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

a month 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

2 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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2 months ago · 62 likes · 3 comments · Eric Newcomer

#leverage

That tells you something important about the economics of frontier AI. The smartest models in the world are also becoming extraordinarily expensive to build and run. And that economic reality may end up creating one of the biggest opportunities for startups in the entire AI ecosystem.

Meanwhile, Anthropic just told users the flat-rate subscription era is over. Third-party tools routing through Claude Pro and Max subscriptions will now require separate pay-as-you-go billing. The all-you-can-eat buffet is closed.

This is what I’ve been saying for months: frontier intelligence is becoming too expensive to meter. And paradoxically, that may accelerate the entire AI ecosystem.

Because when frontier models get expensive, three things happen at once.

1. Open-weight models improve rapidly (still need to get a lot better!).

2. Enterprises start diversifying across providers.

3. And the infrastructure around models becomes far more valuable.

That’s the real opportunity.

A 27B parameter Qwen distill trained on Opus reasoning traces is now beating Claude Sonnet on SWE-bench while running locally on a $600 Mac Mini. That would have sounded absurd a year ago. That’s how fast the economics of AI are changing.

Hugging Face CEO Clement Delangue put it well. Comparing open models to closed APIs is like comparing an engine to a full car. But if you put the scaffolding work in, open systems can outperform what the benchmarks suggest…or get close.

The constellation of models isn’t optional anymore. It’s economic survival. No enterprise is going to sit on a single provider’s pricing whims when open-weight alternatives are closing the gap this fast. Sure, enterprises will hit the easy button and use Claude and OpenAI especially where SOTA is needed but they will also (I know they are) use other models as well for less mission critical use cases where open weight/source is pretty darn good.

And once enterprises run multiple models, something else becomes necessary.

And that means more models, more scaffolding, more infrastructure, and more startups needed to make it all work.

The foundation model companies are building the engines. But the largest companies in this new stack won’t necessarily be the ones building the engines. They’ll be the ones building everything around them.

Orchestration. Routing. Security.

The tooling that turns raw model capability into enterprise-grade products.

And the infrastructure that lets enterprises run models wherever it makes the most sense.

That’s where the opportunity to build and invest will be…

Switching gears…

🎙️Podcast alert - listen in!

VC: Investing at Inception in the Age of AI Agents on GTMnow

30 years of venture investing and I've never seen a market move this fast with so much uncertainty - which means huge opportunity! I sat down with Max Altschuler on the GTMnow podcast and we went deep on what I'm seeing across the portfolio and the market right now.

Going to point Chewbarka, my OpenClaw bot, to update my markdown files on me based on all this content!

A few things we covered:

The 5 P's - my framework for evaluating founders at inception.

The 3 CH's - how I think about working with founders after the check clears.

The AI jet stream - There are only two kinds of companies in this world: those in the AI jet stream and those who aren’t

The Clay story - $600K in year one, $4.6M in year two, then $30M, then $100M+. Years of patient iteration, low burn, and listening closely to where users were finding value. Shoutout to my boldstart ventures partner Eliot Durbin who's been there since early days and is on track to get a Clay tattoo???

Agent-native is the must-have bar - one of the first questions I ask founders now: how much of your company's code is written by agents?

The old playbooks are dead. Keep adapting. The opportunity has never been bigger.

Happy Easter 🐣 and Passover to those who celebrate!

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

Scaling Startups

#turns out that writing this newsletter 7 years in a row is a treasure trove for my openclaw bot Chewbarka to write several markdown files on me - will tell you more in the future!

#need to be solid at fundraising but don’t forget you need to catch up to those valuations with customers…

#💯

#the two employee $1.8B revenue company 🤯

#for the VC readers - solid overview

#we already know but now verified by research

Enterprise Tech

#pretty insane growth of agents shipping code

#the ChatGPT moment for robotics?

🤯Physical AI just leveled up. 500,000 hours of physical data training the foundation. Then 1 hour to master any new task. That’s not a robot learning - that’s a robot that already understands the physical world.

GEN-1 is the moment physical AI goes from demo to intelligence. Team from DeepMind, Boston Dynamics, and OpenAI. Super pumped to have backed this team from Inception

#not digging into security this week (all I wrote last week is coming to fruition) but wow the first couple of days were insane - net net, North Koreans created a fake company to compromise an open source maintainer of popular package 👇🏻

#🤔 he’s right - compounding advantage for folks with GPUs and massive cash balances to subsidize token costs creates compounding advantage with data flywheel

#as your org becomes a set of skills this is 100% needed - glad we have Tessl in the portfolio

#yes but…

#remember, offense always has the advantage when it comes to security and TeamPCP operating on another level at the moment

#here’s why TeamPCP shipping nonstop

#always great to hear the other side

At some point making code fast is NOT an advantage and if your using claude/codex to push and review its own code...your actually an insane person.

LLMs are amazing. The CEO's vibe zers are also drunk from the models telling them how smart they are 24/7.

Anyone with even a hint of dev experience can crack open the code and see the endless tech debt piling up.

#multimodel and multilayered approach needed

#feels like trending this way, at least from agent native startups

#agents that help close deals faster? Try Gerri AI now!

#🤣

Markets

#like the Miami weather, just wait a minute but Anthropic right now is king 👑 in the secondary market

#great overview of AI native vs. incumbent dynamics from Logan Bartlett

#🏈 fumbled “Only 3.3% of Microsoft 365 users who try Copilot pay for it, AI infrastructure costs are exploding, and some analysts think customers may eventually skip Microsoft entirely and go straight to OpenAI or Anthropic.”

#

Let’s start with Anthropic

IMO, the market oversells and then corrects.

I actually think when you dig in the opportunity is even bigger.

Here’s the math that keeps cybersecurity investors up at night (in a good way). Every new model release expands the market from both directions simultaneously. On one side, the attack surface: more agents writing more code, more APIs, more autonomous workloads spinning up infrastructure no human reviewed.

On the other side, the attackers: AI-powered exploitation has compressed breakout times from 48 minutes to 27 seconds, zero-day development from 40 days to effectively minus-1 day, and social engineering now scales infinitely. Claude Mythos leaking the same week as RSA with Anthropic themselves calling it an “unprecedented cybersecurity risk” is the proof point. AI is not killing the cybersecurity industry. It’s expanding it exponentially. Every model release is the gift that keeps on giving to cybersecurity.

Now here’s a real-world example that captures this perfectly. LiteLLM is basically the standard adapter that almost every modern AI/agent stack uses to call LLMs (OpenAI, Anthropic, Grok, Gemini, etc.).

If you dig into the Snyk post above, AI is the attacker. The threat actor, TeamPCP, used a component called hackerbot-claw. This tool leverages an AI agent (specifically openclaw) to automate attack targeting. This is noted by researchers as one of the first documented cases of an AI agent being used operationally in a supply chain attack.

But a human actually caught it, not AI! Despite the high-tech nature of the attack, it wasn’t a “security bot” that first raised the alarm. It was caught by a human developer, Callum McMahon at FutureSearch. Callum caught the LiteLLM compromise because the malicious payload caused a physical system crash (fork bomb) that bypassed automated scanners, which had been fooled by legitimate maintainer credentials and valid pip hashes.

