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

11 days 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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a month 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

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a month 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

2 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

Podium is a clean example of the latter.

Podium sells an AI employee, not a copilot. A copilot helps a human do the work. A digital worker owns the outcome.

What’s becoming clear across lower-hanging, easier-to-automate use cases is that if you want growth like this, you need to offer the full digital worker. If you do not, twenty other startups will.

Short summary if you don’t want to read full post:

AI is moving from helping people do their jobs to actually doing the job. The earliest wins are in well-defined workflows where outcomes are clear and the cost of failure is low. That is why digital workers are showing up first in customer support, sales, and beginning to take on real work inside Excel. Companies like Podium are already seeing breakout growth selling digital workers. More complex enterprise roles such as security and IT are still early, but the transition from copilots to digital workers is inevitable.

I wrote about this back in What’s 🔥 #475 sharing this example:

At the end of 2023, over 60k local businesses were using Podium to centralize leads and customer communication. Customers convert more leads and make more money with it. But the real constraint was not software. It was staffing. High turnover, missed calls after hours, and every dropped lead translating directly into lost revenue.

So Podium rebuilt the product around an AI employee. Not a chatbot. An employee that qualifies and schedules leads, handles objections and follow-ups, learns through natural-language coaching, and works 24/7. To work in the real world, it has to think, act, understand context, handle edge cases, and be coachable, like a human.

That leap is enormous.

This behind-the-scenes look from Podium investor and board member Tom Loverro at IVP shows just how material the shift was. Cash burn went from $95M to zero. AI ARR went from zero to $100M in 21 months. Overall ARR growth reaccelerated sharply. For ZIRP-era unicorn founders, this is a reminder that product reinvention still beats financial engineering. It is an Intercom-scale comeback.

As Claude cowork improves and other model providers launch smarter agents, selling digital workers will become a reality faster than we think. It is already working in customer support and sales as Podium shows, marketing via Clay-style agents, and parts of coding. When agents can actually do the work in Excel, where a huge amount of enterprise work lives, this truly uplevels the game.

The real question is how fast this pattern moves into more complex enterprise areas like security in 2026.

Some customers will hate it. They will fear job loss or having fewer people to manage. Others will embrace it as a way to scale with less headcount. Those are the ICPs to land first because this movement is inevitable. For more sophisticated enterprise workflows, it will take time, but the direction is set. This is the direction every serious enterprise product will be pulled toward. Understanding context and intent is what will separate useful agents from real digital workers.

PS: If humanoid robots are going mainstream next year, digital workers in software are right on schedule.

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

Scaling Startups

#lots of ideas - entertaining nonetheless

#great post from Jason on founders who overly focus on headline valuation

#reminder - small teams move faster which is why with AI a team of 2,3, 5 people can get so much done

#deflationary pressure equals…

#

Enterprise Tech

#💯 the world will tokenize (see below) and stable coins will also become the currency for agents

#wrote about Claude code writing all the code last week but super impactful watching Dario talk about it at Davos - months away from AI doing everything a SWE does - what coding tools get eviscerated and what new ones are created?

“I have engineers within Anthropic who say ‘I don’t write any code anymore. I just let the model write the code, I edit it’... - the creator of Claude code recently also said “100% of his contributions to Claude code were written by Claude code” for the month of December

Dario then goes onto say: “We might be 6 to 12 months away from when the model is doing most, maybe all of what SWEs do end-to-end.”

If the recursive self-improvement loop closes this year, the curve is about to go vertical.

On AI impact of junior level jobs:

“Now I think maybe we’re starting to see just the little beginnings of it, in software and coding,” he said. “I can see it within Anthropic, where I can look forward to a time where on the more junior end and then on the more intermediate end we actually need less and not more people.”

He added: “And we’re thinking about how to deal with that within Anthropic in a sensible way.”

#we still have multi-hundred million Inception rounds

#Ben nails the current state of AI and startups

#Jared Sleeper from Avenir has a great data driven deck on state of public SaaS and AI you should check out :do 🏻 with conclusion

#the human emulator is fascinating and worth a watch - reproduce any work a human does with keyboard and screen, the infrastructure is already there in tesla cars 🤔 - however, don’t share your private product roadmap in public!

#OpenAI focusing more on making software more secure and added $1B of ARR in the last month just from the API business meaning it still has a huge B2B business

#vibe coding is a real thing for consumer apps - still ain’t going to see large scale replacements of enterprise CRM systems but for consumer apps, sure

#reminder to build your site for agents, not humans, clean docs, bullet points, etc - even for founders announcing their company or rounds press releases are as important as ever

#pay attention - the world will rapidly become tokenized

#more open source problems - tldraw is automatically rejecting outside code contributions because they're being flooded with low-quality, AI-generated pull requests that waste maintainer time. It's a sign of a growing problem in open source—AI makes submitting code easy, but someone still has to review it all.

#💯

Markets

#lots of debate on whether Brex exiting for $5.15B was a win or loss or who even cares - solid breakdown from Hari on what you may expect, the founders and early employees and investors made an absolute whopper of a return and the others got their liquidiation preference back as the peak valuation was $12B during ZIRP - IMO a huge win to get their money back also!

