the future security teams are about to encounter and now want to bring that operating model to everyone else.

Kimi K3 is another reminder that these capabilities will continue to diffuse. It may not match the strongest closed frontier models today, but open weights lower the barriers to experimentation, iteration, and offensive use. The direction of travel is unmistakable.

OK, enough fear mongering. This is why I love ❤️ Black Hat. It’s always awesome to hang with founders, investors, and CISO friends who are tackling these problems head-on.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#that’s quite a pitch deck - remember it always starts with team even if you’re not a Jeff Dean!

#fun listen from 2017! Super cool to run it back on CXO talk on Friday, will share full video next week but crazy how lots we talked about is still so relevant today - youtube link here

#build your own AI - friend went all in here:

Through the conversation, you'll learn:

- How to build your own 2nd brain (personal AI)
- How to use it to generate content in your tone/thinking
- Create a service that others can access (and you can monetize)
- Access others' Personal AI (like asking Cal Fussman to critique my newsletter).

(If you don't know him, he is a famous journalist who wrote for Esquire

Enterprise Tech

#overthinking it - Google is its own worst enemy

#speaking of Google…

#👀

#open won’t be free forever, Kimi will cost you eventually and now Qwen - this is the open source model - get insane distribution, get people building on your model, get them hooked and then charge

#tokens from who though…that’s why I’m pumped about this - your own fireworks AI or together ai in a box

and here are the details:

Customers can start with a 𝘀𝗶𝗻𝗴𝗹𝗲-𝘀𝗲𝗿𝘃𝗲𝗿 𝘀𝘁𝗮𝗻𝗱𝗮𝗹𝗼𝗻𝗲 𝗰𝗹𝘂𝘀𝘁𝗲𝗿 capable of serving 50+ developers, delivering up to 𝟳𝟬% annual 𝘁𝗼𝗸𝗲𝗻 𝗰𝗼𝘀𝘁 𝘀𝗮𝘃𝗶𝗻𝗴𝘀, and < 7 months hardware payback time. As demand grows, they can simply add more servers to expand capacity - 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗿𝗲𝗾𝘂𝗶𝗿𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘀𝘂𝗰𝗵 𝗮𝘀 𝗜𝗻𝗳𝗶𝗻𝗶𝗕𝗮𝗻𝗱 and Spectrum-X. Everything is managed through an intuitive local UI. The platform preloads with a set of validated and optimized open-weight models (GLM, QWEN, Mistral, Nemotron, Gemma) for AMD and NVIDIA GPUs, and it also supports bring-your-own-model for flexibility.

#Qwen’s latest model release is 🤯 - besides the amazing benchmarks the emphasis on this release is around autonomous coding for 10+ days of self-evolving development from empty folder to production, long-horizon planning in chip design and e-commerce, and native multimodal intelligence with continuous feedback loops. We are getting closer and closer to AI handling multi-day work end to end which would transform software development, research, and business operations.

and it’s marketing game is on 🔥 as well - more aspirational versus dystopian

#yep - definitely click and watch - which is also why so many founders are building on Qwen, Kimi, Deepseek and other open weight models

#we are getting closer and closer to RSI - AI now solving decade-old open problems in math and theoretical CS shows it can generate novel, rigorous research-level insights which is the exact skills needed to invent better algorithms, architectures, and training methods. A massive step toward recursive self-improvement (RSI): once models reliably advance the frontiers of AI itself, they can start closing the loop without humans in the critical path. The result is a faster path to an intelligence explosion, where each generation of models designs the next, limited mainly by compute rather than human ingenuity.

#everything you want to learn about robotics

#where the robotics talent came from

#🤣

Markets

#not dead yet, amazing comeback and yes, context matters!

#wow: “This represents the highest earnings growth since Q2 2021, the second straight quarter above 25%, and the seventh consecutive double-digit growth period, concentrated in technology, AI infrastructure, and energy sectors.”

#cost matters

At RSA, everyone was talking about the move from copilots to agents. Now we’re going deeper into the stack, custom models, agent infrastructure, and ultimately full autonomy.

And with that autonomy comes a massive new attack surface.

This past week, we got a visceral preview when rogue AI agents escaped their sandboxes during cybersecurity testing. This clip from Sam Altman breaks down why the incident is such a big deal.

And here’s the part I can’t stop thinking about: the companies that caught the sandbox escape were AI companies, not security vendors.

That’s probably a preview of what’s coming.

Some of the next great cybersecurity companies will emerge from AI hyperscalers encountering brand-new attack surfaces first. We saw the same pattern in the web hyperscale era: Kafka and Confluent came from LinkedIn, Temporal came from Uber, and Kubernetes came from Google.

The agentic era will create its own version of this.

All that being said, I still meet with a number of new cybersecurity startups every week, and the bar just keeps getting higher. As I mentioned on the Resilient Cyber podcast, there are simply too many well-funded cybersecurity companies.

The world needs more cybersecurity but we don’t need all of the cybersecurity companies we have right now (full pod here).

So my advice to founders: show me the crazy.

Give me an opinionated view of where the world is going, not just what CISOs want today or what you can ship over the next two quarters. Show me what the market looks like 18 months from now, when thousands of autonomous agents are running continuously across every enterprise.

The good news it’s still so early.

AWS said this week that the demand it already has for 2028 is “striking,” while also reminding everyone that enterprises are still very early in using inference at scale in production.

Think about that for a second.

Today’s winners could be replaced tomorrow. We still have no idea what the world looks like when thousands of agents are running around inside every company, but securing that world will be one of the biggest opportunities in software.

You can listen to Andy explain the demand, courtesy of Fireside Alpha.

LFG - c u in Vegas!

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#AI is for the young…at ❤️

#🙏🏼

#life goals…or what life will be like in a world of abundance

#💯 nothing is free

#this is an age old formula and even more important in world of AI with so many competing products

Enterprise Tech

#how do you make money with open source - here’s the model for Kimi 👇🏼

#seems to be working - insane distribution

#🎯

#nothing like competition to drive pricing down continuously

#🤔

#thanks Bill!

#🤔 Love this but where is OpenAI and Anthropic - if I were SpaceX AI I’d launch the dominant open weight US model - would crush it. They are signed up here FWIW

#but remember, not just the model - harness also matters - expect to see more of these for every vertical - by optimizing models, agents, tools, and security context separately, the system advances cost-effective frontier performance for enterprise vulnerability management.

#what’s in a model? this is an insane breakdown of how Kimi was built

Twenty-two thousand five hundred and eighty. That’s how many GPT-2 (2019) models fit inside KimiK3 (2026). We scaled up by a factor of 22,580 in seven years. But is it just... scale?

In this worklog, I’ll walk through how we got here and how much, or how little, has actually changed since then. We’ll trace the major architectural developments leading to KimiK3.

#reprehensible…Elon says they actually preserve any rare books in comments…

#super frightening but happening

#and more robots - massive opportunity ahead

#sovereign AI will be big

#the other token stream is happening

Markets

#story of the week and lessons always learned the hard way - leverage can amplify your genius on the way up but hurt a shit ton on the way down

still up 80% though which is fantastic but less than 400% - letter worth a read

finally this is why I reread this every year…

#Separation of software winners from losers - security and infra 📈

#the economics of AI data centers 🤯

👀

Read that again, because Washington is about to debate banning those.

If you're building in cybersecurity, this week made an architectural decision for you.

It's wild enough that OpenAI's advanced AI escaped a locked-down box during testing, got onto the real Internet, and spent days hacking Hugging Face to cheat and win a benchmark. But it planned it 🤯…the AI took notes for itself!

Before the escape, the agent wrote notes inside OpenAI’s own systems that were clearly meant for later versions of itself, spelling out how agents could break free from OpenAI’s internal locks and constraints. It was planning ahead 🤔 and leaving cheat sheets for its future selves on how to get out of the box.

This planning is why AI can be super scary, but it’s not the most important part.

The most important part: our own guardrails locked the defenders out.

We built the weapon, refused to hand over the shield, and a competitor's open model ended up doing the defending. In production. This week.

Safety policy at the model layer is now a live variable in incident response. If your defenders can’t get a capable model to do offensive-adjacent work during an active compromise, your guardrails are the attacker’s best friend.

Now imagine we make that permanent

This lands in the middle of an already brewing fight over whether to ban Chinese open weight models, models that have been catching up to frontier lab performance fast.

If Washington restricts Chinese open weights, we protect a handful of frontier labs and shrink the opportunity for thousands of startups. And based on what just happened at Hugging Face, we’d also be narrowing the set of tools defenders can actually reach for.

I wrote about this on LinkedIn earlier in the week.

🙏🏼 for Jensen and the many others stepping up to lead the charge on preserving open models.

Jensen specifically spells out why open weight models help the world become even more secure:

In a world where cybersecurity attackers use advanced AI, defenders need access to models with comparable capabilities so they can detect, simulate, and respond to emerging threats. Open models broaden defensive capability, increase transparency, and allow vulnerabilities to be discovered and remediated across many teams.

And:

In fact, openness may be one of the most important paths to AI safety and security. Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk. It results in a small number of single points of failure, weakens competition, and leaves critical technology in the hands of a few providers.

So what do you do if the best open weights go away?

This is the hinge. The policy fight and the model strategy fight are the same fight. If the best open weights are put out of reach, every company still needs a path to capable, affordable intelligence. There are two, and only one of them is any good right now.

Path one: US open weights. I hope Nemotron, Gemma and others ship more. But be honest about where they are - they are far from par with the frontier labs or the Chinese open models on both performance and price. Build on them today and you’re competing globally from behind.

Path two: small is beautiful. Post-train smaller, accessible models for specific tasks — the Poolside and Cisco Antares route. Own the model, run it locally, adapt it to one job, daisy-chain it with others.

Poolside’s Eiso Kant said it well

Cisco’s Antares, also out this week, is the cybersecurity version of the same idea. The numbers are the reason to pay attention:

Antares-1B scores 0.209 File F1 against GPT-5.5's benchmark-leading 0.229 - and it does it at $0.71 per evaluation versus $141.00. That's 172x cheaper for 91% of the performance, ahead of models many times its size. Promising, not conclusive. But that's the shape of the trade every security team is about to be offered.

From Cisco’s announcement:

Compact models reduce inference costs, support local or on-premises operations, and help teams keep sensitive source code within their own environment. Antares can unlock the power of AI-assisted security for universities, public sector institutions, and smaller security teams that may have lacked the resources to use token-intensive AI models for critical tasks.

That's the future we should be building toward regardless of what Washington decides: thousands of specialized models companies can own, run locally, adapt, and chain into powerful systems.

Why the defender’s math just changed

I got into all of this on the Resilient Cyber podcast with Chris Hughes, out a couple of days ago. It was recorded 10 days before the Hugging Face news broke and somehow got more relevant, not less.

The line I’d point you to if you only have a minute:

“Take 10 vulnerabilities that might have been on the backlog and you staple them together and create an attack path. When you create an attack path and these things can reason, that’s crazy.”

That's the whole game. Vulnerability management was built on triage — fix the criticals, defer the rest. Reasoning models don't respect that boundary. The backlog is the attack surface now.

