Jake Saper

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.

@nmasc_ Yes! I'm interested in AI-native trade schools.

After having weeks of doomsday conversations with some of my smartest friends about AI displacement... I have to wonder: Why is there no standout edtech company synonymous with AI re-skilling? I'm sure folks are trying, but why hasn't there been a break out yet?

@dafrankel @MikeyShulman @suno Amazing. So excited for yall.

Last week, @MikeyShulman announced that @suno has surpassed two million paying customers and $300M in annual revenue. I would love to say I had a grand theory about where AI was heading when I was the sole investor in their pre-seed round in 2022. The truth is I’m largely tone deaf to tech trends, but I do have a reasonably good eye for founders. So far, much of the AI story has been about making engineers more efficient. Suno is about making music more experimental and accessible. That shift has a lot to do with Mikey. He studied physics at Columbia, earned his PhD at Harvard, and later taught at MIT. That kind of academic pedigree can produce a prima donna. Mikey is the opposite, combining serious intellectual firepower with the creative instincts of a jazz band leader. (Fun fact: he’s also an accomplished bassist.) As impressive as he is, I was encouraged by the chorus cheering him on. Hugo Van Vuuren and Nancy Zimmerman were emphatic in their praise. His co-founders and former @Kensho colleagues, Martin Camacho, Georg Kucsko, and Keenan Freyberg, were similarly lauded by every reference I checked. When people you respect harmonize like that, it’s wise to listen. In the early days, the team was experimenting with different ways to commercialize the engine that would become @suno. They were commercially ambitious, but also deeply committed to empowering creators, and early enthusiasm from creative customers gave confidence that they were on the right track. Major talent and Minor ego is a rare combination, but in this case has produced one of the most consequential developments in music since MTV.

@camillericketts @mlmanapat @emergencecap 😊

@mlmanapat @jakesaper @emergencecap Jake is one of the greats :)

I’ve loved talking to @jakesaper for years precisely for the clarity of thought he brings, and this post is a great example of that. So excited to be (officially) working together with him and his partners @emergencecap! emcap.com/thoughts/insti…

@mlmanapat 😻😻😻

13/ Or come build with us if you want to work at incredible depth and complexity for customers shaping our economy: rowspace.ai/careers

Excited to officially launch @Rowspace_AI — specialized intelligence for investors to make faster, sharper decisions by compounding their edge with AI. For the first time, they can turn their stores of messy, fragmented data into judgment and workflows that scale 🧵

@toddsaunders @dafrankel @BainCapVC @ToddKrizelman @nhglass @Olo @scalevp @emergencecap I had the exact same thought

@dafrankel @BainCapVC @ToddKrizelman @nhglass @Olo @scalevp @jakesaper @emergencecap Humbling coming from you :)

Who are the most value add investors, advisors and founders in vertical saas?

What no one is talking about that everyone should be talking about is how agents will remove the switching cost of SaaS providers, turning previously irreplaceable systems into carcasses of data repositories. That is when SaaS will really be hit.

Public software is down 40% in 12 months. The selloff has been indiscriminate. But it won't stay that way. When the market starts distinguishing again, the winners will be companies whose moats survive a world where agents do the work. We spent Monday at @emergencecap debating which moats actually hold up. Here's the spectrum we landed on. The short version: Seat-based pricing and workflow stickiness were the backbone of the last era. Both are eroding. The next era rewards outcomes, context, and trust. And the most important/enduring moats in the AI era and currently being slept on...

Cognition.com is the strangest redirect I've ever seen. I'd love to know the story here...

I think there will be more people and less automation in 3-5yrs than people currently project. @assembledhq is taking a pretty unique road here. They’ve built AI agents on top of older, repurposed call center tech like workforce management, so customers can dynamically get escalated to the right person when AI’s not the right solution.

One market I cannot figure out in a world of AI is customer support. You have: Sierra Decagon GigaML Parloa Forethought Kore Cresta All of these have raised over $100M. and then oldies like Intercom Zendesk Help me out, how does this market shake out?