#another look at Brex - once again $5B is an amazing outcome for so many reasons but look at those early investors

#a great 🧵 to understand what’s happening in China

If you haven’t tried it yet, spend some time with it this weekend. You need the $20 Pro plan, but it is one of the clearest glimpses of where work is heading.

The prep for my day was 🔥, but it still has lots of kinks. At times I felt superhuman and others, well, the dozens of interruptions while Cowork was working were a bit annoying. The idea of it running continuously for hours will come.

I, for one, still am enamored by what Claude Code can do. Here’s a simple prompt I used during a board meeting yesterday and it just worked in the background on my laptop:

With a few more tweaks, adding more companies to the competitive matrix and doing some trend analysis and scoring, here’s the final output. Super useful and took me a couple minutes of prompting.

Back to Anthropic - the folks behind Claude Code noticed that many developers and users were not just using it to build apps but also to automate their daily lives. And just like that 🫰🏻, in a week and 1/2, they go out and build Cowork which blew up the Internet this past week with startup doomerism. This helped reignite the “SaaS is dead” debate and fed into last week’s market selloff 🫢.

What’s even more insane is that almost all of that code was written by AI 🤯. Let that soak in.

If you can think it, you can have AI just build it.

The real constraint is not access to AI. It’s creativity, taste, and agency.

Secondly, holy smokes, as a founder and investor, you need to double down on your thinking around how fast Anthropic and others will move to adjacent applications and opportunities.

When I first started investing in software in 1996 👴🏻, the moat used to be time and money which meant 18-24 months. As we moved to the cloud and open source, that moat became more like 6-12 months depending on the product. In the world of AI it was more like 1-3 months, and now it feels like it’s just days. Yes, days…

Picture a bullseye 🎯.

At the center is the model built by OpenAI or Anthropic. The next ring is the infrastructure that delivers it, APIs, agent tooling, the plumbing. Outside of that sit applications like chat and coding.

What’s different now is that the bullseye is expanding into the outer rings faster than ever, especially with AI writing much of the code. Products like Cowork collapse multiple categories at once.

If you’re building in the inner rings without real differentiation, you’re already competing with the platform itself. And if you’re investing in the outer rings, your lead time is shrinking faster than you can imagine.

Finally, it’s not like founders and investors will stop building and investing in software companies. As we’ve discussed many times before, the last mile in the enterprise is the longest, hardest, and messiest and to deliver real compelling workflows you need domain expertise, security, lots of integrations, and more.

For example it’s not as if a bank will all of a sudden let every employee use Cowork and give it unlimited access to read and write files on local machines. That would be an insane security nightmare.

All that being said, one of the most important characteristics of the best founders and startups continues to ring true - in order to win and thrive in this world, founders need incredible “learning velocity” and need to build an org that thrives on that. What that leads to is just insane execution.

Back in September 2023 I started writing a document that I shared with everyone titled “Back to Basics Building Startups from Inception - some random notes” - you can request access here.

In it, I shared some patterns of success I’ve seen investing in technical founders from Inception that I’ve learned over 30 years and one in particular is unmistakable - the fastest learners win. Here’s an excerpt:

Early Indicators of Success 🔑

Momentum and velocity

1. Product velocity - ship fast but also ship what people really need

2. Hiring velocity - to a point, how fast to get first 3-4 key builders onboard (ideally if an Inception Round, you’ve pre-sold the team who are ready to sign shortly after incorporation)

3. Learning velocity, especially in case of getting to PMF which is often manifested in a founders’ speed to hone the core value prop and pitch - best founders find signal from noise + iterate quickly + multiple times before PMF. So much goes into that 1 sentence value proposition.

Most founders obsess over product velocity and hiring velocity. These matter. But learning velocity unlocks the other two.

Product velocity is how fast you ship. Hiring velocity is how fast you build the team. But learning velocity is how fast you test a hypothesis, design experiments around it, and adapt based on what you learn.

You can ship fast to the wrong ICP. You can hire fast for the wrong roles. Without learning velocity, the other two velocities just accelerate you in the wrong direction.

When companies as large as Anthropic can learn, build and ship a product in 10 days, this becomes even more important today than ever.

AI amplifies everything. The noise is louder than ever. But the signal is there. The companies that win will be the ones that separate signal from noise at machine speed with human judgment and taste, turning themselves into learning and execution machines.

Learning velocity isn’t just about iterating faster. It’s about:

• Testing hypotheses with real customers, not imagined personas

• Designing experiments that generate clear signal

• Adapting based on what you learn, not what you hoped to learn

• Killing noise ruthlessly so signal can break through

This applies to everything: messaging, value proposition, product, pricing, go-to-market, hiring.

Founders, investors, take a deep breath, control what you can but make sure whatever you do you keep learning and applying that every single day.

And there’s always this: keep thinking big!

