Scott Jacobson

🚨NEW EPISODE: Recorded live at FUTURE TITANS 2026 - Jeff Perry of Carta sat down with the iconic Seth Levine, co-founder of Foundry. Seth has been in venture for 25 years, built Foundry from scratch as an emerging manager himself, and has backed about 50 emerging manager funds through his fund of funds. He has genuinely seen every side of this table. They went deep on building Foundry, why VCs are in the influence business, not the decision business, and why the concentration problem in venture is not only bad for LPs, but also for the innovation ecosystem overall. And why Seth's new book, Capital Evolution, is so important for the future of America. 🎧 Links to listen... Apple: https://lnkd.in/ehQUQ2EM Spotify: https://lnkd.in/eU4FExpg

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Every hire, product feature, and GTM experiment is a capital allocation decision, most teams just don’t call it that. I’m kicking off a 3-part series on capital allocation for founders, operators, and finance leaders with a simple question: ➡️ Will the next $1 you deploy become worth more than $1? Series overview Part 1: defines the core concept + the math Part 2: where capital goes + how to measure whether it’s working Part 3: AIMS framework for communicating allocation decisions to management teams and your board Part 1 (attached here) lays the practical foundation: 🔹 The $1 invested test and ROIIC vs. hurdle rates (scoreboard vs. decision lens) 🔹 A lightweight “investment brief” to evaluate bets (GROW / BUILD / BUY) 🔹 The small metric toolkit that translates finance into operating decisions (NPV / payback / ROIIC / incremental margin) 🔹 Early-stage proxies when DCF inputs are unknowable (burn multiple + revenue quality) 🔹 Operator “vital signs” to spot drift early (profit engine, leverage, cash conversion, optics) Series Roadmap ✔️ This series (Parts 1–3): breadth-first. shared, lightweight framework to define value creation, choose decision-grade metrics, and communicate tradeoffs clearly ✔️ Next series: depth-first. momentum drivers + operational decisions that need near real-time measurement to spot drift early and course-correct fast Read Part 1 here and Part 2 and 3 on substack. Next Series will be out next week. 💰 What’s one bet you’re funding right now, and how will you prove it’s working?

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Great conversations at our WillowTree Ventures dinner with leaders across fintech and AI. A few themes stood out strongly: enterprises are leaning into AI for real efficiency gains, product and engineering orgs still set the agenda, and the whitespace in “boring” industries is wider than most people assume. It’s clear that the next breakout companies in AI for financial services won’t come from the obvious places. Thanks to everyone who joined - excited to keep working alongside founders building in these non-obvious, high-impact wedges.

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I’ve spoken to over 2 dozen MDs at PE firms I can confidently say that the arb of figuring out how to implement Vertical AI at portfolio companies is very real right now It will fundamentally change underwriting for those who can do it predictably and unlock generational returns. Most are aware they need to act. Very few have.

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Thrilled to announce that Together Fund is investing in Sentra, alongside a16z speedrun! You track results in Jira. Decisions in Notion. Conversations in Slack. But the reasoning, the debates, trade-offs, and context behind why you chose A over B, disappears into what we call "Dark Matter." A decision made in March looks insane by July because no one remembers the constraints that made it smart. I lived this firsthand at Twitter scaling from 800 to 8,000 employees, and at Google while launching AI Overviews to billions at planet scale. The problem isn't process. Process is compensation for something deeper: organizational amnesia. An organization’s "Systems of Record" doesn’t solve this, they encode it. They store what happened, never why. That's why we are investing in Sentra. Sentra is the always-on collective memory that eliminates organizational amnesia by maintaining accurate context for all members and agents, functioning as an operational nervous system. It connects to every channel where work happens, meetings, Slack, email, code commits, docs, calendars, and treats them not as artifacts to search, but as living signals to synthesize. The fleeting and the permanent, unified into a memory that understands. The founding team is built for this: - Jae Gwan Park (CEO): Product-first founder, memory systems research at UofT and MIT - Ashwin Gopinath (CSO): Former MIT professor, created "Reflexion" (NeurIPS 2023), agents that learn from mistakes, 2x founder - Andrey Starenky (CTO): Early Vapi engineer, ex-IBM, built to process enterprise-scale data firehose Together is an operator-led fund. We invest in problems we've lived. This is one of them. Many congrats Jae, Ashwin and Andrey, we are so excited to partner with you! Read the full thesis: https://lnkd.in/gixj9cE4 Book a demo: https://www.sentra.app/ #OrganizationalMemory #AI #Sentra #TogetherFund #a16z #ContextGraphs