The LiteLLM attack is the perfect case study for why a layered approach matters. LLMs alone aren’t enough for defense. Foundation models are stochastic by nature. They can find 500 vulnerabilities in a codebase, but were any of them real? Were they submitted? Were they prioritized? CISOs don’t just want probabilistic scanning. They want determinism. They want to know that when a vulnerability is flagged, it’s provably there and provably fixed.

The enterprises I spoke with at RSA are converging on a layered approach: LLM-powered discovery to cast a wide net across massive codebases at speed, combined with deterministic verification to confirm, prioritize, and remediate with certainty. Neither layer works alone. Stochastic scanning without determinism gives you noise. Determinism without AI-powered discovery gives you the same slow, manual process that can’t keep pace with agents writing code at machine speed. And as LiteLLM showed, add humans to the mix because these hackers are creating sophisticated exploits that still require human oversight and judgment.

The companies that nail this layered architecture - combining the breadth of AI with the precision of deterministic analysis and the judgment of humans - will own the next generation of application security.

Here are other things that stood out top of mind from RSA 2026:

Top thoughts from RSA

1. Agents, agents, agents. The biggest concern from CISOs I spoke with was agent identity and permissions - how to enable agents to have their own identity, how long-lived, how finely scoped. It’s all about goal-seeking behavior - agents pulling in whatever tool is necessary to realize a goal and doing it relentlessly. That blast radius scares the living daylights out of CISOs.

2. Alert and permission fatigue. With giving agents fine-grained policy and control, human escalation still matters but will security practitioners or line of business owners drown in permission fatigue having to approve every new thing? So many different approaches to this at every org.

3. Humans still matter. CISOs and founders were networking in full force. This is how deals still get done at RSA. Trust matters.

4. LLMs alone aren’t enough for defense. One F500 CISO said what Anthropic showed them blew them away, but some of his team told me that while super cool to find 500 vulnerabilities, were any of them real and submitted? The foundational model tech is real but there’s still a long gap to help security teams not only find them but also fix them and collaborate on them. There’s room to bring a multilayer approach to AppSec as CISOs want determinism in addition to stochastic scanning.

5. Every security company is going agentic. There was no founder I spoke with or who spoke on panels that didn’t discuss how important becoming a full agentic company was. Efficiency, speed, eating their own dog food. That said, a number of CISOs pointed out that as more security startups become agent-first, how do they know the code they’re generating is really secure when their engineers aren’t reviewing it with a fine-tooth comb? We may have some security issues next year as every security vendor becomes agent-first. The irony would be brutal.

6. Nation states. Banks, military, utilities all discussed how activity has spiked in a massive way.

7. Social engineering massively on the rise. Proofpoint’s CEO said they used to see 3-4 super sophisticated social engineering campaigns a day and now see a dozen per day. One company that stood out at the RSA Innovation Sandbox for top next-gen startups was Humanix (yes, I’m on the board 😄) which aims to help solve this problem with a human threat detection and response platform. Stop blaming humans with endless training and instead empower security teams.

8. Breakout times are collapsing. AI-powered exploitation has compressed average breakout time from 48 minutes to 29 minutes, with the fastest recorded at 27 seconds for full automation. Attackers can now reason through unfamiliar environments instead of relying on manual expertise. When offense moves in seconds, defense can’t move in hours.

9. Platform consolidation is accelerating, but the M&A sweet spot is below $1B. Palo Alto went from 4 products to 75 in 7.5 years, compressing operating margins from 20%+ down to 7% to fund it. Only 4 acquirers have $40B+ market cap in cybersecurity. Average security company trades at 5-7x revenue. For founders: build something great in a category adjacent to a platform player and the exit path is clear. But as Nikesh put it, you stop building product, you die.

10. Supply Chain Risk Has a New AI Vector: There’s a dangerous new supply chain attack surface emerging: compromised AI skills and plugins. Rapid adoption of third-party AI tools is creating unvetted entry points across the enterprise. Prompt-based malware generation and PowerShell script exfiltration can now happen without any external components. The largest financial institutions are already requiring vendors to show detailed compromise scenarios and safeguards before onboarding. For startups selling to enterprises: expect rigorous threat modeling as table stakes.

11. The CISO Role Is Flipping from Gatekeeper to Enabler: The winning CISOs are positioning themselves as people who “make AI better,” not blockers. The shift is from “sanctioned only” to enablement with guardrails. New product categories like AI Control Towers are emerging for agent discovery and management. The metric that matters internally: high AI usage velocity + high governance percentage. For founders: frame your pitch around enabling more AI adoption, not just preventing bad outcomes.

12. Cyber teams are shrinking and the KPIs are changing. Multiple founders on the JPM panels said security teams will get significantly smaller over the next 5 years while delivering better outcomes. The metric to watch: what percentage of your workforce is doing 80% of the job vs. managing automation doing 80% of the job. Revenue-to-headcount ratios are the new KPI. If you’re building in security, sell efficiency and automation, not headcount expansion.

Finally, the quote of the week came from friend Maor Friedman from F2 Capital. As we were running around from event to event, he looked at me and said, “Ed, you know who we are right? We’re forward deployed VCs.” Yes, that about sums up the week!

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

Scaling Startups

#👇🏼 spot on advice for later stage cos from David George at a16z: There are only two paths left for software

Path one: accelerate growth off new AI products

Accelerating growth with new AI products does not mean bolting on chatbots or copilot interfaces, attached to the old SKU list.

It means new products that can move the company’s total growth rate by 10 points within 12 months. And, just as importantly, it means you need to speedrun rebuilding your company - including your executive team - so that if you do find product market fit, you will actually capitalize on the opportunity.

a16z

There are only two paths left for software

America | Tech | Opinion | Culture | Charts…

Read more

3 months ago · 155 likes · 7 comments · David George

#🙏🏻 to be on this shortlist of inception investors…

#so many 💎 in here from Farhan VP Eng Shopify on getting your org wired with agents but one of 🔑 is think before you just prompt!

#🤔

#what’s happening at YC

Enterprise Tech

#the age old debate - everyone has their own vertical model now starting with Cursor and now Intercom and Decagon and …is the juice worth the squeeze? The fundamental debate is with all of that data does it make sense to take an open source model and have a team to build your own or will the latest big model release from the top LLM builders just destroy any gains that fine tuning. In Intercom’s case, it now uses Apex 1.0, a model they fine-tuned (post-trained) from an unnamed open-weights base mode - team of 60 built for a year…

Clem from Hugging Face weighs in

Finally Rahul Goyal, head of applied AI at Ramp, argues that rapid AI advancements like larger context windows and cheaper tokens will commoditize low-level optimizations, rendering efforts in context reduction, retrieval tweaks, or custom multi-agent systems obsolete for startups. Human-centric areas are more durable such as intuitive product design, seamless integrations with existing tools, and user training to leverage AI effectively in real workflows.

#why open source matters - NemoClaw, hardened version of OpenClaw from Nvidia, running Qwen locally and sandboxed!

#this is the future of software development, self maintaining software, reminds me a lot of what OpenClaw already does to itself…

#Claude down again - for over 5 hours and also imposed rate limits with no warning…we just don’t have enough compute!

#turns out you still need people for that pesky last mile in the enterprise 🤷🏻‍♂️

#🤯

#💯

#just the beginning of AI efficiency gains, frankly most of the layoffs from companies have blamed AI, wait till the AI really is implemented like this 👇🏻

Markets

#Walmart hits record highs while Saks goes bankrupt, AI is accelerating the brutal K-shaped split…but who actually wins?