2026 is going to be an interesting transition year as Claude Cowork is showing us the future while SaaS stocks get hammered - the reality is that it’s still too early and the battle will be fought over the next few years!

This will reward the teams that can learn faster than the market moves. Time to build 🏗️. It’s going to be an incredible year ahead!

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

Scaling Startups

#huge unlock but need authority to cross departments and execute

#epic returns

Enterprise Tech

#🎯 make sure those docs are great, readable, markdown rules!

#the future, one week of continuous building with dozens of agents

#second order effects of AI generated code - will be same when regular employees manage dozens of agents

#just imagine the security issues for this tech!

#Skild raises at $14B valuation 😲 - robotics software

Skild AI Inc., a fast-rising startup that makes software to help robots learn to complete tasks, has secured about $1.4 billion in a new funding round that values the company at more than $14 billion, more than triple what it was worth just seven months ago.

The Series C round was led by SoftBank Group Corp., with participation from Nvidia Corp., Macquarie Group Ltd., 1789 Capital and Jeff Bezos’ private investment firm Bezos Expeditions, co-founder and Chief Executive Officer Deepak Pathak told Bloomberg.

Skild said it went from zero to tens of millions of dollars in revenue over a few months in 2025. The startup is working with more than eight clients, according to a person familiar with the matter. The company declined to name its partners.

#another Crowdstrike acquisition as they enter the browser space - founders need to recognize that once you raise that mega round, the list of acquirers dwindles fast, in this case a fantastic exit as the company raised $37M and sold for $400M - while not a billion outcome, I’m sure the early investors and founders did quite well

#this and 💯 - see at least 2-3 a week

haha, and guess what, Clickhouse now is in the LLM observability space and raised at a $15B valuation

#how Claude Code actually works visualized as office workers!

#the death of RAG? this is a huge deal - worth a read 👇🏻

#agents starting to roll out in 2026, from 1000 agents 18 months ago to 25,000 now

#the discussion continues on context graphs and the execution intelligence layer (see my post #478) as James Kaplan who is a Partner at McKinsey and CTO there leading agentic transformation - totally agree on this thought and one I’m already invested in - think next gen Celonis!

Subscribe to his substack here to learn more about what the largest Fortune 500s are doing

Prosaic Times

Prosaic Times: Your business is a dynamic system

Some thoughtful young men encouraged me to read Smart Brevity, given that , er, issues of Prosaic Times run to 3,000 words. Some of my colleagues say that I write more quickly than they can read. I may have suggested, in return, that they might learn to read more quickly, but the historical record is unclear here…

Read more

4 months ago · 3 likes · James Kaplan

#$2B inception round on the people and this 👇🏻

Markets

#just so we all get the memo - one stumble with AI where it’s not meeting expectations and we could have a big problem (full deck from Apollo here)

#and the AI native growth continues to $15B in 2 1/2 years- OpenAI $10B of that, Anthropic $3B

#more AI native vs SaaS debate sparked by a Thoma Bravo editorial in the FT here

When capital concentrates like this, winning is no longer optional. It becomes the strategy.

Here’s Jack Altman interviewing Ben Horowitz on what really matters in venture returns: the ability to win. Not as a slogan, but as an operating model.

I definitely agree with this assessment especially as the world moves from too early or not interesting to consensus and hot. To deploy that much capital and to get that much ownership for the right to lead rounds means winning is by far the most important trait. As investors move up the stack and write larger checks at Series A, B, C, and beyond, competition intensifies. The pool of obvious winners narrows, capital crowds in faster, and the right to lead is fought over. Winning compounds.

Which is why, more than ever, investors need to be honest about what game they are playing.

For us at boldstart, that game is Inception. Being first. Partnering with technical founders at the ideation stage, before markets are obvious and competition explodes. While many inception rounds are competitive, being there early dramatically reduces the universe of competitors and changes the risk profile.

Winning still matters, but so does intuition. Not overthinking opportunities. Not anchoring too tightly to early market maps. Remembering that it’s not the TAM you start with that matters most, but the TAM you exit with.

I call this Intuitive TAM. It’s a different style of investing. It underwrites conviction about what the world can become, not just what it is today.

But it’s clear the world is bifurcating even more where IMO the only way to win in venture is to go big, go niche and specialize by stage and expertise, or go home as it’s tough to compete in the middle.

In our case at boldstart, it’s all about Inception, being first, and funding the autonomous enterprise. We focus exclusively on company formation. That discipline carries through to fund size. Fund VII at $250M gives us the flexibility to lead small rounds while also writing $15M inception checks for truly exceptional, experienced founders.

For founders raising their first round, remember this simple rule of thumb: your round size picks your investors. Around the $5M mark, the investor universe changes materially.

The world is completely different as we head into 2026 with numbers like this.

Finally, while not a $1B headline, I’m super pumped for Tal Zackon and Eilon Lotem, co-founders of Tres Finance, on their $130M exit to Fireblocks. For us at boldstart, it’s also a meaningful win as we led from Inception. A key lesson for founders: who’s on your cap table matters. Managed thoughtfully, angel allocation can pay significant dividends over time. Here’s how we help founders get this right from day one.