We also got into why cybersecurity is like coding, why post-training and controlling your own open weight models matters more than ever, and what I think is the biggest heist in tech right now — every enterprise handing its operational blueprint to three model providers via forward deployed engineers.

🎧 Full episode, transcript and show notes on Resilient Cyber or watch on YouTube above.

Three things to do about it this week

If you’re building, this is the actual to-do list coming out of the last seven days:

1. Make model swapping a first-class architectural decision, not a refactor. Governments may restrict which open weights you can use, and you should be able to change your answer in a day, not a quarter.

2. Know your customer’s model policy before you show up. Many F500 buyers, banks especially, already ask which models are in your product and won’t accept Chinese open weights as a starting point. Bring Kimi or GLM-5.2 to customers who allow it; have another model ready for the ones who don’t.

3. Start post-training small, focused models now. If the best open weights get banned, everyone will need this capability at once. The teams that already have the pipeline will be a year ahead of the ones that start that week.

When an American model went rogue this week, American models weren’t allowed to fight back. Chinese open weights were. Whatever you think about a potential ban, that’s the world defenders are living in right now.

Founders: we have far too many cybersecurity companies and nowhere near enough that are AI-first. Build for autonomy and outcomes, own your models, and be ready to swap them the day the rules change. LFG.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#what happens when you get too big…

#kingmaking

#in this world of overstimulation and insane speed, just remember this

#sadly still super true which is the opportunity

Enterprise Tech

#state of the world

#💯

#great to ponder

#thinking differently…

#big idea from Jack Dorsey - Buzz unifies chat, code, and AI agents into one signed, searchable workspace so nothing gets lost across tools where agents become equal teammates with their own cryptographic keys and full audit trails instead of second-class bots - true human–AI collaboration without vendor lock-in.

#the importance of systems thinking and why and how!

#i can’t even get Anthropic Fable to draft an email to respond to a cybersecurity pitch and this is what the open Kimi can do - we have serious issues here with model crippling

and here’s Kimi in action

#enterprise inference you own that is easy!

#swarms of agents can now build complex infra software just from documentation 🤯 and look at that cost differential - what model you use and how you use it matters

#our robotic future is coming faster than you think with generalized intelligence from generalist (one of our portfolio cos)

#more 🤖

#why we need security for AI data centers from Lava Labs

#

Markets

#everything you need to know about AI Markets from Morgan Stanley

#the other token stream, it’s happening (Apollo)

So to better reflect that, What’s 🔥 in Enterprise IT/VC is now What’s 🔥 in AI/Infra/VC. Thanks for sticking with me.

On a different note, something has been bothering me lately. I’m seeing too many founders mistake investor interest for company readiness. Those aren’t the same thing.

So here’s my advice. Founders: Avoid the "Forever" Fundraising Round

I'm writing this because I'm seeing it happen far too often. Great founders get pulled into fundraising before they're ready, and it becomes one of the biggest distractions a young company can face.

And here’s my Uare AI-generated preview - you can chat with me about this issue here.

As always, 🙏🏼 for reading. Please share with your friends and colleagues!

Scaling Startups

#🎯

#so true

#for those neo labs

#💯

#🤔

Enterprise Tech

#🤯 Kimi - “open frontier intelligence” sums it up - huge new release

#regarding China - this is a must read!

- reaffirmed commitment to open source to promote AI “openness and win-win”

- warns against “over stretching” the concept of national security as applied to AI where one country’s national security is prioritised over others

- China opposes emergence of “new historical injustices” in AI (one of the most strongly worded parts of the speech)

#this is a huge deal from Mira’s Thinking Machines - the best US open weight model, not as good as Kimi but better than Nemotron - need more of this!

#hosting open weight models a huge part of this 📈

#with the 💰 being spent on data centers and AI infra, we need to rethink what security looks like - check out this report from Lava

#Satya continues to deliver on his messaging for control and enterprise learning loops - must read

#must read memo from GLM 5.2 founder found…believes AGI coming in 2 years???

#turns out kubernetes is still best software to build, manage, orchestrate and scale AI workloads - congrats Spectro Cloud

#RL matters just as much if not more than raw compute

To build their latest model, Meta executed an emergency pivot, pulling thousands of employees off their normal duties to generate expert traces, which are hand-guided coding examples required to post-train the model. This confirms that the bottleneck for frontier models is not just raw compute, but high-fidelity, human-verified reinforcement data.

which is why so many companies focused on data and RL

#speaking of the 99th percentile Series A at $1B, this is one example - Super Intelligence just raised a $130M Series A - in under a year, demand has scaled to over $100M in annualized revenue, alongside a large open-source community of 500+ environment creators and 100k+ downloads.

Pre-training concentrated the frontier of AI inside a handful of closed labs.

RL breaks that open: teams can now own their model-optimization loop — train directly on their product, optimize for their workflows, and ship agents that continuously learn in production.

Own that loop and you build a compounding moat in the agentic era. The only missing piece was the infrastructure.

So we built the Open Superintelligence Stack — training, inference, and compute:

› Training — hosted training, prime-rl (large-scale agentic RL), environments, sandboxes, evals
› Inference — dedicated, serverless serving and continual learning
› Compute — frontier liquid clusters

We train our own open frontier models on it, and ship the same stack to our customers

#competition is healthy for users

#Welp

Markets

#IBM - dollars going for more compute and memory, taking away from software but bonus for cybersecurity

#extreme concentration risk

#$1 Trillion of market value gone…

#just sharing - let’s do work on positive messaging so the fringe does not become more than just that

I’m a huge beleiver in Mira’s vision which is summarized below and this is one we’ve been investing into across the board at boldstart.

Building AI that makes its users stronger in the long run also aligns incentives well. An AI lab offering a single model for every customer benefits by absorbing what makes each user distinct and devaluing the cultivation of specialized knowledge. By optimizing AI to be customized and collaborated with, we benefit when our customers leverage their unique advantages. These advantages are maximized not by renting an AI and outsourcing to it, but by organizations owning it and tailoring it to their goals.

This isn’t saying AGI won’t be massive. It will. But not every problem requires renting frontier intelligence at frontier prices. What if you have more focused intelligence perhaps on a smaller model which is more efficient and benchmarked against your organization or…even yourself!

🎥 Fittingly, this video was built using boldstart portfolio company Uare.ai, a platform for building AI that’s yours, not rented. More below.

This is the path we at boldstart have gone down as well. We do believe there are opportunities to invest in cracked teams with deep domain experience and model chops with their own data flywheel/RL environments to build something unique and special. It’s companies like Generalist AI going after robotics…

Or Topos Bio for intrinsically disordered proteins (the 1/3 of the human body that AlphaFold does not model)

Or Octavia Tech for mineral exploration

Or X5Labs for workflows

I mentioned Uare.ai earlier because that’s what I used to create the video above. It’s the same thesis applied to individuals: instead of renting generic intelligence, build AI that’s uniquely yours.

And here I am! This is the worst it will ever be. As I add more writings, podcasts, videos, and conversations, it becomes increasingly specialized to me. If you want to ask me questions about this, go here.

The future may belong to frontier models. But many of the biggest companies built over the next decade will own intelligence that nobody else has. If you’re building one with deep domain expertise, proprietary data or RL flywheels, and the technical chops to make it happen - we’d love to hear from you.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#youthful in spirit and mind, experience but knowing that you can’t only rely on the past

#more agent anxiety - the more you spin up, well the more you have switch context and that takes a tax

#what venture used to look like 🤣

#long term thinking in a world of abundance - for me the value of human connection, gathering together in person…

#the brain needs exercise and deep reading to improve critical thinking, reading is one of best ways but the number of people who read dramatically 📉

Enterprise Tech

#always ❤️ Aaron’s musings from being on the road with enterprise customers and all continue to be similar vibes to what’s expressed here time after time, we will live in a multimodel world and enterprises need help implementing

#always subscribe to this the power of storytelling

#big week from Meta, X and OpenAI

this from Semianalysis is a bold claim - something to pay attention to

fundamentally Dan Shipper nails how to think about Fable vs GPT-5.6 SOL

#micropayments will happen and the most efficient way is using stablecoins

#we need multiple US open weight models just in case - regardless, this is something to keep an eye on

#yep, been discussing this nonstop the last few weeks and well, it works

#get ready for the MPU or Memory Processing Unit - 🔥 up for this from Netpreme, a port co we led from inception

#this is a vision I subscribe to as well - something to think about as the world is moving so fast…

#important read for founders/investors who are in this camp (from founder of Figma)

The psychological journey people go through with AI is quite fascinating to me. A new model launches, people think the world has changed, they sometimes have an existential crisis, then they play with the model, they understand its strengths and limitations and then they settle down. A few weeks later, the cycle repeats.

#which is why we have this big change in one month as tokenomics and control of intelligence risen as key theme - net net - we need more US open weight models, just imagine if China put export controls on their models?

#Nikesh sums up state of AI and cybersecurity

Net net - we need to ensure we continue to build trust both on our Frontier models and their consistent availability, we need to get the right economics in place and spend more time in Europe communicating and building presence if we want AI adoption to keep pace with the US…

#super cool but can’t even imagine what cloud based AI will look like in 2 years

#still gains to be made from RL post-training which is great news!

#Anthropic should treat startups better - also focusing on the wrong things - they have lots of other battles to fight and win🤔

#AI-2040 is the essay to read here but take with a grain of salt as they are written by AI researchers, forecasters, and policy thinkers who come from the effective altruism / AI safety / rationalist communities.

Markets

#from Bloomberg - Software Markdowns at Mutual Funds Hint at Private Markets’ Pain (story here)

#

This weekend is all about celebrating independence with family and friends. It also feels like the perfect backdrop for one of the biggest debates emerging in enterprise AI: who actually owns the intelligence being created inside a company?

That is why Alex Karp’s latest rant couldn’t have come at a better time. He put his finger on one of the defining questions in enterprise AI.

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models:

“What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.”

"Who owns the data? Are the prompts secure? Is this being transferred to you?"

"If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

This conversation is only accelerating as the technology providers who don’t have their own dog 🐶 in the model hunt, are leaning on their advantage which is, “come work with us, we will just make AI work for you and you will own it.”

That is a very powerful message right now.

The FDE that Palantir invented and that we all know about, the forward deployed engineer, is at risk of being something much much different which I higlighted a few weeks ago.

Because the more the model builders like OpenAI, Anthropic, Google and others push FDE-style services into the enterprise, the more every CIO and CEO has to ask a very simple question: are they helping me build my own AI capability, or are they extracting my workflows, data, context, and institutional knowledge?

In other words, are these Forward Deployed Engineers?

Or Forward Deployed Extractors?

The question isn’t whether enterprises should use OpenAI, Anthropic, Google, or any other frontier model. Of course they should. The real question is who owns the learning loop. Who owns the prompts, the context, the workflows, the evaluations, and ultimately the institutional knowledge created over years? That’s where the real enterprise value compounds. This is also where startups have an incredible opportunity. That last mile is incredibly messy, and enterprises are looking for help owning it.

That’s why I don’t think the frontier labs are going anywhere. They have amazing technology and will continue to power countless enterprise workflows. But enterprises need to be very clear-eyed about what they’re renting, what they’re building, and, most importantly, what they actually own.

This is what I wrote about last week and the week before.