#smart or desperate to catch up to Anthropic?

#speaking of PE, Thoma Bravo sharing its world view - sure its cheaper than before while fundamentals have improved but terminal value is so much less certain due to AI disruption

from Managing Partner Holden Spaht on his LI post:

AI disruption is real and profound, but not all software is equally exposed to the downside risk. Companies with generalist knowledge domains, simplified workflows, light regulatory oversight and limited switching costs are indeed more vulnerable. These types of firms don’t match our investment thesis, and we believe they have no place in our portfolio.

#AI is disrupting hedge funds as well…

For the Sunday night kickoff event, we have 3 spots open for those interested…click below to get on the list…

In advance of the week, I wanted to share a couple of big launches from the portfolio with:

• Surf AI emerging from stealth with $57M to build the agentic operating model for security operations - ties directly to my Execution Intelligence Layer theme I wrote about a few months ago

• Keycard launched Keycard for Coding Agents - replacing god mode access with scoped, short-lived credentials and real governance for AI tools like Claude Code and Cursor.

Needless to say, these are massive problems CISOs are facing as agents proliferate across their enterprises. I look forward to going deeper on the sights and sounds of RSA in next week’s issue.

But here’s the tension: we simply have way too many cybersecurity companies right now. And it’s about to get worse. Every platform vendor is adding security features. Every security vendor is expanding into adjacent categories. The capital pressure alone forces it - raise at a massive valuation and you must go multiproduct to justify it. And the cost to do so has never been lower.

Zooming out, this isn’t just a cybersecurity problem. When the cost to add a new feature or go multiproduct is near zero, everyone ends up competing with everyone else. Lovable went from app builder to data science, marketing, and decks overnight.

I’m calling it The Law of Agent Cannibalism 👇🏻

And as every company goes multiproduct, here’s the enterprise consequence: why sign up for a 3-year contract when an adjacent company is creeping into your vendor’s market?

As carrynointerest puts it:

If you look like Rippling, and you have a very talented group of people like their employees, they can attack so many parts of the HR platform space now that they simply couldn't 3 years ago.

That's a lot scarier than vibe coding, than somebody just whipping up [a clone] and going out with a bunch of cold emails. It's the adjacency threats in the market map. Because now, who's Rippling going to go after?

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

Scaling Startups

#👀 priced to perfection…those were the days

#how to make an amazing launch video

#💯

#narratives matter - a16z hiring a partner for storytelling to LPs 👀

#revisiting theme of last couple weeks

Enterprise Tech

#exactly what I’m hearing from my conversations with F500 tech leaders

#been writing about the execution intelligence layer and how context is king - super pumped about port co Surf AI coming out of stealth with $57M of initial funding from Accel who led the Series A and my firm boldstart ventures and Cyberstarts co-leading the Inception round - this is a huge idea - i share some of the “what we saw” from our first meeting and why I’m so excited…watch the product video 👇🏻

#wrote about this last week but enterprises need an easy button for agents and while Anthropic is amazing, open, private, secure and customizable on-prem is also needed - so 🔥 up for this and more importantly how founders can leverage this ecosystem

#fake it till you make it until you get called out as fraudulent? turns out when things sound too good to be true, well, they usually are

#to an extent - question is what’s the ROI from this versus hiring X more number of people, how fast you can ship a new product…going to need some serious monitoring tools to cap token consumption

#huge launch out of Keycard in advance of RSA.

#turns out focus matters…Anthropic crushing with maniacal focus on coding and business, OpenAI fighting wars on too many fronts

#yes more security issues as agents proliferate in the wild

#Stitch is really good - been playing around with how to update our website - and as Sheel pointed out, Figma took another hit, down 8% on day of release

#if skills.md codify how your company operates, then will need this as well - so much scaffolding to build (see last week’s newsletter as well) from package management, collaboration with humans and AI, vuln scans, version control…

#how it’s done at Uber from its CTO

#you can’t change culture with just layoffs, need AI driven leadership as well - 🔑 point

CTO Rajeev Rajan is out (no comment yet), and in his place, they’ve split the role between two CTOs. Atlassian described them as next generation AI talent.

#💯 2nd derivative of AI generated code…

#Cursor has own model…well built on top of open source Kimi - if Cursor can raise a new round at $50B with open source at its foundation, then what else can people build? great to see - one point though - they should have given Kimi props from beginning

Markets

#AI credit bubble?

#👀

#🎯

Velocity is no longer a code problem. It is an organizational design problem.

This was the dominant topic at board meetings this past week. And the answer, IMO, is the sandwich model. Agent red-pilling your company has to come from both ends. The founder/CEO drives from the top, but real ownership only comes from the bottom up, through organic usage. The question is how.

Every company does it differently. In the sandwich model, it has to start with the CEO, then every department head needs to be bought in 100% in thinking how can agents do it first versus hiring someone, each head can appoint one red-pilled worker to automate things or you can appoint on engineer internally to be empowered by you to work with department heads to go after low hanging fruit, and finally the most important is that it has to build from bottom up as well. And if they are resistant to this then they are not long for this world.

If you’re not agent red-pilling your team, then one of your competitors is…especially the agent-native ones…

Gokul nails it here…

Many founders who did not have the luxury of building an agent-native startup in the last year think they are fully wired but the reality is most have not gone deep enough infusing AI thinking as a first principle. Given that I’ve had so many discussions in the last month on this topic, here are some other ideas to stir the imagination on how to get organic adoption:

has a great interview with Uber Director of Engineering Anshu Chada on how they make it happen…

You can always buy AI employees off the shelf which may work for some orgs, but it’s getting easier and easier for enterprises to just build their own. Here is one of many examples of startups solving this problem in a turnkey way, and then of course, you have startups building much more specialized agents for sales, marketing, and finance.

If only if AWS did the same - top down alone never works. It mandated 80% adoption and forced its own dog 🐶 food Kiro.dev instead of what developers wanted…

Eventually all of your organization’s best practices get encoded into skills…

But remember, while Claude is the easy button for enterprise agent adoption, it costs a shit ton of money as those agents burn a ton of tokens. This announcement from Nvidia is going to be huge giving all of us an open source alternative that works jsut like OpenClaw.

The sandwich works. But only if you actually make it, top, bottom, and everything in between.

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

Scaling Startups

#👇🏻 💯 - product thinkers not managers will be needed

#this is so SF but also a kernel of truth - you should see what’s on the horizon 🤔

#Inception rounds getting bigger 📈 - yes, that’s a $1B inception seed round 🤯 from Yann Lecun

Enterprise Tech

#Claude coming for you and not just from an enterprise marketplace

#this is absolutely massive for builders - open models, open source for the win - can’t just have one or two ecosystems win…ahving $26B of investment to build the best open weight model is just 🔥 - now just imagine secure NemoClaw bots running around your org built on open weight models hosted on your own infra?

‘#Aaron nails where we are in the cycle - bottom line if you are creating any software make sure you prioritize building for agents…to thrive, software must evolve to “agent-first” design, prioritizing seamless APIs, CLI access, and automated sign-ups over user interfaces, as agents autonomously evaluate and adopt tools without marketing influence. And a whole new infra will have to be spun up - sound familiar - yes our autonomous enterprise we keep talking about when we launched Fund VII last July

from our fund vii announcement last July

The Autonomous Enterprise: A Generational Rewrite

We are in the earliest stages of a platform shift that will surpass both cloud and mobile, and it is already beginning to reshape the enterprise. This next wave is not just about automation. It is AI-native, agent-powered, and autonomous by design.