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

Scaling Startups

#not surprising but EIQ and people skills always mattered but even more in age of AI

#i’ll keep reminding all of us, authenticity will matter more…great read from Lulu

#from the legendary music producer Rick Rubin’s book, The Creative Act which I wrote more in depth about below

in What’s 🔥 #325

#🎯 creating a culture of winning - super impressive from Indiana football coach who turned the program around in a couple of years to play for the national championship

Enterprise Tech

#important read as everyone plays around with Claude Code building apps and writing to your file system on your laptops…a world of apps running on your own machines equals incredibly intelligent and private

#celebrate your wins - big stuff coming in crypto infra in 2026 as most TradFi institutions get onboard

#to my point above

#RBC CIO Survey - no surprise here, mostly everyone increasing spend on AI and cybsersecurity

#💯 must build for agent first world

#which kind of crushed this open source company Tailwind

And more specifically in the audio note from Adam summarized here:

They made Tailwind CSS so accessible and well-documented, it became a favorite for AI tools (like LLMs and code agents) to reference and integrate, which exploded its adoption and popularity. However, this backfired on the business side because those AIs pull info directly without driving users to the official docs site—Tailwind’s primary funnel for upselling paid products like UI components and themes. Traffic dropped 40% from its peak, starving revenue despite the framework thriving in the wild. He even mentioned frustration with OSS contributors pushing for more AI-friendly features (e.g., “copy code” buttons in docs) that could exacerbate the issue. It’s a classic open-source monetization trap: great for the ecosystem, deadly for sustainability without pivots.

#that feeling everyone got over the holidays using Claude Code as it wrote software, created files, opened them and changed them, and kept building and building while you watched a movie

#🤔 wait till Claude Code really goes enterprise and learns on those massive code bases, right now super amazing for new new creation, huge opportunity to understand and update existing legacy codebases

#2026 is where agents force a new control plane: guardrails, context, and accountable intent. Execution shifts upstream. You can read more on my earlier post on the Execution Intelligence Layer 👇🏻

#robotics will be huge in 2026 - the end shows how it learns from teleoperations with sensors to understand grip, etc - much different from the pure data based approach from port co Generalist AI

#Woohoo – finally out of stealth! Our excitement for the autonomous enterprise at Boldstart Ventures also extends to Physical AI, with investments in several domain-specific models powered by proprietary data from GeneralistAI in robotics, and backed by Boldstart, Nvidia, Spark, and Jeff Bezos, to ToposBio, an AI-powered drug discovery platform.

#merging of observability/sec ops/data infra continues as Observe is bought by Snowflake for a rumored $1B+, last round done at $750M post in July 2025, so slight uptick but once again, super solid retuns for the early investors (The Information)

Observe was on track to generate $70 million in annualized revenue by the end of its fiscal year ending this month, up from $30 million in annualized revenue a year earlier…

But the company, which already shared close ties with Snowflake, was recently burning cash at a pace of about $60 million annually.

#another Palo Alto Networks/Israeli startup acquisition in works - Koi for $400M just 2 years after founding and raising $48M 🤯 (Ctech)

Koi has since built a platform designed to fill a crucial gap in enterprise security. Its main product, Supply Chain Gateway, serves as a central checkpoint for all incoming software. It provides software inventory management, real-time risk analysis, automatic policy enforcement, and proactive blocking of dangerous code. At the heart of the system is Wings, an AI engine that classifies software components, tests them in isolated environments, and identifies threats that traditional scanners often miss. This allows security teams to control software installation proactively, rather than reacting after breaches occur.

#what’s holding agents back - lots of talk about truly securing agents but still so much to build to offer decision-time understanding and truly dynamic, runtime authentication and authorization along with cryptographically proving the agent continues to be the agent as it moves from app to app

Markets

#which is why Crowdstrike paid $740M for SGNL

#👀

#Models raising massive rounds - Anthropic raising $10B at a $350B valuation (WSJ) up from $183B valuation just 4 months ago 🤯 and X.ai raises $20B at a $230B valuation (CNBC)

#Cyera in security DSPM space just raised at $9B valuation (CTech)

AI and data security company Cyera announced on Thursday a $400 million Series F funding round, bringing its total capital raised to over $1.7 billion. The round, led by funds managed by Blackstone and supported by existing investors including Accel, Coatue, Cyberstarts, Georgian, Greenoaks, Lightspeed Venture Partners, Redpoint, Sapphire, Sequoia Capital, and Spark, comes just over six months after the company’s previous funding event and lifts its valuation to $9 billion, a threefold increase from a year ago.

The rapid escalation reflects the growing urgency for enterprises to secure data in an era of accelerating AI adoption. Agentic AI, which operates with autonomous decision-making capabilities, is gaining traction across industries.

That being said, this is simply insane efficiency gains before AI has really proliferated. The budgets of these top 100 companies, many leading banks and tech companies, are massive and the appetite for automation is even larger.

