Jake Saper

Everyone's asking what AI can do for their job. We asked if it could teach a 6-year-old. Introducing Ello 2.0: reading, math & more for ages 4-9. Free tier for all. If we want AI to force-multiply humanity, let's start with teaching our youngest.

This is an opportunity to apprentice with one of the most intense growth-stage CEOs in the game. If you’re interested in starting an AI-native services business down the line, this is the best training role available today.

@austinh___ The beta feedback has been so awesome. Stoked to see it unleashed!

We built Claude for outbound sellers. AEs & SDRs can harness GTM engineering through chat across 40+ data sources, no technical skills required. We’ve had 57,548 queries in our first few weeks of beta, growing 45% w/w. The teams winning outbound in 2026 are already on it. Self-serve now live here: app.unifygtm.com/?screen_hint=s…

This is prob the most reflective/personal pod I've done. @nihalmehta is such an authentic interviewer/human.

the best public market comp for deloitte had its single biggest drop in history today...

@HenryYin_ Congrats!!! Excited to see the amazing things yall do with it

Most AI investing happens downstream of the frontier: a capability emerges, a category gets named, and capital rushes in. But by the time a category earns a clean box on a market map, the best builders have usually been living in the messy version for months. Agents. Reasoning. RL environments. World models. AI for Science. Recursive self-improvement. I call this frontier proximity: the ability to see what is becoming possible before it becomes consensus. My frontier proximity ladder: L0 Wrapper: uses today’s models. L1 Reactor: reacts fast to releases, but roadmap is downstream. L2 Anticipator: builds for where capabilities are going. L3 Native: depends on a non-obvious frontier bet. L4 Shaper: helps move the frontier itself. The point is not that every company needs to train models. Apps can have high frontier proximity if they understand what models will make possible next. Infra can have high frontier proximity if it knows what future agents, multimodal systems, robotics stacks, or scientific workflows will need. That is why we’re launching MoE Capital. MoE stands for Mixture of Experts. The idea is simple: build an AI fund around people closest to the frontier: frontier researchers, technical founders, AI-native builders, and seasoned operators. We don’t want to be another AI fund with a newsletter-level understanding of the frontier. We want to build the AI fund closest to the frontier. More in The Information: theinformation.com/newsletters/ai…

@LeoMehr Very cool. Worth reading @alexrkonrad’s write up on the move to services that @GainsightHQ is making. We’ll continue to see more of this. upstartsmedia.com/p/exclusive-ga…

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 🙂

@thattallguy @nrmehta It's early days so no one is at true scale yet. But we're seeing growth stage AINS businesses able to sustain low 70% GM.

@thattallguy @nrmehta Yup. Biggest risk of the whole AINS proposition.

10 Things I Learned at Emergence Capital's AI Native Services (AINS) Summit: In the world of enterprise AI adoption, the last mile is driving change. One approach is to sell agentic software to companies and to help them change (eg via FDEs). Another is to BE the service provider and use agents to win. My friend @jakesaper put on an excellent (totally packed) event on this topic. My learnings: 1. Faster/Better/Cheaper: I like Jake's definition - AINS is about becoming a service provider that uses agents to win in one of those three dimensions versus tradition firms. 2. Sell Outcomes: In software, the customer owns the outcome. In AINS, the vendor does. 3. Gross Margins Have to Eventually Be Software-Like: This is a big unknown but is the bet. 4. Get Better with Models: When something like Fable 5 comes out, software companies get scared about their value being eroded - AINS firms get excited about the new leverage. 5. Mirage PMF Is A Risk: One challenge is an AINS firm throws people at the problem, grows fast but then never gets the leverage through agents. 6. McKinsey+Stripe: It's hard to be both a services and software firm - you need "builders" and "doers." 7. Frenemies: Many firms start by delivering part of a service and working through legacy service providers - with the goal of owning the whole process. 8. Users = Employees: In AINS, the software is used by the firm's employees. So the loop of improvement is faster. 9. Repeat Founders: Sometimes it gets boring to go after the same software category over and over again. But now, a repeat founder who built a software business can go after a TAM 10X bigger (due to labor capture). 10. Are They Winner Take All: My biggest question - services industries (eg legal, accounting) are often heavily fragmented. As such, they don't produce breakout, venture-scale winners in most cases. Will AINS make this different and in which spaces? Awesome event!

@CorinneMRiley @DannieHerz @GreylockVC Before!

@DannieHerz @GreylockVC honored to be next to @jakesaper

Congrats, @CorinneMRiley, on making the Forbes Midas Brink list, a recognition for the next generation of investors poised to shape venture. We want the world to know what we see every day: Corinne brings relentless support to every founder, rolling up her sleeves on whatever the company needs, big or small. She backed @baseten, @usebraintrust, @resolveai, @cogent_security, @FableSecurity, and @altaratech before the rest of the world caught on. She has also built platforms that serve founders well beyond her own portfolio, from Greylock Edge to the Scouts program to Greylock Change Agents. Her energy is unmatched. None of this happens without the founders who trusted us as their first believers. They are the reason this recognition exists, and we are deeply grateful. So proud of you, Corinne.

@alexrkonrad This is the first of what I predict will be many major SaaS businesses shifting to AINS. Great job capturing the shift. And big props to @chuckganapathi and @GainsightHQ for leading.

This is a story I've wanted to write since I first saw VC @jakesaper post about a mysterious walk with a pivoting CEO 3 weeks ago. He's beaten the drum about AI-native services for more than a year now, and backed it up with an intro and some thoughts. linkedin.com/posts/jakesape…

Exclusive: Gainsight is shifting its business to go all-in on AI-native services. I spoke to CEO Chuck Ganapathi about the move -- and what conditions he thinks can make for a successful transition from Software-as-a-Service (SaaS) to Service-as-Software (SaS) in the AI era.

@tuhinone LFG!

5/ Stop selling the seat. Start selling the result. Full piece on when this transition makes sense, what changes, and why waiting is the bigger risk: emcap.com/thoughts/shoul…

4/ The next Salesforce won't sell CRM seats. It'll just run your sales ops. The next Workday won't sell HRIS licenses. It'll deliver fully managed people operations. The winners in the next decade may not sell software at all.

1/ Two weeks ago I took a walk with the CEO of a SaaS company with hundreds of millions in ARR. He told me he's pivoting the entire business to a services model. Can't share who yet. Soon.

Okay this one seems real. First time ever an OSS model beats Sonnet 4.6(!!) on our evals. Now begins vibe testing, but this is promising.

the best American open-source model ever just dropped, and it costs less than $1 per million tokens i feel like more people should be talking about this

We raised a $27M Series A to replace the spreadsheets and human duct tape behind $100 trillion in global assets. Fund administration is the invisible backbone of private equity and venture capital - and it’s broken. Why? Financial data is scattered, stale, and locked inside legacy providers. Books take forever to close. Basic questions about your own fund take days to answer. So we rebuilt the general ledger, waterfall engine, investor portal, and portfolio management from scratch. One single source of truth for your firm. Our AI agents read emails, propose journal entries, and extract portfolio updates in seconds. Our CPAs review every output. Today, we administer $15 billion in assets - and we’re just getting started. Every fund CFO keeps getting asked: how will you adopt AI? Now you have an answer. Run your firm in real-time with @hanoverpark. –- Excited to partner with Jake Saper at @emergencecap @peterjhebert at Lux, @chadbyers/@pratyushbuddiga at Susa and CFOs at the largest private equity firms to forge this future.