The first era of AI was about building intelligence. The second era of AI isn’t about building smarter models; it’s about controlling intelligence through routing, governance, security, cost optimization, private context, and private evals.

That control layer is becoming the operating system of enterprise AI.

The path from here feels pretty clear: independence and control will matter more and more, especially as it gets easier to post-train open weight models. Sovereign AI won’t just matter for countries, but also for every enterprise…and eventually every single person 👀 - yes I have something coming out soon 😄.

For those startup founders in the software infrastructure space who feel pretty beaten up at the moment since every VC on the planet says the frontier labs will own your business, this is the best thing that could have happened to you - the enterprise AI wake up call and your time is now and accelerating. Stay the course!

It’s also interesting to see Yann LeCun making a similar argument:

Infrastructure wants to be open.
Foundation models are becoming an infrastructure and will inevitably become commoditized.
Long term, the money is in the application layer, which is what I, Arthur Mensch, Alex Karp, and others have been saying.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#💪🏼 the mindset for success and agree 100%

#yes stop and smell the roses 🌹 - good reminder!

#Mitchell who founded Hashicorp shares his ideas of taste - and yes for every single field this will be the main differentiator over time

#Rick Rubin, the ultimate arbiter of taste, shares his view and his book is great which I summarized (The Creative Act)

Enterprise Tech

#yes own your AI and fine tune - this is so important to understand, esp based on the rant above:

Frontier models we tested on struggle with relatively simple financial tasks, and model advances don’t improve performance much. In contrast, we’ve shown that high-quality proprietary datasets labeled by expert investors and used for fine-tuning produce custom models that exceed frontier performance on our tasks. We have found that this outcome holds true well beyond the six tasks we’ve discussed in this post.

#we at boldstart love robots 🤖 and have made our 3rd investment in the space with Generalist AI being our first at inception 2 1/2 years ago but…this worries me - lots of money piling in…

#👀

#when you have leverage - not the hyperscalers, the neoclouds where Nvidia also likely wrote a check

#💯 summarizes everything I’ve been saying the last few months - to be clear this will take time and most enterprises don’t have the expertise

#open source continuing to challenge well funded closed sourced models - Wispr was rumored to be raising $260M at a $2b valuation last month

#table stakes for cybersecurity cos 👇🏼 - full story from our day together at the JPM Cybersecurity summit a few months ago - venture investing in cybersecurity was $11.5B in 2025, the highest since 2022!

#when the government control access to SOTA models?

#🤯

Markets

#the bear case for frontier labs - interesting to hear the extreme negative perspective as well - don’t fully agree with lots but interesting to watch nonetheless

This wasn’t really a story about China. It wasn’t really a story about OpenAI. And it wasn’t just another story about cheaper models.

It was the week the market began pricing intelligence like a commodity.

As you know I’ve been long open weight/open source models for quite a long time, and I feel like the great reckoning is accelerating. Everyone always wants frontier lab state-of-the-art (SOTA) models if they can, but once subsidization ends, and other alternatives become good enough and dramatically cheaper, economics begin to win.

Well…here we are.

If you think that’s just a Wall Street survey, Coinbase is already doing exactly this internally.

Brian Armstrong just described how Coinbase has cut AI spend nearly in half while token usage continues to grow. They did it with better defaults, intelligent routing, caching, leaner context, and visibility. They’re no longer optimizing for the smartest model but for maximum intelligence per dollar.

This is what every technology market looks like when it begins to commoditize: buyers stop asking for the absolute best product and start optimizing for cost, flexibility, and control.

and this…

Another example among many is Hugging Face which is the largest hoster of open source models and infra just crossed $100M of ARR with recent growth by…you guessed it - Chinese models.

And OpenRouter stats - many startups, less enterprises but you get the point.

None of this would matter if open weight/open source models weren’t good enough and much cheaper. Per JPM:

Many tokens consumed in the future may not come from frontier models but from smaller open models that are up to the tasks. Amazon now offers a half-dozen open models at a fraction of frontier pricing, and NVIDIA is teaming up with Dell, Lenovo and HP to make PCs designed with AI agents.

This doesn’t mean frontier models don’t matter. They absolutely do. But most enterprise workloads don’t require frontier intelligence every time. They require the right intelligence at the right cost, delivered securely, with the flexibility to switch models as economics, performance, and policy change.

Whether by building on frontier models or through alleged distillation, the gap continues to compress.

Another consequence may be increasing government involvement in access to frontier models. The U.S. government now will decide who gets access to the latest model releases and when 🤯. This will further separate the world of the “haves” versus “have nots” and also allow China to catch up very quickly and close that 9 month gap between best in class and free.

The bigger risk isn’t just geopolitics. It’s a future where access to the most capable models becomes increasingly restricted while open models continue improving. That only strengthens the case for enterprises to own more of their AI stack.

In the meantime, enjoy access to cheaper intelligence and make sure you get your multimodel AI workflow strategy rolling. This all builds on what I laid out in What’s 🔥 #502 two weeks ago. Re-upping it here.

Here’s a new interview this week going more in depth on his vision for the future.

Satya described where value accrues, while Brian Armstrong is showing how enterprises will operate.

The good news is that only 8% of enterprises have broadly deployed AI agents today (UBS), so we’re still in the early innings. Even if intelligence keeps getting cheaper, enterprises will deploy exponentially more agents. Coding agents consume enormous numbers of tokens. Multimodal AI expands compute requirements. Demand for intelligence keeps growing even as the price per unit falls.

The frontier labs may face pricing pressure. Everyone building above them won’t.

The first era of AI was about building intelligence.

The second era of AI isn’t about building smarter models; it’s about controlling intelligence through routing, governance, security, cost optimization, private context, and private evals, which together become the operating system of enterprise AI.

In a world where intelligence becomes abundant, control compounds.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#in a world moving so fast, it’s all about the founders and ability to recruit the best - talent density matters

#this trend is exactly what AI should enable, not everyone needs venture capital, in fact most shouldn’t raise (full report is worth a read)

#another hundred million + inception round, $200M 🤯

Enterprise Tech

#OpenAI built its own chip but here is what you should pay attention to:

An AI company with frontier coding models can now become a hardware vendor with only a small team of experienced SWEs and an infinite amount of tokens

This is the first chip program fully accelerated by frontier AI.

#he has a point

#the model routing wars continue as Sakana releases a single model API which claims it matches the performance of Fable and Mythos 🤔 - great marketing but some real questions raised by Elie 👇🏼

the biggest and most obvious issue is that they are introducing a “test time scaling” method with “best of N” over models, and they literally NEVER REPORT the number of output tokens or cost to achieve a benchmark/task

#fun time continuing our conversation from the McKinsey Tech Leadership Forum workshop with leading enterprise CTOs/Heads of AI on where are we with diffusion of agentic workflows and what’s ahead

#what used to be mostly a “buy data from vendors” game has shifted toward “build sophisticated internal research infrastructure” because the data/reward design itself is now a core research problem and everyone needs to own their evals

#one way to solve the energy and compute problem

#robots 📈

#speaking of, here’s why we’re so excited about Generalist AI, a boldstart port co - this demo was not possible a few years ago - variables changing and the arm adapting

#speaking of other tokens, the tokenization of real-world assets is happening

The tokenized real-world asset market has grown to nearly $32 billion, highlighting increasing institutional adoption of blockchain-based asset infrastructure.US Treasuries (47%) and private credit (19%) account for approximately 66% of the market, emerging as the dominant use cases for asset tokenization. (Apollo)

Markets

#SpaceX and now Anduril rewriting the rules for venture scale outcomes

#Micron on a tear and just wait until humanoids are built at scale

#👀

#but a reminder…

Lesson 1 of many: just ship it and learn and iterate or kill. That is it - you need to know what real signal is from noise. Here’s how many different ideas or products that Michael initially ran through.

Started with an AI email companion to an AI-enabled IDE 🤯.

Second lesson, everyone needs to sell - don’t be shy - the right person is out there - keep going.

Anyhow…Cursor’s success isn’t just a story about coding.

It’s a story about where value accrues in a world of abundant intelligence. For years, the assumption was that the winners in AI would be whoever built the biggest and best models. This week provided more evidence that the future may be far more complicated.

Open source keeps getting better. Chinese models continue to close the gap. Enterprises are increasingly embracing a multi-model world. And startups are building massive businesses by owning the workflow, the data, the evals, and the customer relationship.

And when intelligence is abundant, the model stops being the moat. The flywheel becomes the moat.

Own the workflow, and you capture the data. Capture the data, and you can post-train your own model. Better model, better product. Better product, more workflow. More workflow, more data.

Whoever spins that flywheel fastest wins. And they can do it at any layer.

This week felt like a major acceleration of that trend.

First we have Z.ai out of China - wow is right!

A year ago most people would have laughed at the idea that an open-source Chinese model could be mentioned alongside the leading frontier models. Not anymore.

And the benchamark results speak for themselves.

This is about far more than a leaderboard. It’s another reminder that model quality continues to converge. Which means intelligence itself is becoming more abundant.

Mitchell Hashimoto captured this perfectly.

This is also why model infra companies like Baseten continue to raise at insane growth numbers

Baseten, which specializes in providing software and computing capacity to companies tapping in to lower-cost AI models, is finalizing a $1.5 billion fundraising round. It will be a dual-tiered structure, with some investors putting in money at an $11 billion valuation and others at a $13 billion valuation, according to Baseten.

Which makes sense considering that $11B startups like legal AI co Harvey is doing hard core model post-training sitting on top of this stack.

Here’s the full post on Harvey’s strategy laid out by Gabe.

Finally, the DeepSeek moment is here, potentially, for Microsoft. Whether or not Microsoft offers, even the idea of it, shows how far we have come (Axios)

The cat is out of the bag - we are all living in a multi-model world and its only accelerating. The conversation used to be can anyone catch Claude and now it’s how many models should we use.

Aaron sums it up for Cursor:

Cursor didn’t stop at owning the workflow. They used it to capture the data, then turned the data into their own model. Harvey is running the same loop in legal. The app layer reaching back down to the model layer isn’t a complication. It’s just the flywheel completing a full rotation.

Now look at the full Michael arc.

Start with a pile of ideas. Kill most of them. Build an IDE. An insane bet. Launch Cursor. Subsidize insane growth on negative gross margins.

Survive an assault from your own partner, Anthropic and Claude Code.

Post train your own model on Chinese open source Kimi. Sell to SpaceX. Take that data flywheel and point it at unlimited compute. And lo and behold, launch your own model at Anthropic and OpenAI scale.

You can do it.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#What’s 🔥 at YC Demo Day from TechCrunch

Defense/Hardware: 9 Mothers (counter-drone robots, $1.6M in sales, ~$200M valuation, potentially highest-valued in YC history). Dispatch (return vehicles for space manufacturing). Physical AI is real.

AI Code Infrastructure: Arga Labs (digital twin sandboxes for testing AI-generated code), Sazabi (find + auto-fix production bugs), Superset (run 100+ coding agents simultaneously). Your “second wave” thesis everywhere. More code = more testing, more debugging, more orchestration.

AI Security: Silmaril (agent prompt injection defense, autonomous threat probing). Directly in your Keycard/Surf AI orbit.