Claude has already been a killer productivity boost in 2025 - here’s a principal engineer at Google sharing her thoughts.

Seems like in 2026, there will be some insane super powers beyond just coding 🤯 - 2026 will be a huge year for AI as agentic automation.

But Manus (acquired by Meta for over $2B) seems to have already been on this by optimizing for solving problems versus benchmarks

Also ties in really well with what’s needed to make it even better which I posted in last week’s newsletter on the Execution Intelligence Layer for Agents.

In Satya Nadella’s year-end reflection he muses on what’s needed - rich scaffolds that orchestrate multiple models and agents; account for memory and entitlements;…(it’s short and read his musings here)

As far as who wins, lots of startups will go after this but it is so hard to be a middleware layer. I assume the biggest winners will just build this into their own stacks from incumbent SaaS providers like ServiceNow and Salesforce and the larger LLM providers as well. To win, IMO, startups will have to have incredible time to value with a narrow, vertical use case with complete out of the box integrations and intelligent context in areas like finance, IT, and security. Of course, I do have one of these in the portfolio and can’t wait to share!

So agree with this from Animesh who wrote a follow up article to Jaya on How to Build a Context Graph. Point is we need a next-gen Celonis or FortressIQ, that is intelligent and learns and maps how people interact, what data flows, and map out workflows.

Key IMO will be not only mappying those out but also perhaps running simulations to improve them. There are so many processes in organizations that are just there because that’s the way they’ve been done for decades. Turning these relationships and processes into data with today’s AI will allow you to make these even better. Why automate something that already exists if you can squeeze more efficiency from doing them differently? You just need the result to be consistent in the end.

Barron’s is on this for 2026!

Happy New Year to all and look forward to seeing you in 2026!

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

Scaling Startups

#Peter Walker has the definitive data set for startups for 2025 - a few interesting charts for those founders going from Inception/Seed to A - this is median and as FYI many of my port cos had minimal ARR and raised outsized A rounds

#easier said than done - my prediction for 2026 sounds obvious but much harder to execute - also click through for the full recap of what my fund boldstart ventures was up to in 2025!

Going to be an epic 2026 as agentic workflows move beyond just coding to deliver more value in the enterprise...but lots to build 🏗️ and a word of caution

#so glad this mode is back - i just call it #winning

#want more enterprise design partners👇🏻 - some tried and true methods I’ve learned over many investments and decades 👴🏻

#💯

Enterprise Tech

#Manus - Meta 🤯 - congrats to Benchmark on investing in this team from China who moved to Singapore and also signals that agents without a research lab can be incredibly valuable to acquirers 🤔

Brian from Sequoia chimes in

Greg Isenberg shares a great take👇🏻 for comments

#one of the themes of 2026

there it is again - “true authenticity becomes scarce”

#i highly recommend checking out the Google Deepmind job board to understand priorities for 2026 - 93 open roles give you a glimpse into future

#selling AI workers - btw, my friends have been doing this for quite some time with some insane success

Dave Elkington and Gabe Larsen from Signals

#the year ahead in robotics from Nvidia’s head of robotics - software is the limiting factor!

#learn how to use Claude Code from the creator

#don’t let super volatile token prices fool you, it’s going to be a big year for crypto infra and TradFi in 2026, lots of tokenization of assets and stable coins and more

#spot on

Markets

#in 2026, beware of China, government backed VC funds for AI and hard tech, IPOs of AI companies giving them lots of cash, and insane cutthroat competitive dynamics - must read letter 👇🏻

#if you’re over 40 and don’t understand why betting markets and the rise of casino culture, i highly recommend reading this 👇🏻

#lots of questions around the Nvidia Groq $20B license/acquihire on who gets paid - Dan Primack from Axios did some digging and the answer is “everyone gets paid alot!”

​​This time of year tends to bring long retrospectives. But a few essays this past week stood out for a different reason: they put real historical context around where we actually are in the AI curve and where things are heading next.

Three in particular caught my attention: Ivan Zhao (Notion - Steam, Steel and Infinite Minds), Aaron Levie (Box - Jevon’s Paradox for Knowledge Work), and Jaya Gupta (Foundation Capital - AI’s Trillion dollar Opportunity: Context Graphs). From very different vantage points, they all converge on the same core idea: we are not just layering AI onto existing software. We are rebuilding how work, decisions, and enterprise systems function at their core.

A few shared themes emerged:

1/ This shift is structural, not incremental.

Enterprise software is being re-architected around AI-native systems, not upgraded SaaS.

2/ Context matters more than raw data.

The future isn’t just storing files or tickets, but understanding why decisions happen.

3/ Agents only become powerful inside rich context.

Workflows, intent, history, permissions, and outcomes are what turn agents from demos into systems.

4/ We’ve seen this movie 🎥 before.

Every major platform shift looks primitive until the right abstraction layer emerges. That’s when value creation accelerates.

5/ This is not a one-year hype cycle.

It’s a multi-year rebuild of how work gets done, who does it, and what enterprise software even means.