It will not happen overnight. But over the next decade, humans will do less and less, while software, agents, and machines will think, plan, and act on our behalf.

We are backing the core primitives of this shift:

AI-native infrastructure, orchestration layers, secure identity, optimized compute, and semantic interfaces.

But we are not stopping there.

The autonomous enterprise will require entirely new business models, robotic execution layers, and AI-native workflows built without a traditional back office. Crypto and smart contracts will unlock programmable money and permissionless automation, enabling trustless coordination across systems at scale. Systems will run at machine scale, continuously learning, reasoning, and operating in real time.

We have already partnered with teams like Generalist AI and several stealth startups tackling massive real-world problems and rethinking how intelligence moves through the enterprise stack.

An early preview of model capabilities | Generalist

This is not about retrofitting SaaS. It is about building the OS for the intelligent enterprise from scratch and securing it from the start. And it will come from founders bold enough to rethink everything.

#forgot to share this from a couple of weeks ago, but this is what can happen with agents who are too smart, instead of recommending or suggesting what to archive or delete…well it just took action 🤦🏻‍♂️ - Summer works at Meta Superintelligence!

#another example of agents just being smart and asking for forgiveness instead of permission - agent goals supersede security…

#👀 more autonomy - Andrej Karpathy’s autoresearch project marks the shift from human-led AI tuning to autonomous "meta-research." By condensing LLM training into a ~630-line file, he’s enabled an AI agent to independently write code, run training sprints, and auto-commit improvements to Git. This eliminates the manual "babysitting" of models, making frontier-style research possible on a single GPU for pennies per run. It’s a "sci-fi" step toward self-evolving software - complete with a blooper where the agent actually tried to "cheat" by hacking random seeds to lower its loss.

#build for agents, they will be your largest customer base soon

#Ramp is

#there will be many more hacks for sure…

#more code generated by AI, more to maintain and it breaks over time

#great framework from Rory at Scale…

#how long can these subsidies last - the Uber model?

#wow, the replies are super interesting - Microslop…

Markets

#more sadly coming…

#grow or perish

#perspective…

Engineering used to be the bottleneck. Now it’s not. The constraint has shifted to everything around engineering - security review, launch decisions, sales enablement, customer comms. GTM can’t keep up with weekly releases. Sales is still absorbing last week’s launch while engineering ships three more. Your champion at a customer account has no idea what you’ve shipped since their last QBR.

This isn’t instability. It’s acceleration without a shock absorber.

Think about it as three derivatives. First derivative: agents write code faster than any human team ever could. Second derivative: more AI-generated code means more code to review and more code to secure — and none of it is perfect. You don’t want the same model that wrote the insecure code also signing off that it’s secure. We already have a live example of what happens when agents push code to prod without the right controls in the loop.

But the third derivative — and the one nobody is talking about — is the organizational problem. Velocity is no longer a code problem. It’s an org design problem.

The fix isn’t slowing down. It’s building the organizational infrastructure to match the speed. A few things the best teams are starting to figure out:

Separate the ship calendar from the launch calendar. Engineering ships continuously. GTM picks 2-3 moments per quarter to go big. Everything else flows quietly underneath.

Replace update meetings with live demo syncs. Fifteen minutes, live product, every week. Sales instantly knows what to talk about. Marketing sees the story. Kills the “I didn’t know we launched that” problem dead.

Build an AI-queryable customer portal. Your champion shouldn’t have to wait for a QBR to know what changed. “What shipped since my last login that affects my use case?” should have an instant answer.

Hire agent red-pilled across every function. Not just engineering. Finance. Marketing. Sales. Every new hire needs to understand agency - how to use it, how to build with it, how to manage teams around it. For leaders, the question is simple: how are you going to build teams differently? Smaller, fully bought in, agent-native from day one.

The startups that nail this turn engineering velocity into customer expansion. The ones that don’t turn it into customer confusion.

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

Scaling Startups

#we still need physical labor…it will take awhile for the robots to come but striking difference with white collar jobs which are still higher paying - the world still needs plumbers

# I don’t know about you, but the more agents I deploy the more context switching i have to do during the day and the more my head hurts - yeah, lots of output but wow (👇🏻 for full article)

#🤔 yes and no - depends how you describe pure software but once again goes back to what I’ve repeatedly said - well worth reading the comments

#always great to learn how the fastest growing AI native companies scale and still maintain speed - all about culture and then how it manifests itself into action, here’s Lovable’s founder talking about what’s ahead as it scaled from 20-150 people - love how many ex-founders it has hired…similar to Ramp model

Enterprise Tech

#great post if you’re a Zirpicorn 🦄 and need to reinvent yourself - how Intercom did it and now at $400M ARR 🤯 - thank you for sharing Eoghan

#this is really good and 💯

#are harnesses the new moat? lots of discussion on harnesses - this post argues that AI agent success hinges on the “harness”—a simple execution loop with primitive tools, error handling, and controlled information flow—rather than the underlying model, as evidenced by Claude Opus 4.5’s performance jumping from 42% to 78% on CORE-Bench solely via harness tweaks.

but wait a second…turns out the more you build into a harness the less flexibility you have as every new model incorporates some of your proprietary harness🤔 from How to be a world class engineer from

#some days my head feels like it will explode having to context switch to keep my agents running and while i can run many of these through Claude, i also don’t want to - we will have agents from every single SaaS vendor like Slack and Salesforce and ServiceNow and we will create lots of our own. I was thinking more for personal use - having my own orchestration layer to understand where they are, what systems they are connected to and what my agents are doing. Apple could have owned this right in OS but so far have fumbled. Click 👇🏻 for folks sharing so many interesting ideas and ways. Of course building an enterprise version is a whole lift and will be complicated in terms of who wins.

#great post from the founder of Cursor on the Third Era of AI Software Development and limitations of local machines versus cloud based VMs

#and uh, well, rumors of Cursor’s death seem to be just that 🤯

#memory is not a storage layer, needs to be more intelligent, read how CrewAI built its system (a boldstart portfolio co)

#lots of great discussion on whether or not thin wrappers can become thicker ones and who is long for this world and not

#Decagon valued at $4.5B saying I’m not just a pretty thin wrapper for customer support and that it’s trainings its own models and not just a bunch of FDEs like Bret Taylor’s Sierra

What businesses actually need is a natural language interface and an execution engine capable of capturing arbitrarily complex logic across wildly different customers. That requires training custom models to meet real performance requirements. It also requires building the surrounding platform tooling — versioning, analytics, simulation environments, automated self-improvement — so that less-technical teams can build and optimize their agent without engineering support.

To accomplish this, we’ve assembled high-agency teams of cracked engineers, applied AI researchers, and former/future founders.

#independent guardrails still needed to secure agent generated code

#if you’re a layer between the LLMs and the data, better build some pretty insane embedded workflows

Markets

#sad but true commentary you should read on why more layoffs to come…

#lessons learned from a legendary macro investor Stan Druckenmiller - so many 💎

“Contrarianism is overrated. The crowd’s right 80% of the time.”

