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Services are the future. Today we launched Ramp’s AI services motion. It's easy to buy an AI subscription. It's hard to transform your company to actually run on agents. Here’s our entire strategy. 1) Why now Services are the new software (Sequoia) Human labor TAM >> software license TAM. The market is bearish on seats and subscriptions. Every enterprise AI company is doing this -- the labs have poured billions into services partnerships and their own deployment functions. Superintelligent models alone are not enough. Palantir proved this is a strong business model: deeply embed engineers, build on top of a powerful platform, and customize extensively. 2) The real problem Companies want AI. But the gap between "we have AI tools" and "agents run our workflows and we spend way less time" is enormous. What we've found across over 50 companies we engaged with: agents start replacing real work when there is: complete data, read/write access across systems, agent-friendly policies. Most big companies struggle because: - processes live in operators' heads - dozens of disconnected systems (legacy ERPs, endless one-off excel sheets, etc.) - archaic software with poor or no API access Good data in the right place is a hard prereq to working agents. Also, vibing in localhost ≠ a production system your enterprise can rely on. You still need hosting, ci/cd, observability, feedback loops, good interfaces. And taste to know what's even worth automating. Everyone has a bulldozer, but most jobs just need a shovel pointed at the right spot. What companies usually need is to be made agent-friendly. That's exactly what we do. 3) What we do We focus on what Ramp does best -- finance. And we embed FDEs that: -> understand your problems -> identify high-leverage, high-impact workflows that fit agents -> scope the solution -> connect your data -> capture your context -> deploy agents and often bespoke software for humans to collaborate with them -> drive the business metrics that matter Discovery and scoping are crucial. Building is easier than ever and thus judgement about what to build is more important than ever. We're not a generic AI services arm, we're finance domain experts. Across the spectrum of financial operations, we help companies find and frame the problems worth automating -- similar to the taste a founder has in choosing which problems are worth solving (ex-founders make great FDEs). Here’s the stack we deliver: - Production infrastructure. Shipping an index.html from Claude isn't the same as creating a repo, hosting in a cloud service, ci/cd, testing, setting up evals, managing memories and skills, adding feedback loops, ensuring uptime, incident management, etc. Agents don't one-shot production systems yet. Production software is hard -- we build, host, and run it for you in a single-tenant, dedicated cloud environment. Most operators don’t have the time, knowledge, or experience to do this e2e. We help abstract the low-leverage plumbing so they can focus on the essential parts of their jobs. - Data connectivity. Most enterprises have data lakes, but data is often incorrect, stale, or entirely missing. And write interfaces vary dramatically. Ideally we can use MCPs or CLIs, but usually it’s poorly documented APIs, SFTP, manual uploads, and email. - A context layer. Things people have done for years aren't written down, so an agent can't do them until we capture that context -- ranging from simple policies to complex decisions. This usually involves creating policy documents, shared agent memories, and skills. - Evals and feedback loops. How you know an agent is doing a good job, and how it improves over time. 4) Why Ramp AI Solutions We focus on finance because it’s the vertical we know deeply, have structural advantages, and are most differentiated: - Data. 70k customers use our core product, over $200B in annual payments, years of vendor data, millions of transactions and bills monthly. - Money-movement primitives and partnerships. Global money movement rails, partnerships with banks, Visa, Stripe, etc. You don’t want to vibecode international wires for bill payments. - An intelligence layer on top: fraud detection from hundreds of millions of expenses, PO-to-invoice matching, state-of-the-art OCR, and fine-tuned models for accounting coding, spend routing, policy review, etc. Unlike the labs, we’re not incentivized to sell tokens. Ramp is an AI fiduciary and an impartial broker to deliver AI that is: - model-agnostic -- we benchmark all the leading models (labs, open source) and fit the right one to each task - and token-efficient by design Our main incentive is business outcomes -- which is Ramp’s mission, to save our customers time and money. I’m extremely bullish about our motion, and the broad industry growth of AI-native services. If you're a finance leader trying to be more agent-native, If you’re interested in joining our FDE team, I’d love to talk 🙂

What cannot be measured cannot be improved! As @mecadoinc shows in our work with @huggingface, there's a whole lot of work to do to create AI systems that really work for mechanical design. And these are just single parts - no requirements or assembly context!

Most companies raise to defend what they built. We raised because customers keep pulling us further. Accounting firms weren't on our core roadmap 6 mo ago, it's a $150B industry. AI spend mgmt didn't exist a year ago. Europe launches this year. Demand isn't the constraint. We're trying not to be.

This is the bet we made when we started AC. We work with our customers to train specialized models to be on the pareto frontier.

This is exactly right. People are starting to look for cheaper model alternatives and realizing two things at once: open-source models are already very good, and the ability to train and serve them efficiently at scale can change the economics pretty meaningfully. Tokens are still being subsidized, demand is ramping quickly, and the compute crunch is likely to persist. That will push companies toward using the right model for each task instead of defaulting to the most expensive one. We’re still early, but I expect open-weight adoption to accelerate much faster than most people think.