(Cross-posting this from LI bc it seems to have hit a nerve, hopefully in a helpful way). ~~~~~ I'm mostly a buy (ideally at the startup phase 😉) and hold type of guy. I often struggle with when to sell, particularly names I've become attached to. Fortunately, AI is a great thought partner, which is why it's become core to my public equities trading approach. The prompt I use is cobbled together from a few others online. I've spent a ton of time testing different approaches and tweaking them for my specific needs. My current version is very long (20 pages), but the results have been strong so far, driving two sell and one hold decision that, so far, look like the right calls. Critically, the prompt addresses one of the major issues with most models: the sycophancy default. They all naturally bend bullish. Tell them you own a stock, and they'll find reasons why it's great. I've tried to mitigate this with aggressive prompt engineering. This includes adding phrases like "You are a senior buy-side equity analyst with a risk-manager mindset and forensic-accounting rigor." For fellow public markets investors using AI: how are you handling the built-in optimism bias? And what prompts have worked for your use cases? Here's a link to the prompt: docs.google.com/document/d/1PQ…

@koomen @HenrikTao @LudvigSwanstrom Precision over promises! emcap.com/thoughts/preci…

Vesence is a linter for lawyers. It's a simple idea, executed brilliantly by two formidable founders. Congratulations to @HenrikTao, @LudvigSwanstrom and @jakesaper on a great seed round!

@reflection_ai Congrats on the raise! And, more importantly, on the @JamesPelly get!

Today we're sharing the next phase of Reflection. We're building frontier open intelligence accessible to all. We've assembled an extraordinary AI team, built a frontier LLM training stack, and raised $2 billion. Why Open Intelligence Matters Technological and scientific progress is driven by values of openness and collaboration. The internet, Linux, and the protocols and standards that underpin modern computing are all open. This isn't a coincidence. Open software is what gets forked, customized, and embedded into systems worldwide. It's what universities teach, what startups build on, what enterprises deploy. Open science enables others to learn from the results, be inspired by them, interrogate them, and build upon them in order to push the frontier of human knowledge and scientific advancement. AI got to where it is today through scaling ideas (e.g. self-attention, next token prediction, reinforcement learning) that were shared and published openly. Now AI is becoming the technology layer that everything else runs on top of. The systems that accelerate scientific research, enhance education, optimize energy usage, supercharge medical diagnoses, and run supply chains will all be built on AI infrastructure. But the frontier is currently concentrated in closed labs. If this continues, a handful of entities will control the capital, compute, and talent required to build AI, creating a runaway dynamic that locks everyone else out. There's a narrow window to change this trajectory. We need to build open models so capable that they become the obvious choice for users and developers worldwide, ensuring the foundation of intelligence remains open and accessible rather than controlled by a few. What We've Built Over the last year, we've been preparing for this mission. We’ve assembled a team who have pioneered breakthroughs including PaLM, Gemini, AlphaGo, AlphaCode, AlphaProof, and contributed to ChatGPT and Character AI, among many others. We built something once thought possible only inside the world’s top labs: a large-scale LLM and reinforcement learning platform capable of training massive Mixture-of-Experts (MoEs) models at frontier scale. We saw the effectiveness of our approach first-hand when we applied it to the critical domain of autonomous coding. With this milestone unlocked, we're now bringing these methods to general agentic reasoning. We've raised significant capital and identified a scalable commercial model that aligns with our open intelligence strategy, ensuring we can continue building and releasing frontier models sustainably. We are now scaling up to build open models that bring together large-scale pretraining and advanced reinforcement learning from the ground up. Safety and Responsibility Open intelligence also changes how we think about safety. It enables the broader community to participate in safety research and discourse, rather than leaving critical decisions to a few closed labs. Transparency allows independent researchers to identify risks, develop mitigations, and hold systems accountable in ways that closed development cannot. But openness also requires confronting the challenges of capable models being widely accessible. We're investing in evaluations to assess capabilities and risks before release, security research to protect against misuse, and responsible deployment standards. We believe the answer to AI safety is not “security through obscurity” but rigorous science conducted in the open, where the global research community can contribute to solutions rather than a handful of companies making decisions behind closed doors. Join Us There is a window of opportunity today to build frontier open intelligence, but it is closing and this may be the last. If this mission resonates, join us.

Proud husband 😍 VERY happy wife ☺️

@hnshah almost all the boards I'm on now have an orphaned board seed, left by a partner who has departed the firm. linkedin.com/posts/jakesape…

Nobody seems to be talking about the amount of churn happening at venture capital firms.

@HarryStebbings It's called product-market fit.

You have something very special when all your customers use the same words to say why they love your product.

We're in a weird era of AI where amazing things are happening but simple things can still be surprisingly hard. Today, I've been trying to make a market map by uploading a spreadsheet with list of companies, URLs, descriptions, etc. I've tried OpenAI, Anthropic, Bolt, and nothing has been able to render the company logos properly. Ideas?