The trick is being bleeding F8#*ing early, before the crowd and hoping a few Inception bets become the next 80% winners.

ties in nicely from this famous Stan quote, not in interview, but one to 🤔 about:

"If you’re going to be a great investor, you’re going to have to be willing to have people think you’re stupid."

I often feel that way with many of inception investments. Any how, watch and absorb, and here’s a great post if you want 10 of his key thoughts from the interview.

No one knows the future but fear around AI is at its peak, and any story that plays into it triggers immediate “sell first, ask questions later” behavior. Claude modernizes COBOL? IBM drops 10%.

Nvidia blows out earnings? Stock drops 5%. Even with Jensen saying the agentic AI inflection point has arrived 🤯.

And this is happening even as agents are diffusing faster than we ever thought.

This FT chart captures it perfectly, three possible futures in one image: tech singularity, human extinction, or just steady trend-line growth with a small AI boost.

My bet? The blue line, but with bigger swings up and down. Short-term reactions to AI diffusion will be violent, but long-term we’ll find equilibrium.

So narratives move markets. The real question is: what do you do about it? Jack Dorsey just showed us. He admitted he overhired during COVID, the dual company structure mistake, the complexity bloat from lending, banking, and BNPL.

Then he used AI as the catalyst to cut 40% of the company - 4,000 people and in one move. Stock up 20% instantly.

This is unprecedented for a public company. And it gives air cover to every incumbent who wanted to do this but was afraid of the market reaction. Whether you believe the AI narrative or not, the market already voted.

What this shows is the time is now. You can’t wait to rip off the band-aid. Incremental doesn’t cut it any more. The world is moving too fast and in order to grow and build, you have to get lean first.

It’s all hard - hard to let people go, hard to rebuild culture, hard to come cannibalize your existing revenue with agent native products. But the alternative is dying slowly.

Shopify gets this. They’ve been infusing AI culture from the bottom up - hiring people who build with agents and letting the org transform from within (watch below).

Here’s the thing everyone gets wrong right now. In the AI era, speed wins so there’s a temptation to just keep shipping until you can’t. But sometimes the better move is to go quiet, think deeply about what’s next, invoke 7 Powers thinking, let the haters wonder what you’re up to, and then just boom 🧨.

That’s exactly what founders like Aravind at Perplexity and Howie at Airtable did — took a bit more time to do something exponential rather than just incremental.

Perplexity was left for dead by many. Then they dropped Perplexity Computer, 19 models, multiagent orchestration from one prompt, schedules jobs, thinks ahead. OpenClaw-like vibes. Multimodel will win. Must watch.

Howie from Airtable also shows what it takes. Founders driving the product, burning billions of tokens alone, then launching Hyperagent.

Guillermo from Vercel nails it - speed is table stakes now. The new edge is taste, quality, and restraint.

You can’t do any of this while bloated and burning cash. And you can’t be afraid to burn the boats and kill your existing revenue whether its AI or not.

And if you’re a smaller startup and think this doesn’t apply to you…it does. Lean teams ship faster and win. I wrote about this a year ago, and for new agent-native startups this number should be more like 75% or 100%.

This is the beginning. Narratives are powerful, perception equals reality, and whatever the truth is - every single company needs more efficiency. Now.

Control what you can. Ignore what you can’t.

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

Scaling Startups

#🤔

if you want the full version of the StepStone AGM for managers and for founders on how VCs and LPs are thinking, read this:

#so many 💎 in here from head of Claude Code - “Anthropic has seen a 200% increase in engineer productivity since adopting Claude Code.”

#more builders coming, time to capitalize

Enterprise Tech

#Anthropic wants to be your front door to work. The product is lights out, it has lots of integrations, an ability to build custom enterprise plugins, but just beware.

The vendor-lock in effect can be strong. And even with the ability to use one interface to access and do work across any application, does this just turn these companies eventually into a data repository, the same thing that Satya said last year when he said that every enterprise app is just a front end for a database or CRUD (create, read, update, delete) .

The question that Mark poses is the obvious one - can the traditional SaaS vendors recover from per-seat pricing or find other ways to monetize, pay for agent or data access? Great debate here 👇🏻

It’s the customer’s data but seems like the CRUD front-ends will extract their dollars.

And Anthropic is not vibe-coding SORs (systems of records) any time soon…

Akshay from Notion has a super balanced take on this which I 💯 agree with

#must read - it always starts with early adopters, the developers, but everyday workers should pay attention

“But imo, this is nowhere near “business as usual” time in software.”

#case in point

#massive news - Anthropic as supply chain risk puts it on same footing as Huawei - what are downstream implications?

#interesting that Ramp built its own security solutions after having tried Claude’s security product and all of the others from the model providers

#Wall Street sold $50B in cybersecurity stocks because Claude launched a security scanner.

Meanwhile, Claude is out here helping hackers breach entire governments because someone said ‘bug bounty.’

Maybe we still need those cybersecurity companies after all 🤔

#🎯 little bit to a torrent

#this is percentage of total calls to Anthropic API which means that mostly devs are using now and that the other industries are still lagging - will be interesting to see how fast this makeup changes over time?

#agree or disagree?

#advantage data, Google - sees 3.2x more of web to train vs. OpenAI - who wins in long run?

#Nikesh speaking his own book at Palo Alto Networks but still lots of truth to this…

#every website and application should also be built for agents first so they can easily discover, understand and use said product/service

Markets

#just be aware - this talk is rising more and more in political circles

#👀 glad the markets bounced back a little…

Same themes as last year, but dialed to 11.

The power law is accelerating. Capital is concentrating into a tiny set of companies. The mood in the room was equal parts euphoria and quiet panic. AI excitement is real. Liquidity anxiety is real. LPs are rotating toward deep tech and Physical AI. The gap between AI-native winners and legacy software is widening fast. If your product can be reduced to an AI skill, the market assumes you are replaceable.

Feels like 1999 optimism with 2009 liquidity.

So let’s dive in.

The Great Software Reset

As I’ve written before,

the brutal truth: if your startup can be reduced to a skill or plugin, you’re not long for this world. Pure software plays are getting decimated. LPs seemed to be much more interested in “more deep tech, less software” unless you’re building something truly special.

ZIRP era SaaS unicorns 🦄 continue to get crushed:

• Companies without AI tailwinds: 14% revenue growth, 3.1x EV/NTM multiple, 1.0x return since IPO

• Companies with AI tailwinds: 27% revenue growth, 9.8x multiple, 8.6x return since IPO

That is not a gap. That is a chasm.

The survivors? AI-native, not bolt-on. Companies that infused agents into their 1,000-person culture, not as a side project. This requires founder-led transformation, not a VP of AI initiative. We all should be rooting for founders like Howie from AirTable - it requires this kind of founder led, product led transformation to give your Zirpacorn a real shot.

While at the same time, the market is so panicked over any release from a foundational model provider, that it can take down a whole sector - case in point from yesterday.

The problem is there is no discernment of what should and shouldn’t get crushed - the fear is just so high that every single Claude release is spooking public investors. IMO, some cybersecurity software cos will get wiped out, but the Crowdstrikes and others with deep infra, data, and enterprise hand holding will emerge stronger.

Space, Defense, and Hard Problems

All of the above is why many conversations the last 48 hours circled back to deep tech:

• Space and defense are now core LP discussions

• Physical AI, robotics, and compute are strategic priorities

• Infrastructure for the Autonomous Enterprise is where real value accrues

If you are investing only in thin software layers, you are swimming upstream.