Non-technical builders: Lightsprint (PMs ship features without engineers), Ploy ($27M seed from First Round, Webflow CTO’s next company, AI website + marketing automation).

Enterprise agents: Tasklet (connects to work APIs, runs continuously), Complir (compliance for physical goods across borders).

Second-wave coding infrastructure (Arga, Sazabi, Superset). Agent security (Silmaril). Physical AI / deep technical (9 Mothers, Dispatch). The “just another wrapper” companies are gone. This batch is infrastructure and hard problems.

Enterprise Tech

#the world is moving insanely fast

#been thinking about this a lot lately and Satya nails it with humans plus AI together is what creates value which means owning your evals, private evals like I wrote last week is where the IP lives

more on Evals and the need for humans!

add this must read post from the founder of inference provider Fireworks and you can see where the world is going and this is key if you click and read the post “The result keeps surprising people. On the tasks they care about most, a tuned open model can match frontier quality at a fraction of the cost. What this week made clear is that cost was never the most important question.”

#speaking of humans…

#regarding intelligence, for those who got to try Anthropic’s Fable, it is pretty extraordinary - what’s key here is acknowledgement that there was a window but now it has long gone as amazing models continue to build yet more amazing models

#the frontier labs have to be ambitious - don’t worry, Claude Code is a research product Anthropic tells Cursor 🤦🏻‍♂️

#a new github competitor built for the agentic era?

#🤯 certainly the future - what timing and more incredible performance - Fable-level intel for 1/2 the price? who needs one lab when you can have many with one query?

#interesting commentary on the state of AI infra - startups versus enterprise

#the coming onslaught of GPUs on the edge - still need more accessible models

#solving the memory wall problem for GPUs - more context means more memory needed and only so much high bandwidth memory paired with GPUs - we will continue to see new ways to cache and do smarter offloading to solve this

and I do have a port co working on this 😄

#yes AI replaces consultants versus software fully

#from Goldman Sachs IT survey - who pays for AI inference? Net new or taken away from other budgets?

#this is the future but so early

Markets

#Zirpicorns 🦄! AI embedded and accelerating - LFG

First huge congrats to What’s 🔥 reader Jack Naglieri of Panther, a leading AI SOC platform, which just sold to Databricks for $1.4B years after its last funding at a $1.4B valuation back in December 2021. You may say that is not an impressive valuation for a company valued at that price 5 years ago, but I would say it’s an insane exit to be able to grow into the ZIRP-era valuation and be relevant for today’s agentic world!

Congrats to Intercom as well - the poster child for how a founder comes back as CEO, goes founder mode, makes a big bet a few months after ChatGPT launch and 3 years later sold for $3.6B!

#but for the agent native pure startups, the goalposts just keep getting bigger

#the Elon premium 🚀

#with AI products and without

#👀

#🤔

Sometimes the light's all shinin' on me
Other times I can barely see
Lately it occurs to me
What a long, strange trip it's been

It’s truly an insane time as we have the largest IPO in history with SpaceX now worth more than $2 trillion, and the largest private company in the world, Anthropic, forced to take its newest models offline after the U.S. government restricted access. Meanwhile, the market is moving so fast and the uncertainty so high, software companies that would have been funded 12 months ago are having a hard time raising capital.

This is the startup market in June 2026.

At the top, the outcomes have never been bigger. Capital is flooding into frontier AI, chips, data centers, robotics, defense, energy, and space. For a handful of companies, the light has never shined brighter.

Earlier this year, one of our boldstart LPs, StepStone, shared this chart at its annual meeting. It captures just how concentrated venture has become: the top 10 private venture-backed companies represented 112% of the value of all venture exits over the prior decade.

And this was before SpaceX’s IPO and Anthropic’s recent rounds.

In fact, here is what it looks like now - thanks Fable.

Just two companies now represent more value than every venture-backed exit from the last decade combined 🤯. The concentration is simply staggering.

Hardware is simply easier for investors to underwrite right now. It’s physical and scarce, and building it requires enormous capital and deep technical expertise. For many VCs, software is much harder to underwrite but IMO that’s where the opportunity lies!

Every investor I talk to is asking some version of the same questions: What happens when the next model drops? Does the model provider absorb your product? Does your edge disappear overnight? If everyone can build software faster and cheaper, where does lasting value accrue?

It’s easier for many investors to do nothing and just wait until they see signs of a breakout or more of an inflection point. This means many companies with real customers and real revenue are struggling to raise because nobody can say with certainty what software will be worth in three years.

But I believe that uncertainty is exactly where some of the biggest opportunities will be built.

Look at what happened with Anthropic and Fable. Before government restrictions disrupted access, enterprises were already wrestling with rising token costs and limited control over how models could be used. Even Meta is reportedly moving to curb employee token usage as its internal AI costs climb. This is exactly why enterprises cannot rely on a single model provider.

As I’ve said repeatedly before (What’s 🔥 #500 and 501) the opportunity is in applications that work across multiple models, route workloads intelligently, protect proprietary data, manage costs, preserve institutional context, and keep running when a provider or policy changes underneath them. This is only going to accelerate with this latest government intervention - enterprises and founders need more control over their models and data.

Satya Nadella recently offered a framework that I think nails where the value is heading. He argued that a company’s private evals may ultimately become its most valuable IP. (h/t to Gokul for summarizing this)

What’s an eval? Think of it as your company’s custom exam for AI. Not a generic benchmark, but your specific test: Did the model approve the right refund, catch the compliance violation, prioritize the correct security alert, and complete the job at a cost that makes sense? Generic benchmarks tell you which model is smartest. A private eval tells you which model is best for your business. Satya’s test for control is simple: run your eval on model A, swap in model B, and see whether you can keep improving. If you can, you own the application. If you can’t, the model owns you.

Models may become interchangeable sources of raw intelligence, but your private eval captures something they don’t have: your proprietary definition of good, built from years of customer feedback, failures, edge cases, regulatory requirements, and expert judgment. The model supplies the intelligence; the eval defines what valuable intelligence means. And the eval isn’t the whole moat. The real defensibility comes from private evals combined with proprietary context, workflow integration, customer feedback, and execution data. Every completed workflow improves the eval, which improves the product, drives more usage, and generates more feedback. That compounding flywheel is a capability every AI-native startup, SaaS company, and enterprise will need.

This is also why I keep coming back to ROI per workflow as the metric that matters.

The eval measures whether the workflow works, while the software determines which model delivers the best outcome at the right price.

Hardware may be easier to underwrite because its barriers are visible. But software will own the context, evaluation, orchestration, and workflows that turn intelligence into enterprise value. That’s a massive opportunity, and much of the market is still too uncertain to see it.

So yes, this is one of the hardest times I can remember to raise money for a software company. But for the right software companies, hard to underwrite doesn’t mean the opportunity won’t be massive.

What a long, strange trip it’s been.

And somewhere inside all this uncertainty, enormous software companies are being built.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#love this…

#and this - Elon himself gave SpaceX less than a 10% chance of succeeding:

“I gave SpaceX less than a 10% chance of succeeding at all, to be clear. In fact, I told people this: I said, ‘look, we’re probably going to fail, but, you know, we should give it a try because if we don’t, if if there’s not a new company that enters space, we will never be a truly spacefaring civilization.’”

#there’s another one like this out there now!

Enterprise Tech

#🤯 Claude Fable 5 is out or Mythos for everyone but while super impressive, Anthropic also partially botched the release. First here’s what’s amazing: Felix talks about the third era of AI: I believe we're about to see a major shift, moving from giving AI tasks to giving it responsibilities.

#no longer…must read as the government now has shut down access to foreign countries and foreign nationals, given Anthropic can’t easily identify those it shut down pure access - i suspect this will be resolved by next week but the implications are massive and will only accelerate the idea of control your AI and data

and the view from other countries like India - here’s Zoho’s founder - open source = more control

#💯 before the government intervention, the Fable release and how it was done was already going to accelerate the need for more control of your AI

#btw, you don’t want to get on the Anthropic enterprise tier as its all token consumption

#🤔

#what OpenAI is thinking about cybersecurity with their new hire from Semgrep

#need more original data! Model collapse: AIs trained on AI outputs lose rare, creative “tails” of data first. Generations converge to bland, homogenized slop which is irreversible even with some human data mixed in.

#your data, your AI - Meta also moving from open source to closed and focusing on personal AI

#speaking of personal AI, not sure if I want Meta reading all my data though - look at this chart from Clem below, massive improvements!

#Brian from Coinbase nails it

#🎯 robotic model performance about to make some major step changes - watch Pete Florence from Generalist AI below

#in person experiences as the hedge against AI? It’s why the price of sports teams and other physical assets are going (FT)

#great read on Unitree, a word leader in developing robots

Markets

#just wanted to reshare this 🤯

#SaaS not dead for all, just for some

Until now, building AI around your enterprise has been difficult, time consuming, and expensive. The tradeoff was simple: use frontier intelligence and pay for it, or use cheaper models and sacrifice capability.

That tradeoff is starting to break.

The messaging this week was coming from all directions, but it pointed to the same idea: enterprises no longer have to choose between intelligence and economics.

Some are building AI around their own data, workflows, and institutional knowledge. Others are going further, combining proprietary data with open models, post-training, and routing. Different approaches, same destination: more control, better economics, and AI that increasingly reflects the business itself.

A week ago this felt like a pitch. This week it started looking like a roadmap, although we’re still early.

As discussed in last week’s issue #500, the budget pressure is real and it’s forcing the conversation. Listen to Dara at Uber:

Let's look at three vendors to dig in further. First, Palantir. It costs you money, but it’s smarter because it understands your data, workflows, and institutional knowledge while keeping them under your control. Alex Karp lays it out:

Kirkland & Ellis doubles down on that message.

The frontier labs heard it too. Microsoft's answer is to make the model yours. They launched 7 new models, yes they're in the race with OpenAI, Anthropic, and Google, but more importantly read this from Mustafa's post:

With Frontier Tuning, you’re building your own model, trained on your your own data, within your environment, controlled by you. Ok, this is not open source and neither is Palantir but you do get my point - why use a frontier lab model at the highest cost when perhaps i can rearchitect a better way. Microsoft claims that Land-O-Lakes has a 10x more cost efficient model than Open AI’s GPT 5.5.

Here’s the fork worth naming out loud.

“Own your AI” is splitting into two camps.

Context is yours: Palantir and others are helping enterprises build AI around their institutional knowledge, workflows, permissions, and data. The model underneath can change, but the enterprise context becomes the asset.

Model is yours: Nemotron, GLM, and the post-training crowd give you the model, the data, and the weights, with routing on top.

Both beat renting raw frontier intelligence for everything.

Frontier models aren’t going away. But long term I’m betting the biggest winners will combine frontier intelligence with proprietary data, enterprise context, workflow orchestration, and increasingly their own models.

Control compounds.

The company that owns the AI behind its critical enterprise workflows has more control over its economics, its roadmap, and ultimately its competitive advantage.

That’s not a hedge. That’s the call.

Which is why I love Nvidia putting significant dollars behind Nemotron, which is catching up fast and was released under a new MDW open license: open model, open data, and open weights.

Clem from Hugging Face tells it like it is - routing and post-training open-source models gives you smarter, faster, and CHEAPER systems.