Last week I was so proud to see Forbes recognize my twin brother @apartovi and @Neo as the #2 seed investor on its global “Midas” List. And this was *before* Cursor and Kalshi’s current level of success. Next year #1. 🤞 forbes.com/profile/ali-parto…

Genetic engineering in human embryos is here. Today, in a world first, @Columbia and @nucleusgenomics announce high-efficiency editing of human embryos. The study, led by Dr. Dieter Egli's lab at Columbia University, with Nucleus Genomics’ Dr. Nathan Treff as a senior co-author, achieved editing efficiencies of up to 100% at targeted loci. Simultaneously, we showed no detectable editing-induced chromosomal abnormalities and low off-target activity. In other words, this is the closest we've come to practical, high-precision gene editing in human embryos. We are also excited to announce we will be funding and participating in the next phase of this research, alongside Columbia and Dr. Egli. We see ourselves as a natural pathway for eventually bringing technologies like this into clinical care as part of a broader genetics platform — a full "Genetic Optimization" stack. @nytimes broke the news in what is a historic moment for Genetic Optimization. See story in thread.

if your entire company doesn’t dress up as you for your birthday, they don’t love you as much as cognition loves @ryanbai1412

This simple image is absolutely brilliant. It shows how you can fit the capacity of a Toyota Tacoma in the footprint of a Mini Cooper.

Although LLMs aren’t conscious, this isn’t an intrinsic limitation of the transformer architecture Natural language and video/audio are only a small slice of cognitive output, which also includes motor control, emotion, hormone regulation, etc. So learning a generalized representation of this slice is unlikely to precipitate anything close to human consciousness However, consider a model defined by the following Inputs: current neuronal state sensory inputs Outputs: neuronal state at the next timestamp Loss: comparison of predicted neuronal state against the ground truth A sufficiently strong model with this parametrization should be able to explicitly model the interior process and state of human consciousness, albeit via a different underlying mechanism. If consciousness is empirically determined by neuronal state and a functional model of consciousness holds, the quality of consciousness should be indistinguishable from that of a biological human

When President Trump’s Q1 financial disclosures came out, many people assumed he was actively trading individual stocks. It turns out he was most likely using some form of an automated, managed strategy like direct indexing. Our team helped Bloomberg’s @justinaknope investigate the nature of the trades. She published her findings in an article that came out a few days ago, linked in the thread. Here’s the back story. President Trump’s recent financial disclosures included thousands of securities transactions in Q1 2026, including 3,642 equity transactions. Reporters and commentators quickly raised questions about whether the president was personally trading individual stocks. During a White House press briefing on May 19, Vice President JD Vance was asked about the trades by @AndrewFeinberg of The Independent. The VP rejected the idea that Trump was personally placing trades, saying, “The president doesn’t sit at the Oval Office on his computer on his, like, Robinhood account buying and selling stocks. That’s absurd.” Eric Trump later posted on X that the president’s holdings were maintained by third-party advisers through “automated, model-based portfolios and direct indexing strategies.” Our own analysis points in the same direction. A typical direct indexing account on @frecfinance can generate hundreds or even thousands of trades in a quarter, depending on the index, account size, cash flows, tax-loss harvesting activity, and rebalancing needs. We also saw patterns that looked consistent with tax-loss harvesting, including sales around market drawdowns. So there we have it. POTUS most likely uses direct indexing. And with Frec, direct indexing is no longer just for institutions and the ultra-wealthy.

Today, Ramp raised $750M at a $44B valuation. Last time we grew this fast, we were 1/20th the size. For 2000 years, business was built on two pillars. Today, a third: intelligence. It’s your least governed cost. It’s also your single greatest opportunity.

Today we are announcing our collaboration with Pfizer to put Chai's frontier AI—including our latest model, Chai-3—directly into the hands of one of the world's leading pharmaceutical teams.

One year ago today, we launched Nucleus Embryo. The biggest leap in embryo screening since IVF began. At the time, IVF patients were choosing their future baby randomly — with no health information. Today, Nucleus Embryo is one of the fastest-adopted technologies in modern fertility care. In just 12 months, we've expanded to 165 fertility clinics globally, kicking off an international patient-led movement on genetic optimization. What a privilege it is to serve our patients, and help couples all over the world build generational health. Learn more below.

I’m delighted to announce @chaidiscovery's collaboration with @pfizer. Their scientists will deploy our AI platform to accelerate drug discovery, including early access to our latest frontier model Chai-3. You can learn more about this partnership and our momentum in @amyfeldman's feature in @Forbes out today forbes.com/sites/amyfeldman/…

If you use conductor, codex, claude, devin, etc you can now use them all from one place AND get builtin devin review IDE Windsurf has evolved into Devin Desktop

Introducing Stack. The AI operating system that lets accounting firms take on more clients without hiring. Learns your firm's process, runs the close, posts the journals. Fully auditable. We’re living through the biggest shift in accounting since the spreadsheet.

Search agents have no explicit belief state or value function. I think that’s why long-horizon agents degrade and test-time search saturates. A few small experiments and thoughts: shreshthrajan.com/search-age…

Cognition’s $492M run-rate, your personal property manager, & more | Neo & Noteworthy Check out our May recap of learnings and announcements by Neo portfolio companies and community members 💙 open.substack.com/pub/neo/p/…