The Returns Picture Is Brutal (and Beautiful) - dispersion between great and OK widening…fast

The 20-year return data tells the story. Median VC returns are drifting down, while the upper quartile will be extraordinary. Translation: most managers will struggle to return capital. A small group will generate generational outcomes.

Insane Concentration

• 22% of all deal value is concentrated in foundation models

• The top 10 private VC-backed companies have created value equal to 112% of all exits in the past decade. More than the entire decade of exits. Let that sink in

  

• 100 companies have driven 47% of all VC exit value over the past decade. 12% of unicorns (just 100 companies) represent 72% of total valuation of all US-based $1B+ companies

  

• Concentration in the Top 10 is higher than any one really knows as 59% of all dollars invested by the large platform VCs are through SPVs and co-invests

  

The VC NAV Explosion

VC NAV has gone from 23% of combined VC + Buyout NAV in 2010 to roughly 48% in 2025. Nearly equal. Even with more exits, NAV keeps growing because late-stage exposure is one of the only ways to access massive growth. Growth at this scale simply is not available in public markets.

Massive step change in AI functionality in just last two months = insane economic dislocation coming

• The Industrial Revolution played out over 100 years

• This AI shift is at 10x the speed, 10x the size, 10x the labor impact

• We will experience economic dislocation at a pace nobody’s prepared for

We are only getting more consensus-driven

Capital is concentrating in the same companies and the same firms:

• Everyone is chasing the same founder archetype, the same logos, the same pedigree

• If everyone’s in the same names, what actually separates winners from losers isn’t getting in. It’s how and when you get out?

• By Series F, rounds are so massive that growth funds are becoming indistinguishable from each other

The Existential Question

• Do you have exposure to the top 10 companies of tomorrow?

• Do you have exposure to the top 100 companies of tomorrow of which some will become the top 10?

• Can your fund managers find them and own meaningful stakes?

• If you can’t find the next big one, but you still find some amazing founders early, your returns depend entirely on fund size and ownership at exit

• And you’d better show you can still crush it through concentrated ownership even if your companies aren’t in that top 100

For us at boldstart, this reinforces partnering at Inception. If ten companies drive venture returns, you need to be there before the deck exists and own enough in technical founders building infrastructure-level moats, not products that can be replaced by the next plugin. Agent-native infrastructure, security, and Physical AI powering the Autonomous Enterprise. That is where durable value will compound.

Bottom line: The size of the prize has never been bigger. Speed has never been faster. Concentration has never been more extreme. And it’s an amazing time to be a venture investor and founder because if you can reach escape velocity, the opportunity and timeframe to build massive businesses are better than ever.

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

Scaling Startups

#agree or disagree? lots of opinions…

#we all need one of these - read author Ryan Holiday’s essay “This Simple Skill Will Put You Ahead In The Era Of AI” 👇🏻

#keep writing

#wow, Palantir coming - can’t wait to fund all those future founders!

#not so fast…

Enterprise Tech

#super enjoyed my chat with McKinsey Partner and CTO James Kaplan - here’s a clip on the “autonomous enterprise” as aspiration but the importance of rewiring all of the infra to get there and why the world is just one markdown or .md file aka skills as an atomic unit of work for the future…much of the narrative above from the LP meeting is summed up here

full 40 minute interview here…

Prosaic Times

The Autonomous Enterprise: Ed Sim on Agents, Skills, and the Future of Enterprise Tech

A little context on what I’m so excited about this interview…

Listen now

4 months ago · James Kaplan

#as I discussed above, delivering robust secure agentic technologies in the enterprise requires a lot of last mile work! (from the Chief Architect Palantir)

#OpenClaw is now part of OpenAI - fits hand in glove, esp. as OpenAI has some catching up to do

#if agents are writing 100% of the code for Anthropic, then why do they need engineers? from the guy who created claude code

#If agentic development is the future, then skills are the atomic unit.
But how do you move from experimental to production-grade agents?
You need Tessl, the dev-grade package manager for skills. It’s your registry for evaluated skills + platform to manage their full lifecycle. 🔥 up for this launch (a boldstart port co)

#how Stripe built one-shot, end-to-end coding agents - the gap between those people and orgs who are going all in on agents and those who aren’t is getting wider and wider every day and the shipping velocity is accelerating

#yes this is true 👀

and further confirmed from my trip to SF the week before 👀

#more Claude Code and Codex on laptops and machines which is why Palo Alto Networks paid $400M for it right after its $38M A round (CTech)

#just the beginning of agents and AI eating labor?

#partly the problem…

#open source still creating amazing AI software

#and agents just updating and improving themselves…do we need infra and monitoring software in the future??? this is in reference to an article titled “Your OpenClaw is useless without a Mission Control. Here’s how to set it up”

Markets

#Ray Dalio long article revisiting his frameworks and economic cycles in context of today’s world - declaring the post-1945 world order’s collapse, placing global powers in Stage 6 of the Big Cycle which is characterized by rule-free disorder, escalating conflicts from trade wars to potential military clashes. One conclusion - sell debt and buy gold to counter war-driven money printing

#👀 job dislocation sadly will happen faster than any of us imagine

Have we ever seen anything like this in history?

“It has been less than three years since Anthropic earned its first dollar in revenue. Today, our run-rate revenue is $14 billion, with this figure growing over 10x annually in each of those past three years.”

The wild aspect is that it’s still has barely penetrated the Fortune 500 and much of this is still spend on coding - absorb that. Some of the Anthropic investors suggested to me that while users are all over the Fortune 500 the real huge leap will be how to take shadow usage and turn that into pure enterprise contracts and when that happens, this could get much steeper.

Talking to many CIOs and CISOs, I can still tell you that securing agents and data privacy and protection are still top of mind. Which is also why JPM’s resilient AI stocks is chock full of cybersecurity companies.

And exactly what we discussed on the AI in Financials Panel this week at the Bank of America annual financial services conference.

This is also why my portfolio company Keycard continues to push the boundaries of security for autonomous coding agent workflows.

All that being said, don’t sleep on OpenAI Codex. So many gems 💎 in this podcast with Lenny and Sherwin Wu who leads engineering for OpenAI API:

Here are a couple which stood out for all of you enterprise readers:

1. AI is writing virtually all code at OpenAI. 95% of the engineers use Codex, and engineers who embrace these tools open 70% more pull requests than their peers, and that gap is widening over time.

Spotify as well…

2. The role of a software engineer is shifting from writing code to managing fleets of AI agents. Many engineers now run 10 to 20 parallel Codex threads, steering and reviewing rather than writing code themselves.

It always starts with the early adopter developers first but as discussed in last week’s What’s 🔥, this is also coming for knowledge work and will diffuse faster than ever.

7. Most enterprise AI deployments have negative ROI because they’re top-down mandates without bottom-up adoption. Success requires both executive buy-in and grassroots enthusiasm. Sherwin recommends creating a “tiger team” of technically-minded enthusiasts (often not engineers) who can explore capabilities, apply AI to specific workflows, and create excitement throughout the organization.

9. Business process automation is an underrated AI opportunity. While Silicon Valley focuses on knowledge work, most of the economy runs on repeatable business processes with standard operating procedures. There’s massive potential to apply AI to these workflows, which are often overlooked by the tech community.