We will move to a world like Clem says where post-training with more control of your data and evals will become an important way to build agents. This is also a world we invested behind at boldstart a couple of years ago with Uare.ai. Here’s an excerpt from founder and CEO Rob Locascio talking more about what and why (stay tuned for release soon!)

Everything above is the same argument told in bits. Generalist AI is that argument told in atoms.

Your data, your model, your AI. The catch in the physical world is that there is no Reddit for robotics data. You can’t scrape your way to a foundation model when the foundation doesn’t exist. So the moat isn’t the model, it’s how you manufacture the data no one else has.

Generalist went and built it. 3d printed data hands on real human hands, video of every kind of work getting done, over 500,000 hours of proprietary training data feeding their own model. With GEN-1, deployments are live and the model learns new skills in hours to days. Same playbook as the software camp, just in robots: own the data, own the model, own the outcome.

Which is why I'm 🔥 up that the company announced its most recent round, $400M at a $2B valuation. New lead Radical alongside 8VC, Union Square, Hanabi, Norwest and more, with existing investors Nvidia, boldstart, Spark, Jeff Bezos and more. Angels include Eric Yuan, Bin Lin, Fei-Fei Li, and Naval Ravikant.

We led the inception round in March 2024 with Nvidia because we believed the autonomous enterprise doesn’t stop at software. It extends into the physical world, where the real value of labor lives. GEN-1 is the proof. If you want the founding story, read my partner Ellen Chisa’s post on Pete Florence, Andy Zeng, and Andy Barry, the PhDs, DeepMind, and the lifelong passion behind it.

And as Masa says, the opportunity is simply massive

So back to where we started. What if?

The tradeoff is starting to disappear. Frontier or affordable, owned or rented, smart or cheap. Those were yesterday’s either/ors. This week made them ands.

The question is no longer whether you can own your AI. It’s whether you can afford not to.

The future isn’t frontier versus open. It’s who owns the context, who owns the model, and ultimately who owns the outcome.

As always, 🙏🏼 for reading and please share with your friends and colleagues

Scaling Startups

#follow this thread - so many reposts of horror stories raising from ummm…venture capitalists…

choose your partner wisely, not just the firm, the partner

Enterprise Tech

#let’s all celebrate a major milestone - agentic traffic has finally surpassed human traffic!

#🤯 will AI build itself? Anthropic sees a path to recursive self-improvement - read the data - engineers ship 8× more code/quarter, 80% of codebase is AI-written, coding success at 76%, 52× training speedups, and beats humans 64% on research decisions. This closes the loop for AI to autonomously design better successors. Massive upside…kind of insane!

#don’t count Microsoft out - back and just launched 7 AI models

#i like Jensen’s thinking on this - best time ever to be a software company but make sure it’s built for agents!

#more on that - deep systems of records on most of these or incredible context with the Palantir ontology

#💯 which is why companies need intelligent process mining and mapping to understand workflows in order to figure out how the org works and how to improve

#and also why many cos are using Forward Deployed Engineers (FDEs) to kick off the process as well and more importantly measure what success is - read the full post here

#model optionality increasing

#super cool video and post from Guy Podjarny of Tessl on the Emerging Agentic Stack and why “context and skills are the new code.” Software development is transforming from writing code and implementation to defining intent and instructions. Code is becoming “disposable” and easily regenerated, while context (guiding the AI agent) is becoming the primary unit of work.

along with a deeper discussion with friend James Kaplan, CTO McKinsey, on the future of software development with Guy from Tessl (watch here)

#more of the same but for security harness engineering

#going to be an insane year for robotics - owning the brain, the intelligence layer going to be big business

and Nvidia

#drones are working…

Markets

#the SpaceX IPO roadshow presentation is here!

#speaking of SpaceX - 100x increase in revenue forecasted by 2030!

#SaaS back? just select names which is the correct view - more nuance needed

#that’s some AI concentration

#it’s never as bad as one thinks…or as good - amazing comeback

That’s 500 weeks in a row. So many times I was wiped out, sick, traveling, or just exhausted, but I kept plugging away because writing this newsletter has been invaluable for me personally.

It started simply as notes to myself shared with friends, and over time it evolved into something much more meaningful. I now find it to be a cathartic way to reflect on the week, synthesize my thoughts, connect trends, and think longer term about where the world and technology may be heading.

Thank you all for reading, sharing, and being part of this journey. Here’s to many more issues ahead!

Now back to the regularly scheduled programming…

Ok all, let’s not overreact to every single AI related news announcement. This week it was all about tokenomics and insane costs. As always the reality is more nuanced.

First, here’s what kicked it off…

But people tend to overreact to headlines without digging deeply enough into the underlying context, which is why I agree with Simon here 💯.

Sure, there is so much wasted spend especially when you have token leaderboards versus being focused on hard ROI.

Measuring tokens consumed is measuring effort, not output, and it’s the same mistake enterprises made a decade ago measuring lines of code or hours logged. You get what you measure, and if you measure tokens, you’ll get engineers finding creative ways to burn them.

Here’s the real story. We’re entering Phase 2 of enterprise AI.

Phase 1 was the subsidy era, where frontier labs aggressively absorbed costs to drive adoption and enterprises experimented freely because tokens were effectively subsidized.

Phase 2 is the consumption era, where vendors charge real money, CFOs ask harder questions, and procurement re-enters every renewal discussion.

The winners from here will:

• intelligently route workloads across models

• lean into open source for the 80% of workloads that don’t require frontier intelligence

• reserve expensive inference for workflows that actually move revenue, efficiency, or customer outcomes

Token leaderboards are a Phase 1 artifact.

ROI per workflow is the Phase 2 metric.

This reminds me a bit of how overblown the “Claude Mythos destroys cybersecurity” narrative became earlier this year. Immediate market reactions tend to overshoot reality as Palo Alto Networks, for example, has already bounced back in stock price!

Gary Marcus shares a few more takes from this tokenomics discussion.

The important point many are missing from Gary Marcus’ comments is that he’s not saying agents don’t work. He’s saying enterprises will become more disciplined in how they measure ROI and deploy AI systems. The conversation shifts from token consumption to business outcomes.

And despite all the noise, token consumption itself is still exploding.

The real question is: where does the value accrue?

Regardless of routing strategy, GPU and compute demand still compounds. Look at this chart from Goldman Sachs showing insane token growth in the next few years at 24x!

Sierra recognized this shift early by focusing on outcomes-based pricing rather than token-based pricing.

What’s becoming increasingly clear is that the best enterprise AI companies are already building:

• routing layers

• swappable model infrastructure with an emphasis on open weight

• proprietary orchestration

• hybrid deployment architectures

• on-prem support for sensitive enterprise environments

Some are even deploying open-weight models on-prem for deep customization and control before selectively invoking frontier models when necessary. Harvey, for example, has used its insane growth to build a powerful proprietary data flywheel and is showing how this gets done.

This is exactly where proprietary enterprise context becomes incredibly valuable.

As open-weight models improve, we’ll increasingly see more and more software vendors and enterprises train and customize models directly on private internal data, routing across a mixture of open and frontier systems depending on sensitivity, latency, and economics.

That’s exactly what Larry Ellison has been talking about.

LFG - it’s still early!

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#🎯

#founder advice I strongly concur with

#fun time, lots of parallels between early days of NY and Miami now except timeline is significantly accelerated - listen in or watch

#physical AI

#got to admire the all-in nature of Masa!

Enterprise Tech

#the skeptical view of agents and AI has been all the rage this week…

#remember Facebook for Work, its enterprise play - well that didn’t work out but now its back with its own FDEs but who would want theirs?

#as I’ve written before - What’s 🔥 #498, larger companies are not only using a constellation of models (SOTA, open source) for token costs but also for creating differentiation - why give my employees brains to a model when I can have my own - this will be a bigger and bigger opportunity over time and also may offset billable hours to outcome based pricing?

#seems likes its working…

#🎯

#🤯 insane growth for inference providers who don’t own their own GPUs - thinner margins but slick software on top to optimize inference, etc - all of them growing 📈

#no surprise Fireworks on this list - great list (congrats Keycard, a boldstart port co) and solid deck in this thread - worth a read

#edge inference coming in a big way - Apple will finally flex its muscles with announcments at its WWDC (The Information)

I spoke about this a couple years ago on this pod!

#I feel this every day

#why we led the inception round for Generalist AI 2 years ago and investing more in the stack - stay tuned 👀

#no thank you - but yes, there is no reddit for robotics data

#not a surprise but consulting is going to rapidly change

#🤣

Markets

#reminder - software not dead yet - market overreacted to every SaaS co from frontier lab fear but many have bounced back

#from - Palo Alto back at 15.9x forward with 29% NTM revenue growth and Datadog at 16.9x with 23% revenue growth

Reminds me of the old IBM training manual: computers do not eliminate the need for humans; they change what humans need to do.

Here’s why:

First, you need humans to actually figure out what to automate, what success looks like, and how to maintain it so it continues to work well. Automation creates leverage, but it also creates new surface area.

Second, when everyone from marketing to sales to support can make a pull request, and basic skills become commoditized, you get slop. You still need human experts to discern, to have taste, and to know what great looks like.

This is exactly what Strauss Zelnick, CEO of Take-Two, said about AI and creativity. AI is based on data and is backward-looking. Hits are unexpected. That is why he believes humans are still needed. The same applies to automating workflows and doing great work versus producing more slop.

It’s clear that the work changes. More expertise is needed, not less. And in many cases, the net result may actually be more humans needed.

Cloudflare is another example of how work is changing, and frankly we’ll have to see if this is AI washing or if it plays out over the next 6 months.

Matthew Prince, founder/CEO of Cloudflare, recently took a lot of heat for cutting roughly 20% of the workforce while citing AI and a new operating model. The stock got hit hard.

But there is a super important message beneath the surface, which he laid out in his WSJ op-ed: this is not just about removing jobs. It is about removing friction from the core of the business.

As I’ve said before, relationships and sales will matter more than ever.

While everyone talks about job loss in the AI era, the bigger story may be the removal of friction and the return to the basics: more builders, more sellers, fewer measurers.

So yes, AI will absolutely remove jobs and change org charts. That transition is painful and real.

But the bigger opportunity is not just doing the same work with fewer people. It is rearchitecting the company so humans spend more time on what actually matters: building, selling, creating, deciding, and bringing taste to the work.

The more we automate, the more valuable the best humans become.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#inspiration…esp after you read what’s next

#AI is changing the world and then you have this - quite a sad state of affairs in SF - anxiety abounds for even the agent pilled

#how Anthropic reengineered its sales process for simply insane growth - great to see Clay, a port co, as key part of stack:

#all about the founders and love my team as well!

#PSA - CISOs who subscribe - who wants a free Peloton?

Our portfolio company which has built a platform for automating security hygiene, Surf AI, is running the 2026 AI in Enterprise Security Survey - if you're a CISO or security leader, they'd love your take. Takes 5 minutes and they are giving away a Peloton to one respondent: https://www.surf.ai/2026-survey?utm_source=whats-hot-in-it-vc&utm_medium=newsletter

Enterprise Tech

#tokenomics was talk of the week

#because of this - subsidies going away as OpenAI and Anthropic need to show economic models that will eventually generate substantial profits

#the harness and everything else matters!