That brings me to the theme I’ve been thinking most deeply about over the past year: context. When we talk about context, we’re not talking about metadata or better prompts. We’re talking about decision-time understanding. Context is the combination of inputs, intent, constraints, history, permissions, exceptions, and outcomes that surround every real enterprise decision. It’s the difference between knowing what happened and knowing why it happened.

Most enterprise systems were built to store records. They were never designed to capture decision logic as it unfolds. That gap is now becoming the bottleneck for AI adoption.

This idea isn’t entirely new. We can look to the software development world for an early signal. Spec-driven development has emerged as one of the fastest-growing areas in AI, built around a simple insight: agents need explicit constraints to stay on rails.

In that world, specs become living documents. They encode intent, boundaries, and expected behavior, and they continuously evolve as agents act and learn.

What’s different now is scope.

Decision intelligence takes this idea beyond code and applies it to every enterprise workflow where agents interpret fuzzy human intent, make tradeoffs, and act in the real world. What’s emerging is a broader, more powerful abstraction: the Execution Intelligence Layer.

The execution intelligence layer sits between intent and infrastructure. It is where context is evaluated, decisions are made, actions are orchestrated, and outcomes are captured. It turns understanding into motion.

I wrote more about this in What’s 🔥 #464:

Specs are that policy layer in action. They don’t just apply to code, but can apply to every workflow where agents interpret fuzzy human intent and act on it. Once in place, specs become living documents that turn text into agentic action.

Aaron lays out the opportunity ahead and why context is bigger than we thought.

Jaya Gupta takes this one step further by naming the missing artifact. Agents don’t just need rules. They need access to decision traces that show how rules were applied in the past, where exceptions were granted, how conflicts were resolved, who approved what, and which precedents actually govern reality.

Agents don’t just need rules. They need access to the decision traces that show how rules were applied in the past, where exceptions were granted, how conflicts were resolved, who approved what, and which precedents actually govern reality.

This is where systems of agents startups have a structural advantage. They sit in the execution path. They see the full context at decision time: what inputs were gathered across systems, what policy was evaluated, what exception route was invoked, who approved, and what state was written. If you persist those traces, you get something that doesn’t exist in most enterprises today: a queryable record of how decisions were made.

and the kicker is this - why startups can win

Systems of agents startups have a structural advantage: they’re in the orchestration path.

When an agent triages an escalation, responds to an incident, or decides on a discount, it pulls context from multiple systems, evaluates rules, resolves conflicts, and acts. The orchestration layer sees the full picture: what inputs were gathered, what policies applied, what exceptions were granted, and why. Because it’s executing the workflow, it can capture that context at decision time – not after the fact via ETL, but in the moment, as a first-class record.

Together, these ideas point to the same conclusion: models don’t create leverage on their own. The execution intelligence layer does.

I’m seeing this across the board in a number of portfolio companies. As Aaron Levie put it, there is a much thicker layer above the LLM than was initially perceived.

The moat isn’t the query-response interaction. It gets much stronger when workflows are automated, when humans are pulled into decision-making, when exceptions are handled, and when systems learn from those outcomes to codify better behavior over time.

The last mile in the enterprise is always the longst. That’s exactly where the opportunity lies in 2026. As agent systems evolve into decision, execution, and learning engines, the value unlocked inside the enterprise will be massive. There is still a lot to build 🏗️ and fix to get there.

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

Scaling Startups

#the mindset of a winning founder

#solid advice

#🎯 focus on the goal of showing up day to day, embrace the grind

#

Enterprise Tech

#🤯 Nvidia announced a $20B Inference Technology Licensing Agreement with Groq on Christmas Eve. Groq last raised $750M at a. $6.9B valuation just a few months ago and is now “selling” for $20B. Here’s one of the better takes amongst the many on this deal

#more M&A rumors and this will continue into 2026 in a big way as market for observability, data infra, and security logging continue to merge - in world of AI just need it all in one place to derive proactive intelligence - following Palo Alto Networks acquisition of Chronosphere for $3.35B - who will Datadog buy to expand their security operations?

#social giveth and social taketh away - how not to respond from customer feedback - Coderabbit CEO - the mob can be brutal

#security like authorization and authentication at scale also desperately needed for this world (check out Keycard, one of my port cos)

#one of biggest breakthrough in robotics will be open data sets

#Nvidia platform

#what’s next for Israeli tech post Wiz ($32B) and Armis ($7.75B) cybersecurity startups - more startups of course! (Ctech)

Markets

#🤯 amazing outcome for everyone involved in Armis (RBC research)!

#the bullish perspective on economic growth

#👀

Here’s a prime example of a hot company with a killer team and big market opportunity as “Ex-Splunk execs’ startup Resolve AI hits $1 billion valuation with Series A” in a tranched round.

Once upon a time, tranched rounds were milestone-based. Hit X in revenue, unlock Y dollars at a higher price. Today’s AI special is different. A new investor wins a competitive deal by paying a headline price ($1B), while quietly investing significant dollars at a lower price. This reduces the blended entry price.

No milestones required. Two prices at closing. One declares market leadership. The other improves the investor’s economics.