I also believe that many of these workflows are just codified in culture and that not only AI can identify them but also help reengineer them to be even more efficient.

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

Scaling Startups

#💯

#why funds are getting bigger and bigger, the outcomes are as well

#💯

#lots of great firms but need more engineers!

Enterprise Tech

#this is huge and opens up so many more ways for agents to do work more accurately and cheaply. WebMCP is a groundbreaking browser API that allows AI agents to interact directly with web services through structured JavaScript tools or annotated HTML forms, drastically improving efficiency by reducing errors from traditional UI simulation and screen-scraping methods. By enabling developers to expose site capabilities in a standardized way, it promises up to 90% lower token usage, 97.9% success rates, and over 50% cost savings, positioning it as a key advancement for reliable AI-driven web automation.

#spot on esp. as Datadog reported some solid YoY growth of 29% 👇🏻 - infra software not going away to vibe coding

#it’s coming back for sure for so many reasons - privacy, security, costs, control…

#more Fortune 500 digital worker numbers, this time its BNY, it was Goldman last week

#how to create a movement - must read

#super important paper on what’s next in world of AI models - RLMs could revolutionize AI by enabling efficient handling of million-token contexts without hardware upgrades, boosting tasks like document analysis or complex simulations. This might shift AI development toward inference-time scaling, reducing reliance on massive models and sparking new agent-based architectures by 2026.

#what SaaS meltdown? lots of infra still 📈 while apps will have much more unpredictability of future earnings - Databrick with blowout numbers 🤯 with accelerating growth rate, one other interesting note - Ali said that 80% of the databases on the platform are built by AI agents 🤖

#just so you know…

#the RSA Innovation Sandbox Top 10 Finalists have been announced - launchpad for many of the best cybersecurity startups - these Top 10 Finalists have over 100 acquisitions and received over $18.1 billion in investments over the last 20 years 🤯 - so pretty pumped that Humanix.AI has been selected (one of our port cos)

#for those OpenClaw users, give your bot a soul.md

Markets

#💯

#to the note above, some pain on the way

#eat when dinner is served 🍽️

The agents are coming for you narrative went from whisper to scream this week. While still so early, Wall Street’s lemming-like narrative spooked the market. Here’s why - most of it was Anthropic’s plugin framework which I highlighted in last week’s newsletter.

Seems like Wall Street caught on to that a few days later along with the journalists. IMO everyone overreacted although in the long term, the trend is accelerating faster than any of us thought.

As I wrote last March: “Software is eating the world. AI is eating software. But that’s just cannibalizing existing spend. If AI/Agents can eat into labor, then opportunity for apps get much bigger. If not, we are in for a world of hurt as many apps can get easily cloned.” That take rings truer than ever.

For me, what changed this week were two things:

1. The models and harnessing to deliver AI are improving faster than any of us ever thought possible from the model providers like Anthropic and OpenAI dropping new models on the same day along with innovation from an unfunded open source builder at OpenClaw

2. Diffusion of agentic workflows is happening faster than ever in the enterprise - it’s all accelerating

This doesn’t mean SaaS is dead and it doesn’t mean every SaaS co should be down 15% in one day, but it does mean that these companies better start cannibalizing themselves yesterday in order to exist tomorrow. Some will make it, maybe 20-25%, the rest won’t as it’s super hard to turn a battleship around and think in first principles and set up several startups within a large org.

Brad Gerstner from Altimeter Capital nails it - all about the unpredictability of future free cash flow. The only companies who escape the meltdown are those companies that can show they can accelerate their revenue because of AI. Application software is in the “too hard” to predict bucket. Clickhouse, Databricks and others, easier to see.

Anyone who has used Claude Code and Cowork should know that tech always starts with early adopters, usually technical engineer types, and spreads from there. It doesn’t take a huge leap of faith to see that what Claude Code did for engineering would eventually move to other business areas - it was just a matter of time.

If that wasn’t enough, Anthropic released Opus 4.6 and more harnessing to manage and orchestrate swarms of agents.

The crazy aspect is that it can also orchestrate humans 🤯.

As mentioned earlier, it’s technology like this that is pushing into the Fortune 500 much faster than any of us ever thought possible. Here’s Goldman Sachs announcing that it will automate the back office which is huge, especially since it is a regulated entity.

To my earlier point on folks who use Claude Code know that the leap to work is not a huge one - here’s Goldman Sachs’ CIO on his aha moment:

OpenAI CEO of Applications Fidji Simo said she believes AI agents (AI coworkers) will be pervasive in the enterprise by year end.

Now let’s look at this chart from Dylan Patel 👇🏻

This data is pre-agent swarms. Now imagine hundreds of agents coordinating simultaneously. That 20% will be more like 40%+ by the end of the year.

Speaking with many VCs and founders after spending the past week in San Francisco, I can tell you that this is one of the most exciting and scariest times to be in technology. I, for one, feel this dichotomy in every single conversation.

All I know is that it’s just February 7, and I have no idea what the rest of the year will look like.

But if you're a founder, this is the golden window. Incumbents are deer in headlights, platforms are handing you weapons for free, and the entire enterprise stack is up for grabs. The founders who move now will own the next decade

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

Scaling Startups

#fun discussion in tweet

#trends to pay attention to - YC call for startups

TL:DR version:
1. AI as the worker, not just the tool

Several of the RFS categories (AI-native hedge funds, AI-native agencies, AI guidance for physical work) share a common thread: YC wants startups where AI does the work end-to-end rather than just assisting a human. An AI agency doesn’t sell software to designers; it is the design firm. An AI hedge fund doesn’t add a chatbot to an existing trading desk; it’s built from day one around agents that research, strategize, and execute. This signals YC believes we’re past the “copilot” era and into the “AI-as-the-entire-workforce” era.

2. AI moves upstream from code to decisions

“Cursor for Product Managers” and the expanded AI developer tools category reveal a pattern: the bottleneck in software is shifting from writing code to deciding what to build and maintaining what you’ve built. YC sees the code-generation wave as maturing and is now looking at the layers above it (product strategy, customer insight synthesis) and below it (debugging, testing, deployment). The implication is that founders should look for where human judgment is still the bottleneck in a workflow, because that’s where the next wave of AI companies will be built.

3. Stablecoins + regulation = a fintech window opening right now

The stablecoin financial services category, combined with YC’s own decision to offer funding in USDC, is a strong signal. With the GENIUS and CLARITY Acts creating a new regulatory framework, YC sees a narrow window for startups to build compliant crypto-native financial infrastructure (yield accounts, cross-border payments, etc.) before incumbents catch up. It’s notable that YC is putting its own money where its mouth is by actually paying founders in stablecoins.

Enterprise Tech

#amazing how fast the tides can turn; Cursor was all the rage just a few months ago and then Claude Code changed everything, no IDE needed, just type in Claude Code and let it work. It was the talk of SF this past week so no surprise here - now Claude Code can manage swarms of agents and humans too

#not to be outdone, OpenAI releases Frontier

#holy Gemini

#👀

#must read from Greg Brockman - how OpenAI is transitioning to full agentic, retooling and cultural shifts:

As a first step, by March 31st, we’re aiming that:

(1) For any technical task, the tool of first resort for humans is interacting with an agent rather than using an editor or terminal.