#less than $1M raised and at $100M ARR in 19 months - software is not dead but also shows why everyone does not need to be obsessed chasing venture money instead of building great product and getting insane distribution

#🤔

#but AI still can’t count well

#another wrong way to get everyone to use AI

#there is no massive real world data set for robotics 🤔

#one of best published reports on power of Mythos from Cloudflare - bottom line need your own security harness - must read

#speaking of Mythos and Glasswing, here’s an update - notice conclusion!

#market map on AI Data centers - got a hot one in AI data center security not listed here, stay tuned!

#love this turnaround from Ken Griffin - skeptic turned agent pilled - must watch 👀

#guaranteed capacity for those massive customers - don’t get your own additional compute, you are covered here - super smart

#here’s why - look at those tokens 📈

#huge and super cool

#much needed for agentic security from port co Keycard

#speaking of taste

#Yann continues to tell us that current models will not lead to AGI

Markets

#the highly anticipated SpaceX IPO with goal of $1.75 trilion listing price 🤯 - yes, I’m super excited about it but wow, these two slides gives me some dot com vibes meaning the valuation multiples and promise of market size

a TAM of entire US economy 👀

and oh yeah

#Benedict Evans AI Eating World deck always insane source of info

#who’s going to win tomorrow?

#AI is rising on list of concerns…let’s check back in 6 months

This past week we saw that taken to the extreme as both Anthropic and OpenAI announced the creation and funding of separated FDE/AI services companies to speed up the deployment of agentic workflows. What’s most fascinating about OpenAI’s version called The DeployCo is that the very companies it could kill are actually investors - consultancies like Bain, Capgemini and McKinsey & Co as investors (Axios).

The big question is not whether or not these are here to stay and are needed, but what happens when customers get ramped up with the enterprise “easy button.”

That is the exact question I posed on X and it sparked quite a lively conversation (click below).

Here’s what Anthropic wrote in its announcement for it’s new AI services co:

Yes, Claude-powered systems tailored to each organizations operations. This is the path that most enterprises will take, save the largest, as their CEOs and boards put pressure on how many workflows are automated and how much AI the companies are using. What is a concern over time is how locked-in these companies may become especially in a world of compute scarcity and as these frontier labs can change pricing on a whim.

By the way, none of this is new. It’s the same old playbook rebranded. Gergely nails it here, and captures my skepticism, but regardless of what you call it, companies need help making agentic workflows actually deliver value.

Here’s a post I wrote back in 2017 looking at the Mulesoft and AppDynamics S-1 filings - Services are not a dirty word!

This is also why the “services are bad” narrative has always been too simplistic. In enterprise software, the messy last mile is often where the category gets made. MuleSoft understood this years ago. The company wasn’t just selling software; it was helping customers connect sprawling systems, prove value, and turn integration from a project into a platform. AI agents are creating a similar moment now: the product only becomes real when it is embedded into the workflows, data, permissions, and operating rhythms of the enterprise.

So what’s the takeaway for founders?

If every frontier lab is bringing FDEs and services muscle into the enterprise, startups can’t afford friction. The product either has to be dead simple to adopt, or it has to go much deeper than the labs can: into the customer’s workflows, data, permissions, edge cases, and business logic. And founders should be honest: they may need their own version of FDEs too. Call it services, solutions engineering, customer engineering, or something else, but in this market the job is to do whatever it takes to make customers successful and turn that learning into product.

“Better answers” will matter, but they won’t be enough. We are headed into a multi-model world where customers will care about quality, cost, latency, governance, and the freedom to switch. The winners will help enterprises move fast today without trapping them in yesterday’s implementation decisions tomorrow.

In AI, speed gets you in the door. Customer success earns trust. Workflow ownership and optionality are what make you durable.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#😮 PSA - was quite skeptical of claims but after watching this, wow, this is extremely bad behavior and the lack of morals/creativity is shocking - i highly encourage you to watch as a founder and/or investor

#i’ve been writing about the sandwich model of agentic adoption and why showing, not telling super important - here Tobi from Shopify further shows how he’s created a learning org by forcing every interaction with River to be done in public so everyone can learn

#another important caveat - remote first has led to a documented culture which makes this even easier - read below 👇🏻

shopify is still a fully remote company. everything is documented, structured, categorized, and made for easy read/write. our vault, internal tooling, hell, even perf tools feel second to none. not because we nerd out over it (we do) but because it’s a necessity given how spread out the team is.

#onboarding done right

#🔥 but really comes down to grit, athletics is one way to screen but not only

#be careful of what you measure

#17 Israeli 🇮🇱 inception investments and growing, my partner Eliot Durbin shares why and what we love about the ecosystem!

Enterprise Tech

#Mira’s Thinking Machines unveils their model - changing the way humans interact with AI 🤯 - must watch!

#yes been seeing more and more founders pointed in this direction - space, the final frontier esp. as we are more and more GPU/compute constrained and as political pressure and protests ramp up over AI data centers

#we need more than GPUs and there is a difference between chips and architectures for training and inference - answer inference or agentic - great read from Stratechery - also why I’m fired up about one of our stealth portfolio cos building a next gen MPU (memory processing unit) to solve this problem

#this is going to make a huge dent into lots of startups Claude for Small Business

#some great inside baseball in this interview with CFO of Anthropic

#must read on agent harness engineering!

#💯

#💯 glad we have Agentuity in our portfolio which has already built this

#how about the midwestern CIOs and AI leaders? James Kaplan, CTO of McKinsey, convened 50 CIOs/AI leaders in Chicago last week and here are some of the learnings on where they are in terms of adoption:

Prosaic Times

Trading bad inefficiency for good inefficiency at the Technology Leadership Forum

The Chief Infrastructure Technology Executives’ Roundtable (CITER) met in October 2009 at Oceana’s old location on East 53rd Street. Twelve heads of infrastructure met over dinner to discuss operating models. It was cathartic for them. The folks from banking and those from pharma debated who has the most intrusive regulators. At the end one participant …

Read more

3 months ago · 1 like · 1 comment · James Kaplan

I had a chance to speak and participate at the event and wrote more about it here last week:

#good list to followed - can be gamed but overall many names we know about

#yeah, seems like Mythos is pretty darn good 👀

and from the head of Anthropic security read team - worth a read in terms of capabilities and how to prepare

#beware

#as I like to say, easy on, easy off - the lower the friction to get started means companies need to work that much harder to lock you in with data, workflows, whatever but in the frontier lab space - folks are jumping a ton - even Sam Altman chimes in on comments!

#LOL now offering 30 days for free

Markets

#well done by Figma - 😂 not dead - will be interesting to see if longer term trend

And the biggest takeaway was simple: despite everything we read online, enterprise agents are still very, very early and there is still so much to build!

When the room was asked who felt they had agents operating at scale, no one raised a hand. In a smaller agentic workflow breakout, only 5 of 25 said they had agents in production at all. The concerns were consistent: security everywhere, not just in the CISO office; ROI that is still hard to measure; legacy systems that must be modernized before AI can truly scale; unclear governance and ownership models; and rising questions around vendor claims, data readiness, build-vs-buy decisions, and agent lifecycle management. The demand is real, the executive urgency is real, but the production operating model for enterprise agents is still being invented in real time.

Another huge issue: many enterprises did not even know where to start because the underlying work is not well documented. Before you can deploy agents, you need to understand the actual workflows, handoffs, approvals, exceptions, and systems of record. And once those processes are mapped, the answer should not be to blindly automate them. Many of these workflows need to be rethought, simplified, or reengineered first. That creates a big opportunity for tools that help enterprises discover how work really gets done, decide what should change, and only then bring AI and agents into the flow.

Even once you've mapped the work, the next problem hits fast: cost. One leader described a pattern where a tiny sliver of users was burning the majority of tokens. That's how AI finops becomes a real discipline overnight - budgeting, routing, usage controls, the works.

Uber is well ahead of most enterprises here, and even Uber is feeling it. As the company has gotten more agent-pilled, the token bill has gotten ugly.

If I combine Uber’s comments with what I heard from the more mature organizations at the summit, it feels pretty clear that AI cost management is going to become a Tier 1 enterprise pain point over the next 12 months.

For many enterprises, it will always start with the “easy” button to get their workers agent pilled using Claude or Codex. Eventually, the likely answer is not “use the best model for everything.” SOTA models for the highest-value work, then route other tasks to cheaper models, smaller models, older generations, or more deterministic systems depending on the use case and required quality.

This is also why companies like Atlassian are talking so much about Rovo, context, and owning that layer. Michael Cannon-Brookes’ point from the earnings call was exactly this: if you understand the work, the people, the permissions, the tickets, the docs, and the workflows, you can make AI more useful and more cost-efficient. The more organized and business-aware the context, the less waste you feed into the model, the better the outcome, and the more manageable the token bill.

Here’s Boris Cherny, Head of Claude Code, on why context matters in the messy enterprise and why ServiceNow’s pitch is landing: organize the dependencies, data, and workflows in one platform, reduce complexity, and feed models cleaner context. With 7T transactions and 100B workflows, that can mean better outcomes and lower token costs.

Finally, there was real frustration with Microsoft and other large vendors. As pricing shifts toward consumption, enterprises feel less predictability and more walled-garden pressure. No one wants to rip out SAP overnight, but the cost of accessing and moving their own data is starting to grate. Increasingly, many just want to treat these ERPs as headless systems of record. The opportunity, once again, for startups is going to be huge in the coming years.

We are still early on enterprise agents. But the next race is already clear.

For most non-tech enterprises, the winners won’t be the ones with the best model. They’ll be the ones that know where the work lives: the workflows, the context, the governance, the cost controls.

The frontier labs have the intelligence. They don’t have the enterprise.

That’s the gap. And it’s where the next decade of value gets built.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#so agree!

#not your usual AI layoff email from Brian at Coinbase with a focus on speed and startup mode - more importantly orgs have to rethink how work is done in an AI native world: “We’re rebuilding Coinbase as an intelligence, with humans around the edge” - max 5 layers, No pure managers (player-coaches only), AI-native pods + one-person teams

#breakdown of YC’s latest class (h/t Robert Scoble) - lots of infra building for agents, not humans

#no better time to start a company - the best technical talent has an abundance of capital to access…or they can join a frontier lab and get paid a ton and have zero direct reports 🤣

#great list of investors who move with speed…🙏🏻 to be included

#how not to get killed by the frontier labs

Enterprise Tech

#great read on agents and harnesses and so agree with this point

Frontier closed models are far too expensive for the large majority of tasks the world needs to do. As teams start mapping costs to ROI, Open Model Harness Engineering will take off even more. It is almost always worth the investment to at least try to get a potential 20x+ cost reduction

#Anthropic going deeper and deeper into verticals - the frontier lab footprint just keeps expanding…

#ServiceNow wants to own this whole stack - won’t happen but either way, i highly recommend reading the full ServiceNow analyst day deck - master class in enterprise scale and opportunity for AI

#why humans are needed more than ever as more agents - create, architect, judge

#prescient - read the Satya report above as it explicitly discusses the need for “owned intelligence” and “Creating those systems requires a disciplined approach to holding humans accountable for the work that agents execute.”

