There are so many products I would buy if the vendor just said "I'll do all the work for you" Instead, they all just want to tell me why they are 10x better But not really help me: - buy out my contract - migrate my data (this is easier with AI) - onboard the agent At least try

A leather bag made from Tyrannosaurus rex cells is heading to auction in Paris, with an estimate that stretches past $500,000. phys.org/news/2026-06-leathe…

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 🙂

Google's philanthropic arm, Google․org, commits $50M to help train 300K skilled trade workers across the US, amid a shortage of workers for AI projects (@madisonmills22 / Axios) (Visit Techmeme dot com for the link and full context!)

Take this and run as fast as you can. You will not got a state pension. Cash it in now.

Today @Citadel has come out and covered the shift toward cheaper AI models This is squarely in line with my thesis x.com/Moshaikh/status/206471…

At this point every CEO should be asking what their strategy is to avoid model lock-in. If it isn’t clear what Anthropic is doing, it is: - build something amazing - decide who gets to use it after you prompt it if the prompt falls into areas they deem unacceptable by their sole standard To be clear this is completely above board and legal. It’s just an idiotic risk for corporate users to bear especially as the coding models become equivalent. The business continuity risk will become more obvious as companies accidentally trip over Anthropic’s ToS and have to decide if they will subsume their business viability to them by doubling down on Anthropic models or find open source (and, btw, much cheaper) alternatives where they are in control. As stated previously, get ready to be inundated with the term “control plane” which is the natural solution to this problem. Shameless plug - this is what 8090’s been building as we expected this moment to arrive… If you’d like to learn more: 8090.ai

£120m. That's how much @OpenAI (or rather, DeployCo) likely paid for Tomoro, their first AI consulting acquisition. Terms: undisclosed. But UK Companies House tells you almost everything so we did some digging 🧵

The pyramids were old data centres cooled by the Nile River. Good night.

What’s one archaeological theory that survives mostly because it’s taught, not because it’s the best explanation?

Bending Spoons just filed to go public with the SEC The Italian startup has snapped up a string of aging apps like Evernote, Eventbrite and AOL over the last few years. It generated $1.3 billion of revenue last year, and $601m in Q1

Marc Andreessen: We need to drastically increase the number of founders: "It's shocking to me how few people actually give entrepreneurship a shot. The fate of the world over the next 1,500 years is riding on the people who actually want to give it a shot." — @pmarca

Have been told that I have basically unlimited allocation for the SpaceX iPO which doesn’t strike me as a great sign.

SoftBank is promising to spend at least $52 billion on building a network of massive data centers in France, helping advance Europe’s goal of tech independence with what would be the continent’s largest AI infrastructure project. on.wsj.com/4nXCsvp

It's just a matter of time before prediction markets try to offer bets on public securities. SEC would be the bulwark, rather than CFTC, but unclear if this version of SEC would stand in the way. cnbc.com/2026/05/19/polymark…