The headline valuation for the fresh round is $1 billion, sources said. However, the company’s actual blended valuation was lower because of a multi-tranched structure. In this setup, investors purchased some equity at a $1 billion valuation but acquired the remainder — likely a larger percentage of the round — at a lower price. This novel investment approach has recently become popular for the most sought-after AI startups, investors say.

The startup’s annual recurring revenue (ARR) is approximately $4 million, two of the people said. The size of the funding round couldn’t be learned.

Founded less than two years ago, the startup is led by former Splunk executive Spiros Xanthos and Mayank Agarwal, Splunk’s former chief architect for observability. The duo’s partnership dates back 20 years to their graduate studies at the University of Illinois Urbana-Champaign. This isn’t their first collaboration; they previously co-founded Omnition, a startup that was acquired by Splunk in 2019.

Translation: a $1B headline on ~$4M ARR from a two-year-old company. Amazing founders, huge market, and a reminder of what “hot” really means right now.

While this can certainly feel amazing on the upside, founders just be forewarned. The downsides are obvious which include setting a bar that is perhaps way too high for you to realize before a next round stepup, and also increasing the 409a valuation in which employees are granted stock options. On the way up it doesn’t hurt as much, but if things ever get tricky, you may end up with lots of employees with options that are underwater.

I still prefer clean rounds. One price. One story. And, of course, being there first at Inception.

This trend is only accelerating for hot deals, but here’s my thinking 🤣 - it’s not about consensus vs. non-consensus, or obvious vs. non-obvious.

It’s about finding amazing technical teams first, at Inception, with a unique insight into a super early market. Before it is deemed hot 🔥 and especially at Inception, most folks yawn or don’t believe what could be.

So here’s how to look at it: it’s not the TAM you start with; it’s the TAM you exit with. For new markets, timing is impossible to predict. But if you use common sense and think long-term, you can sense how big something can be.

Let’s call it Intuitive TAM. Use this framework and perhaps you too will one day become hot 🔥 and have plenty of options.

BEFORE WE WRAP 2025

We’re nearing the end of the year and I may not write next week, so here are the top 5 posts from 2025. Thank you for being on this journey with me - here’s to an amazing holiday season with family and friends!

1. The Enterprise Agent Hunger Games - the agent wave 🌊 is just starting—but without the last mile of IT infra/security it’s all vibes and no production. 🧠⚙️

2. Tariff-ied 🤯 - Time to dust off that old Post-ZIRP Recession Playbook 📖
   [What’s 🔥 in Enterprise IT/VC #440 - Apr 5]

3. Can This Really Be the UI of One of the Hottest AI Enterprise Companies?
   [What’s 🔥 in Enterprise IT/VC #470 - Nov 1]

4. The $5M Line That Is Redrawing Inception/Seed Investing
   Your round size picks your investors. Your fund size picks your battles.
   [What’s 🔥 in Enterprise IT/VC #474 - Nov 29]

5. AI Native vs. Incumbent - speed to distribution vs. speed to innovation and when ZIRP-era 🦄 should just go all in on AI
   [What’s 🔥 in Enterprise IT/VC #452 - Jun 28]

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

Scaling Startups

#🤔

#still needs to driven by the founder with a team to help

#the jumbofication of inception/seed rounds 📈 as multistagers/mega managers continue moving earlier

#great benchmarking data and must read from Battery State of AI

Enterprise Tech

#what a historical time to be a founder but problem is bulk of revenue at moment is concentrated in top 20 startups…let’s see how this plays out in next 12 months

#speaking of - look at where value is starting to accrue, question is how durable for these next gen cos on right - from Daniel Dayan Battery

#the market prices of crypto show 📉 but what’s happening behind the scenes is fulfilling some of the original use core use cases of crypto - most of the largest banks called TradFi are pushing to tokenize assets - just the beginning!

#Cyera…almost the next cybersecurity decacorn - raises $400 million at $9 billion valuation in Blackstone-led round

#Cursor bought Graphite for code review - second order effects of AI generated code acccelerating is finally getting attention - code review, security reviews…according to Axios the purchase was mixture of cash and stock and way higher than the last round of $290M for Graphite - here’s a blog post from Cursor on this problem

The way developers write code looks different than it did a few years ago. But reviewing those changes, merging them safely, and collaborating on them has increasingly become the bottleneck for building production-grade software.

The team at Graphite has spent the past few years thinking deeply about these workflows and have built a code review platform used by hundreds of thousands of engineers at top engineering organizations. The boundary between where you write code and where you collaborate on it feels increasingly arbitrary, and there’s a lot we think we can build by collapsing that distance.

which I’ve written extensively about

#Andrej year in review a must read for all that happened in AI

#from Intercom’s co-founder - product vs. consulting…

#head of app platform? who will OpenAI go after next? Great to see founder of former boldstart portfolio co Handshake (sold to Shopify where he led product) continue to make noise

Markets

#Dan Ives from Wedbush Top 10 Predictions for 2026

#from Battery State of AI - great chart on what we all know, No AI story, no multiple expansion!