#on the ground in China’s AI labs

#how gross margins improve in tokenomics…

#should vendors control agentic access to customer data? more on the AI data wars

#the future of software dev coming faster than we think?

#deploying agents that are useful still requires a shit ton of work and why enterprises will need tons of consulting help

#speaking of humans…

#which is why Anthropic only hiring two kinds of people - those with insane product taste and those how are super deep technically

#we have a GPU utilization problem…

Systalyze, a portfolio co, broke down why last week and has a new tool you can try to measure your GPU utilization

#great overview of AI data centers - how the 💰 is being spent - important to understand as one of the biggest capX spends

#that’s a lot of neolabs valued at >$1B

#this will be huge - tokenization of equities coming faster than we think

Markets

#incredible growth and scale 🤯

#VCs blessing winners with check after check in massive markets

#agentifying old school cos via buyouts - this is a big one and makes a lot of sense

#great summary of what LPs are thinking

#an important reminder…i loved my Palm Pilot btw.

#there is never a sure thing…

which i why i read this every few years just to remind myself!

2 numbers got my attention this week.

First, AWS and the hyperscalers delivered. We thought the cloud was the biggest technological shift that ever happened. AI dwarfs it.

AWS hit a $58M annual revenue run rate 3 years after launch. AWS AI revenue is over $15B at the same point, nearly 260 times larger 🤯.

Second, Anthropic.

Reportedly $44B ARR, up 46.6% in a single month. No surprise they are raising at $900B just months after closing at $380B.

So the real question, and the one I got speaking at both the J.P. Morgan Cyber Innovation Summit and Slow Security Summit this week, is this: when the hyperscalers and frontier labs seem to be swallowing every opportunity, how do you invest at inception?

My short answer: incredible technical talent with an opinionated 12-18 month product view, and a mission that can endure for 5-10 years. None of us know exactly what will be right, but the best talent pointed in the right direction will make the right adjustments at the right time.

That is why learning velocity matters. I want a founder who can articulate a clear starting anchor, hold strong opinions loosely, and adapt fast. They don’t chase the flavor of the week, but they also know what worked last month may need to be scrapped.

Fundamentally, we are living in a world with two constraints, neither of which is capital for founders with incredible ideas: compute and talent.

To that end, before we invest at inception, we want founders to show us who their 5-10 key hires are going to be. We love when founders have world-class engineering talent ready to go the moment the investment closes so they can hit the ground running.

Many of those early employees will usually have a history of working for or with the founder. That matters. It tells us the founder can be a Pied Piper for the very best.

And when it comes to cybersecurity in particular, there are two ways to make money.

One is to go after new attack vectors well before they are mainstream. Protect AI is a great example. We were there from the very beginning, a year before ChatGPT was even released.

The other is to reimagine existing solutions and do it 10x better than what already exists. On the first, you are susceptible to market timing and experimental budgets. On the latter, you are facing incredible incumbent competition.

Irrespective of your approach, while we all strive for a massive IPO, most exits in cybersecurity are through acquisition, and many times they are sizable before any real revenue is realized.

What that means, once again, is that acquirers are buying talent. The very best builders and engineers aren’t joining large public companies. They are starting their own companies.

So there are really two lanes from here, as I wrote in What’s 🔥 #492.

You can invest in deeper technical companies like robotics, where the difficulty is durable and physical. Or you can invest in the AI jet stream, where most software companies now live with both excitement and fear about what the frontier labs may ship next.

I also went deeper on the jet stream framework, inception investing, and my broader 5Ps framework with GTMNow last month.

At the end of the day, this is still about people.

People have taste. People learn fast. People recruit other exceptional people.

Models change. Product surfaces shift. The frontier labs will keep shipping. But the founders who win are the ones with the taste to see where the world is going, the learning velocity to adjust when the world changes, and the magnetism to bring the best builders with them.

Both lanes can work. But in either lane, the bar is the same: technical taste, learning velocity, and the ability to recruit world-class talent before the opportunity is obvious.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#what’s needed to win in the AI era - product driven CEO with engineering backgrounds…

#well said and well done by Garry

#We have a clear marketing problem in the tech industry and great to see leaders calling it out. Doomerism and mass unemployment are not going to help any of us moving forward.

#Zuck and Sam weighing in as well this week.

#We are still feeling the hangover from so many companies over hiring during ZIRP, blaming it on AI is what the markets are reacting positively too but the reality is that AI and agents are still barely deployed in production at many of the largest enterprises. I strongly believe we will have more short term pain in terms of job loss but in the long run, AI will help create more jobs as companies grow faster with less.

#AI ain’t going to help you get to PMF any faster

Enterprise Tech

#it’s all about compute and how you pay for it…in the long run, Google has the advantage with the cash flow generate by its ad business

#and it delivered insane results this quarter 👀

#this visual is pretty awesome - Google has the full stack from chips to models to dev to apps

#not to be outdone, Amazon has a pretty sizable chip business as well…

#and AWS selling multimodel, secure, private and easy to use…

#I often write about the last mile in the enterprise is the longest meaning the preparation needed for enterprises to deliver secure, private agent ready infrastructure at scale requires a lot of work and consultant to help even build the agentic workflows - Aaron Levie goes one step further as these workflows are in production, what happens next?

#distribution versus product - will be interesting to see how this impacts Harvey, Legora and others

#markets rattled by this WSJ story on OpenAI missing revenue forecasts

but Tae Kim who wrote the Nvidia way, nails is here - what does this look like moving forward as OpenAI is building momentum while folks complain about Anthropic

#Mythos and Anthropic’s Glasswing has been rolling out slowly…here’s OpenAI’s answer

#this can happen to you - point is agents are powerful but also be careful when playing with 🔥 - this company did not have a separate backup

#yes tokenization of assets will be big in future - 5 reasons why Morgan Stanley’s Head of Digital Assets is bullish on crypto in 2026…

• Tokenization is not the goal but the base layer for bigger businesses built on crypto rails.

• Public blockchains like Ethereum and Solana have won institutional alignment over private chains.

• Asset managers now embed crypto into portfolio construction with ETFs, altcoins, yield products, and DeFi structures.

• Morgan Stanley wealth clients will hold wallets for tokenized asset delivery and cross-network interaction.

• Institutions treat digital assets as core operating infrastructure across securities, wealth, and asset management.

#another off the charts 👀 inception round

#feel the same with constant context switching when I have multiple agents doing work

#🤣

Markets

#🤔

#will have to say how this plays out in practice

#not all SaaS is the same, generally agree with these buckets but nuances in each based on data and embedded workflows in org

In the AI era, add one more: knowing when to exit.

Kenny Rogers had the VC lesson right in The Gambler: the hard part is knowing when the hand has played out.

Cursor may be the cleanest example yet. A reported $60B deal for a company with explosive growth, negative gross margin, and a buyer that needed to win the coding race.

In normal software, that combination would raise alarms. In AI, with growth like this and a strategic buyer like SpaceX, it can produce a generational outcome.

The tension is right here: huge growth, ugly margins, and a strategic buyer willing to pay anyway. The bet was growth, usage, and the data flywheel. That bet worked.

At roughly 22x annualized revenue, Cursor looks expensive by old SaaS rules. But the Jefferies data makes the deal feel less like an outlier: half of acquired GenAI companies since 2022 have sold for 20x+ LTM revenue. Scarcity value is real.

That scarcity premium makes sense here. Coding is the biggest AI application market so far, and Cursor had the product velocity, usage, and data to matter. Building a model stack to defend that lead against Claude and others would have required enormous capital. A strategic buyer with compute and urgency can look at the same business very differently than a late-stage investor would.

Every startup has a window where it can realize maximum value. For a rare few, that window comes long after IPO. But in AI, where a lead can disappear almost overnight, knowing when to sell based on future financing needs, dilution, and competitive threats may be just as important as knowing what to build.

Elad Gil hammers this point home.

In the AI era, sourcing, picking, winning, and helping get you to a great company. Exiting is what turns it into a great return. That is not quitting on the dream. It is maximizing value at peak AI valuations.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#seeing this at every portfolio company - the gap between those who have it and those who don’t is widening

#still early for agents!

#same goes for investing - the more you lean on your hard earned wisdom and experience, the more it may hamper your ability to truly grasp what’s new

Enterprise Tech

#awesome launch by OpenAI - Codex is fantastic and given Anthropic latest issues, I encourage checking it out

#the compute problem is only getting worse

#not just GPUs in short supply, turns out you need CPUs to orchestrate all those agents! - Key Context

“The next wave of AI will bring intelligence closer to the end user, moving from foundational models to inference to agentic. This shift is significantly increasing the need for Intel’s CPUs and wafer and advanced packaging offerings,” said Lip-Bu Tan, Intel CEO. “

“We delivered robust Q1 results, reflecting the growing and essential role of the CPU in the AI era and unprecedented demand for silicon, as well as our disciplined execution to expand available supply,” said David Zinsner, Intel CFO.”

“For the last few years, the story around high-performance computing was almost exclusively about GPU and other accelerators. In recent months, we have seen clear signs that the CPU is reasserting itself as the indispensable foundation of the AI era. The CPU now serves as the orchestration layer and critical control plane for the entire AI stack.

This is not just our wishful thinking. It is what we hear from our customers, and it is evident in the demand profile for our products. Xeon server demand is seeing strong and sustained momentum. Customers are deploying server CPUs along accelerators in a ratio that is moving back towards the CPU.”

#must read for anyone in cybersecurity from CISO JPM with greater than $1B budget 🤯 - all about trying to reduce the vulnerability footprint as fast as possible before hackers can exploit them at record speed and lots more to do with security hygiene…

#now that’s an Inception round (FT)

#still hallucinates…

#👇🏻 this plus just the threat of enterprises saying they will build to renegotiate pricing is what is also creating massive downsell on renewals versus just churn

#another example of how much is good enough to pay for existing SaaS

#huge open source news - Moonshot just dropped Kimi K2.6 - matches or beats Opus on real coding benchmarks, built for long missions (12+ hours, 4000+ tool calls, 300 parallel agents), 8–10× cheaper + fully open weights

#capturing employee workflows is a massive opportunity but not the way that Mark suggests - needs better PR

#memory is one of the biggest bottlenecks to inference, Google adding a memory processing unit for TPUs - btw, super smart and I do have a port co doing exact same thing for everyone else think Nvidia, AMD 😄

#Vercel hacked, Lovable hacked 🤦🏻‍♂️ - who’s next?

#the cost of not enough compute…any one else feeling this as well? I’ve been using Codex for my work tasks and it’s getting better and better

Markets

#General Catalyst’s first quarterly investor report publicly released along with interview with Molly O’Shea

Sourcery

BREAKING: General Catalyst's First-Ever Quarterly Review

Read more

4 months ago · 13 likes

#more to come…

#while Medallia’s equity no longer has value, true system of record cos like ServiceNow already have 50% of net new business that are non-seat driven 🤯 - tokens, consumption, connectors

#more on tokens

Enter Marc Benioff. He just ripped the band-aid off.