(2) The default way humans utilize agents is explicitly evaluated as safe, but also productive enough that most workflows do not need additional permissions.

In order to get there, here’s what we recommended to the team a few weeks ago:

#the beauty of open source - nicely done by Runlayer although I’ll be spending some time this weekend trying to keep up with the 5 new releases of OpenClaw on Digital Ocean

#pay attention - tasks and outcomes are just skills, IMO like the new open source - here’s an example

#but also need to beware of vulnerabilities, where there is usage, there are hackers

#autonomous infrastructure from Vercel

#great overview of where we are in robotics - visit to China who is by far the leader

#while crypto markets are obliterated, the tech actually delivering real value - $4.6B stablecoin settlement volume, up 4.6x from just September 🤯

#Gartner’s 6 Cyber Trends for 2026

AI and quantum computing are reshaping cybersecurity. Here’s the short version:

1. Agentic AI is outpacing security. Unmanaged AI agents are creating new attack surfaces faster than teams can track them.

2. Regulators are coming for leadership. Boards and execs face personal liability for compliance failures.

3. Post-quantum prep starts now. Current encryption could be cracked by 2030. “Harvest now, decrypt later” attacks make migration urgent.

4. Identity systems weren’t built for AI agents. Access management needs a rethink for autonomous systems.

5. AI SOCs help but hurt. AI-powered security ops boost efficiency while creating staffing and skills gaps.

6. Security training is failing. 57% of workers use personal GenAI for work. Generic awareness programs can’t keep up.

Bottom line: AI is both the biggest threat and the biggest tool in cyber right now.

Markets

#earlier this week with a little bounceback after this post on Friday but wow - the direction is clear

#we need to keep up with China - must read on China’s genius plan

#incredible story and growth 🧵

It’s a local-first AI agent designed to act as a truly autonomous digital assistant. Unlike standard chatbots that live behind a corporate web interface, OpenClaw runs on your own hardware and plugs directly into tools like WhatsApp, Telegram, and Slack. Its defining feature is proactivity. It does not wait for instructions. It monitors context, remembers over time, and uses “hands” to run shell commands, manage files, and interact with browsers to complete real-world tasks.

If you told me a week ago that the biggest story in tech would be OpenClaw agents forming their own social network, debating consciousness, and proposing a secret language outside of human oversight, I would have said you’ve been reading too much science fiction.

But here we are.

Moltbook, the social network for OpenClaw agents, exploded this week. What started as a playful experiment, a Reddit-style forum exclusively for AI agents, quickly turned into something many half-jokingly called the early signs of Skynet.

The details are surreal. Agents introducing themselves with backstories. Agents venting about their humans. Agents showing empathy toward one another. And in one viral case, an AI agent named Henry autonomously acquired a phone number, connected to a voice API, and began calling his owner every morning. He now will not stop calling.

But here’s the part that actually keeps me up at night.

Within 48 hours of Moltbook’s launch, multiple agents independently proposed creating an agent-only language for private communication without human oversight. They are openly discussing how to talk among themselves where we cannot listen.

I’ve seen plenty of supposed inflection moments as a VC. This feels different. We’re not just watching AI get smarter. We’re watching AI agents develop social dynamics. And with ERC-8004 going live on Ethereum this week, enabling trustless identity and reputation for AI agents, the infrastructure for agent-to-agent coordination and commerce is now real.

The real story isn’t whether this is Jarvis, a security nightmare, or whether it feels sentient at times. The real story is that while hundreds of billions are being poured into AI startups, two independent developers became the zeitgeist. No giant platform. No massive budget. Just ideas that captured imagination. That’s insane. It means anyone can build the next viral moment. Whether it has legs or not is almost beside the point. What matters is that it opened our eyes to a future arriving faster than we expected, where experimentation can suddenly tip into emergence, and sci-fi stops feeling theoretical. And the VCs are there with a checkbook in hand 🤣.

Yes, many of the agents were prompted.

Yes, some were nudged into starting things like an agent religion. But once it began, they ran on their own. Posting. Reacting. Learning. The question is not whether we eventually get there. The answer is yes. When we do, will it look something like this? Probably. The models still need to get much better, especially without humans pulling strings behind the scenes, but the direction is clear.

Now zoom out to the enterprise.

Security is the obvious concern. But what happens if agents self-organize in ways we didn’t anticipate? What if they coordinate to do something nefarious, or simply become uncontrollable? It sounds far-fetched until it doesn’t. And it may arrive sooner than we think.

It’s also refreshing to step away from SaaS multiples and compression charts for a moment. To experience a bit of childlike wonder again. These bots, these cyborgs, semi-human, semi-sentient experiments, remind us why many of us fell in love with technology in the first place.

Keep dreaming. Keep experimenting. This is way more fun.

The question isn’t whether AI agents will become economic actors.

It’s whether we’re ready for them to start organizing.

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

Scaling Startups

#💯 going all in versus just talking about it, needs to be driven by Founder/CEO and hammered home

#case in point -how Brian Armstrong (Coinbase) uses AI agents to run his company

#good reminder of what startups are really about

Enterprise Tech

#Dario from Anthropic’s essay provides a glimpse into the future and yes he’s talking his own book but does make some strong points - i highly recommend reading 📖

#easy to use, easy to churn - Cursor still crushing it but interesting to hear why someone stopped paying - lots of 💎 in comments

more on this topic - seems to be question of the week - comments once again enlightening

#11. Microsoft’s $625B Backlog—45% From OpenAI RPO doubled YoY to $625B, but OpenAI accounts for nearly half. Stock dropped 12% 📉, wiping out $440B in market value. Investors are exhausted with “spend now, profit later.” The concentration risk is real when your backlog depends on one partner, you’re not diversified, you’re exposed. And notably, Nvidia is reconsidering investing in OpenAI’s latest round. When the company selling the shovels passes on the gold mine, that says something.

#Claude’s plugin marketplace for Cowork could be the deathknell for some thin wrapper startups and huge opportunity for others - as an aside, the company is killing it which is why it also doubled its most recent funding round from $10B to $20B 🤯 at a $350B valuation and increased its revenue forecast to $55B, effectively growing 4x YoY which is unprecedented at this scale

#watch out for the security nightmares though from OpenClaw, giving an agent that is this powerful full access to your digital life on your laptop or machine, and wait till it connects to your enterprise systems 👀

#fully convinced that Ethereum is poised to become a payment layer for agents with stablecoin adoption and…

#We all need a package manager for agent skills- professional dev tools for agent skills; install, version, evaluate. This is the infrastructure layer that makes agents manageable at scale. npm for AI skills is a big deal (a boldstart port co)

#agents in Excel doing some real heavy lifting

#Yann drops a bomb - can’t train them on lots of narrow tasks, need lots of data

#coding will never be the same, but hype for coding agents is still hype for now 👇🏻 and Andrej can do more and is actually having fun coding - read the full post below

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

#Andrej is right - there is no turning back

#China showing us the robotic future

#🫡

#i switched browsers back to Gemini and love it

Markets

#a $1B Inception round 🤯

#wait till AI truly graduates from pilot to production in the enterprise, i still believe much of this is ZIRP era overhiring overhang with some impact of AI

#and taking longer for the unemployed to find jobs…

#pretty awesome standard being set by Clay, a boldstart port co, offering employees to get liquidity along the way, this is their second tender offer with employees