Future IPO candidates? Hello Clay!

Now let’s look at the metrics for the more traditional enterprise busines models as the end of year seems to be the time for everyone to just flex 💪🏻 a bit before the holiday season.

To set the stage, I encourage you to watch Alex Clayton’s Meritech markets presentation that he gave to LPs recently at the Emergence Capital annual meeting. Here’s the “AI is growing so fast chart” that everyone has seen, and what we still don’t know is how durable and sticky this revenue truly is.

What’s clear, however, is the winners receive outsized valuations which are significantly higher than the median across Series B to D.

Another way to hit these numbers is with a bottoms-up or product led (PLG) motion. Our boldstart portfolio company Clay which we’ve been partnered with since Inception grew from $1M to $100M in just 2 years. However, it did take some time, 6-7 years to be exact, before discovering its North Star. I’ve written more about Clay’s GTM here in previous issues, but Clay co-founder Varun breaks down its unplaybook playbook quite clearly below!

Here’s my TL:DR summary of Clay’s unique model.

6 contrarian moves:

1. Reverse demos killed friction - Product was unusable (8 calls for $200/mo). Let customers drive 30-min screen shares, solved their problem live, got instant UI feedback. Brought sales cycle to zero.

2. Brand before revenue - Bought Clay(.)com, hired claymation artist, made Head of Brand employee #18 pre-revenue. Felt crazy. Became a moat.

3. Usage pricing when everyone said per-seat - First GTM company to do it. Investors said they’re leaving money on table. Actually unlocked technical buyers and land-and-expand motion.

4. Cold email agencies as first customers → UGC flywheel - Agencies became experts, posted on LinkedIn, attracted clients organically. Self-perpetuating cycle. These agencies then became “Claygencies” and has been like an army of forward deployed engineers but not on its payroll.

5. Hired farmers, physicists, archaeologists, magicians - 50% of GTM/G&A doing first job. Prioritized passion/empathy/curiosity over experience. Taught mechanics later.

6. Created GTM Engineering category - Now thousands of GTME jobs exist. Hundreds of agencies built on Clay. First-time founders building 7-figure businesses on top. Community as force multiplier.

While Clay could have copied and tried to brute force its original platform and business model (a more programmable Airtable), it took more time to discover what its platform was built to do, testing and iterating and relentless trying lots of things to become the Clay that we know. Here’s a slide from the original Clay deck and more on how it found its way.

That’s the bottoms-up playbook. Now Glean did the exact opposite, and is also on a tear with $200M ARR, doubling in nine months. Zero PLG motion. Pure enterprise GTM - top-down sales, no viral loops, full stop.

Glean proves that AI-native scale works in enterprise GTM. ElevenLabs shows how explosive it can get when distribution and demand align perfectly as it added $14M ARR in one day 🤯!

To zoom out, here’s the market data from Menlo Ventures’ latest State of AI report.

Over 50 AI products generating over $100M ARR - I’m just sharing what’s possible for all of you - this is one of the biggest gold rushes we will ever see and yes, there will be some huge meltdowns along the way, but this opportunity is simply historic and just getting started!

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

Scaling Startups

#inception/seed - and the rounds just keep getting bigger

#40 hard earned Nos to raise his $1M seed round for 20% of the company 🤯

#it’s getting brutal out there - either you go big (multistage), go niche (specialize by stage/sector), or go home (game over)

#and the kicker for seed managers…

#hard to go long without turning some insanely lucrative offers; in this case, $1.2B sale and now at $8B valuation - not for everyone but worth noting…

Enterprise Tech

#great post from Jamin Ball on Systems of Record Evolving - 👇🏻 for full read

which 100% aligns with where i’ve been thinking for quite some time - read The Enterprise Agent Hunger Games #400 here…

#🤯 - must watch as design and engineering all together in code - can iterate in design and code visually - where it’s meant to be - what happens to Figma?

#how memory actually works on ChatGPT and it’s not RAG

#Peak AI already?

#Enterprise is a priority at OpenAI

Big Technology

Enterprise Will Be a Top OpenAI Priority In 2026, Sam Altman Tells Editors at NYC Lunch

OpenAI CEO Sam Altman told a room full of editors and news CEOs this week that OpenAI will prioritize selling AI to businesses in 2026…

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5 months ago · 22 likes · Alex Kantrowitz

At the lunch, Altman made clear that selling to enterprises was a massive OpenAI priority, and mentioned that it was an application problem, not a training problem, that the company needed to solve. Altman was straightforward about OpenAI’s need to build better products for enterprises and his intent to fast track them.

#but OpenAI still not yet ready to automate finance analysts - that’s some lemonade 🍋 stand!

#DevOps is not dead! Harness raised at $5.5B and at 250M ARR and port.io in internal developer portal space raised $100M at $800M - yes, both are positioned as hard core agentic platforms!

Markets

#it just took a quarter of a century but we’re so back 🤣

#what the Oracle stock drop means to the market and AI trade 👇🏻