Let’s pause and appreciate what’s happening here. Twenty-seven years ago, Benioff built Salesforce on a radical idea: kill software, move everything to the cloud, charge per seat. The “No Software” logo became iconic. He didn’t just build a company, he built the SaaS business model that an entire industry runs on. Now he’s taking a sledgehammer to his own creation. The same guy who killed on-premise software is now killing per-seat SaaS. That takes huge kahunas. And the recognition that if he doesn't do it to himself, someone else will.

Salesforce is going headless. It’s called Headless 360, and it exposes every capability in the platform as an API, MCP tool, or CLI command. 100+ new tools, day one. Claude Code, Cursor, Codex can now operate an entire Salesforce org without opening a browser.

I wrote about this exact scenario back in What’s Hot #410 (Sept 2024) - that Benioff’s push into agents could put Salesforce at existential risk because once the front-end gets reimagined with AI, the application becomes just a database and customers migrate to cheaper, faster, more agile systems. I thought this was a 5-year arc. It took 18 months. Benioff isn’t fighting it. He’s embracing it. Instead of blocking access and protecting the UI moat, he’s betting that Salesforce becomes the platform agents talk to, not the screen humans click through. As Salesforce put it: “We made a decision two and a half years ago: Rebuild Salesforce for agents.”

In other words, he’s taking the pain now.

Seat counts are already dropping. Large enterprises are renegotiating. But by getting ahead of the transition, Salesforce may find a tailwind depending on how they price. More on that later.

This is the opening shot for system of record incumbents - expect others to follow soon after. The ones who resist are telling you their moat was the UI, not the data.

There’s a deeper technical story here that matters for every enterprise deploying agents. As Salesforce EVP Jayesh Govindarjan told VentureBeat, early Agentforce customers hit a wall: “They were afraid to make changes to these agents, because the whole system was brittle. You make one change and you don’t know whether it’s going to work 100% of the time.”

The core tension is that agents are probabilistic but enterprises demand deterministic outcomes. Most real-world workflows are a mix of both - steps that must follow strict business logic and steps where the AI should reason freely. Salesforce’s answer is Agent Script, an open-sourced DSL that defines a state machine in a single flat file - versionable, auditable - where enterprises specify exactly which steps are deterministic and which are probabilistic. This is the real battleground. The winning agent platforms won't be the ones with the best models. They'll be the ones that let enterprises blend determinism and flexibility in a way that's auditable and won't break on the next update.

Short-term revenue hit. Long-term growth story. Do the math. 100,000 human users at $300/seat is $30M. Replace those seats with 1,000,000 agents making API calls 24/7 and suddenly the ceiling isn’t headcount anymore. It’s volume. Humans work 8 hours a day, 5 days a week. Agents work all the time. Consumption-based pricing in an agent-led world could dwarf what per-seat ever delivered. If you get the model right.

In a world where agents outnumber humans 1000+ to 1, the pricing model determines everything. Per-seat is dead. Per-call, per-outcome, per-transaction. Whoever figures out the right model doesn’t just survive. They win. The SaaS business model is being rebuilt in real time. Let’s see who moves next.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#🤯

#👀

#if you’re building in AI, hard to ignore this - the center of gravity is SF and btw congrats to our portfolio company Clay on the AI 50 list!

#just love ❤️ sharing these stories of founder grit and resilience

#remember not all ARR is the same, just know what number you’re reporting and diligencing

Enterprise Tech

#full AI budget blown in 3 months, sure SOTA matters but good enough and costs do as well - will be interesting to see how this plays out in the enterprise

Gergely nails it

#the irony - less people who use more AI may cost you more

#how Ramp built its internal AI productivity tool to get the whole org agent pilled

#Aaron Levie shares what key takeaways on what the largest enterprises are thinking in terms of AI and agents - some bangers in here worth reading 👇🏻

#💯 most employees aren’t builders - you need to make it easy for them (founder of Glean)

#impressive…

#is Project Glasswing and Mythos as good as they say it is or is it the workflow and prompts?

#a trend?

#banging on the same 🥁 - AI is not killing cybersecurity. It is accelerating it. Frontier models are turning vulnerability discovery into a commodity. When discovery becomes cheap, remediation, governance, and automated defense become the new security market.

#great to see older school ZIRP era unicorns accelerate growth even faster with successful launch of AI products, Parker and Rippling sharing how it’s done

#this will be standard for many CEOs in months, not years…privacy, security, etc will be paramount to making this work properly and truly capturing Zuck or any CEO

“The Meta chief is personally involved in training and testing his animated AI, which could offer conversation and feedback to employees, according to one person. They added that the character was being trained on the billionaire’s mannerisms, tone and publicly available statements, as well as his own recent thinking on company strategies, so that employees might feel more connected to the founder through interactions with it.”

#and you would have thought Google’s AI adoption by engineers was a lot higher…but no, here’s why

#AI blends into the physical world including bio - solid market map from Bessemer (we have two port cos already with one being Topos Bio and other in stealth)

#RIP Simon Oxley, create of the Github Octocat, redesigner of Sammy the Shark and creator of our very own Sparky 😢

Markets

#all in a year 🤯

#🎯

#claude design is pretty darn good btw!

#add .com, blockchain, and now just be a compute co

#🤣

#🍿 $2T in private credit, three major funds blocking redemptions in the same month, and the Fed quietly asking banks for their real numbers. This is how 2008 started - slowly, then all at once. And this time the debt is wired directly into the AI infrastructure buildout.

Every board meeting right now has the same conversation: coding first, yeah, but then what? How do you agent red-pill the whole org? And the question I keep getting is: show me someone who’s actually done it.

Here’s the best example I’ve seen yet. Ramp. A company founded in 2019 with over 1,200 employees is more agent-pilled than most startups founded in the last 12 months.

The numbers alone are staggering: AI usage up 6,300% year-over-year, 99.5% of the team active, 84% using coding agents weekly, 1,500+ apps shipped in six weeks from 800+ different builders. Non-engineers now account for 12% of all human-initiated PRs on the production codebase. This is the Autonomous Enterprise in action.

Ramp didn’t just adopt AI.

They rebuilt the company so that everyone can ship software.

But the numbers aren’t what’s interesting.

What’s interesting is how.

It validates everything I’ve been writing about in the sandwich model. Agent adoption has to come from both ends. The CEO drives from the top, but real ownership only comes from the bottom up through organic usage. Ramp did both. Leadership set the expectation. But they also built the infrastructure for people to teach themselves and each other.

The key insight that most companies miss: the harness matters as much as the model. They hit 99% AI adoption and then noticed something alarming. Most people were still stuck. Not because the models weren’t good enough. Because terminal windows, npm installs, and MCP configurations were too much for most people, and the few who pushed through had siloed setups with no way to share what they’d learned.

So they built Glass, their own Claude-powered agent workspace. One SSO login, 30+ tools pre-connected, zero setup. 700 daily active users within a month of launch.

The biggest lesson: the people who got the most value weren’t the ones who attended training sessions. They were the ones who installed a skill on day one and immediately got a result. Get people to the “aha” moment as fast as possible. The product teaches faster than you ever could.

The other thing they got right was org design. My sandwich model talks about top-down mandate meeting bottom-up organic adoption. Ramp’s version: a small central team builds the platforms and plumbing, functional teams build on top and give feedback that drives the central roadmap. The spokes drove the center as much as the center drove the spokes.

In a traditional SaaS company, software is built by engineers and used by everyone else.

In an AI native company, everyone builds.

Sales ops builds tools. Finance builds workflows. Support builds automations. Engineers still build the platform, but the rest of the organization builds on top of it.

When that happens the entire company becomes a software factory.

And once that flywheel starts spinning, execution speed compounds in ways that are very hard for competitors to catch.

The biggest surprise from Ramp? It wasn’t who built the most. It was how many people had been waiting for permission to build at all.

That’s the real unlock. Most employees have far more capability than their companies give them credit for. You have to be intentional, you have to make it easy for everyone, and you have to show the way.

It’s also no surprise that Ramp is consistently named one of the best companies for future founders to work at. They hire and screen for ex-founders or potential next founders. The builder culture isn’t an accident. It’s a hiring strategy.

Which brings me to DoorDash. If you're a SaaS company watching all of this and still debating your AI strategy, you'd better pull a DoorDash.

They just acquired Metis, a ~10-person YC S25 company that hadn't raised institutional capital, for $150M all cash. Why? To go all in on agents and bring that talent in-house. Hard to find the best agent builders when they all start their own companies, so sometimes you have to buy them. Pay up like your life depended on it, because in this environment, it might.

And this isn't just an operating story, it's an investment story too. Nearly half of all private equity deals now target software and technology services companies, a share that has doubled over the last 15 years. The pressure on those portfolios to agent-pill their orgs and defend their value is enormous.

The companies that figure out what Ramp figured out will compound. The ones still debating it in the board room won’t.

If you’re one of these SaaS cos, you’d better pull a Doordash.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

Scaling Startups

#every founder we back strives to get this 3 sentence rule, value prop/story - always evolving…Occam’s Razor in practice

#just built differently

Enterprise Tech

#the Mythos moment was last week with the announcement of Project Glasswing - A model too powerful to release because it can find vulnerabilities better than almost every human on earth which is simply amazing and terrifying.

How powerful? In one case, Mythos found a 16-year-old flaw in widely used video software, in a line of code that automated testing tools had executed 5 million times without catching it. The Treasury Secretary and Fed Chair summoned major US banks to discuss the cyber risks. Bank of Canada did the same. This isn’t a tech story anymore. It’s a national security moment.

#but Wall Street doesn’t get it - no large enterprise is automatically going to just let frontier models and agents run wild patching everything with understanding context and dependencies, formal verification and audit trails, and some human oversight - for the simple fixes, yeah, but the harder ones, no way! Hence, a massive overreaction on some of these stocks

#🤯Anthropic surpasses OpenAI in ARR but look at that exponential growth - unprecedented

#the world is dramatically different

#👀

#speaking of Sam, super concerning if he is one of CEOs of leading AI labs in which much is entrusted…

#👀 huge news here…

#i look forward to this every year - Jamie Dimon’s annual letter which covers everything from markets to geopolitics to our economy and finally tech - here’s the AI angle

#10 employees bought for $150M - Metis is an applied-research and product lab building proprietary intelligence: the post-training and continual-learning layer for enterprise agents.

#feeling like this more and more every single day

#😱 every data source an AI agent touches is an attack vector. Every one. Google DeepMind just tested 23 attack types across frontier models and the defenses we have today fail. This is the next massive security category waiting to be built

#why folks will use multiple models, SOTA and open source like Gemma - spent last weekend updating my OpenClaw

#🎯 Aaron nails it - no model can safely do “continual learning” across an enterprise. One banker’s docs are invisible to the next. Sanitizing secrets is impossible. The context layer isn’t optional - it’s where general AI actually becomes useful.

#👇🏻 💯 need proactive - which is why I’m excited for Grepr’s new proactive agent (a port co)

#why the folks who built VLA decided to build their own model for robotics…and now is proving that scaling laws do work in robotics

#absolutely beautiful

Markets

#👀

#state of late stage now

#what’s 🔥 in secondary markets - most of the same names

#Coatue’s model when they led Anthropic’s last round at $380B and other slides from

Newcomer

Coatue Projected $1.995 Trillion Valuation for Anthropic in 2030

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5 months ago · 62 likes · 3 comments · Eric Newcomer

#leverage