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While there has never been a more exciting time to be a founder building dev tooling or next-gen infra, it has also never been less investable at seed/pre-seed. I'm either really missing something or a lot of my peers are lost. As someone who has not just written, but also SHIPPED, about 75k lines of code in the past 6 months I can tell you that the evolution of how to build products has changed as much in the past year as it did in the entirety of 2007-2017. The complete rise and fail of frameworks, platforms, methodologies, etc... paved over and forgotten... that is of course except for the 1 company that gets a 1000x return from a wildly overvalued hyper-scaler or drunken growth stage investor obsessed with compounding at scale. Imagine a world where any seed investor in trends like Openstack, Hadoop, PaaS, etc all took a full loss on their investment. That's what we're looking at right now. I personally know of over a dozen well-funded seed-stage companies building in these spaces, with years of runway, scrambling to get acquired for a return of capital + several million personally while they're still relevant. If you're not seeing this unfold in front of you, you either aren't paying attention or you're satisfied playing the lottery instead of investing.

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We're only a month in but we're already seeing demand from clients for Benzinga's APIs. Platforms want better access to account data, trading infrastructure, and high quality data feeds (like Benzinga's Newsfeed API and their earnings call transcripts) to help them build a one stop shop for the retail investors of tomorrow to do their research. Thanks Clint Rhea and Michael Saad for the shout-out in the news article!

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🦄 $150M to improve tutoring and education globally. Preply just announced a new round, putting the company on a clear trajectory to become an iconic global marketplace. In a world obsessed with fast wins and volatile growth, some companies quietly beat their goals year after year — for more than 6 years in this case (as long as we’ve at Acurio Ventures been partners). A few learnings from this journey, relevant for founders and investors: Pick a growing market with an unsolved problem. 2 out of 8 billion people globally are learning a second language. Despite many options, outcomes are still poor. Our original thesis was simple: if you build the reference platform, everyone who wants to learn will eventually come to you. Category leadership matters. When we backed Kirill Bigai and Dmytro Voloshyn back in 2019 (together with Rob Kniaz), there were dozens of similar startups. Small details showed Preply had already built a superior tutor base and a scalable growth engine. Build a product customers love. Speaking a language and teaching it are very different things. Preply transformed the learning experience by combining a motivated base of +100,000 tutors with tools that actually drive outcomes. Never stop experimenting. Few companies maintain a strong experimentation culture as they scale. Preply’s DNA reminded me of Booking.com — enabling them to execute 10x better than most marketplaces. Great companies turn every change into an opportunity. From riding the post-COVID shift to online learning, to betting early and heavily on AI as Dmytro Voloshyn has excelled at. What once sounded like sci-fi is now reality. Great companies become talent magnets A company maturity can't be addressed only by looking at the revenue, profit or product. I like to see how much better they become at attracting talent and retaining it. Proud that Acurio Ventures made this possible and the WestCap team saw things as bullish as we do and are now supporting the next phase of Preply’s journey. Huge congratulations to the entire Preply team!!

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I have been thinking about the quick service delivery model ever since a little before UC's IPO. Here are some thoughts: 1. Density Math is Unforgiving But Solvable With Geographic Discipline Instant SLAs convert idle time into structural cost. Without synchronized demand in 2-3km micro-markets, every unfilled window becomes dead weight. Travel time that's negligible in scheduled models (20 minutes across a 3-hour booking) consumes 40% of a 60-minute SLA. No-shows and rework—manageable at 5% in scheduled models - become margin killers when platforms absorb travel, wait time, and redeployment costs. Contribution margins sink to 15-18% versus 20% for UC. Density isn't destiny - it's a choice about where not to compete. 2. The Disintermediation Treadmill Or the Trust Moat, Depending on Execution Top performers churn fastest—creating adverse selection. Rising CAC to backfill supply meets stalling transaction density. Disintermediation risk becomes trust moat when platforms layer value beyond transactions - Uber's injury insurance and loan documentation, Urban Company's brand premium that commands 20-30% higher rates. Partners stay not because they can't leave, but because leaving means losing systematic advantages. The winners will be platforms that professionalize supply and make the platform indispensable to that professionalized supply. 3. Quality-Speed Trade-offs Fracture Until AI-Enabled Training Closes the Gap Instant models collapse the quality feedback loop. Partners onboarded in 48 hours lack deep training. Escalations spike. Refund rates hit 8-12% versus 3-5% in scheduled models—a structural margin tax, not a scaling problem. Simulation-based training, real-time quality nudges, and predictive dispatch algorithms might compress onboarding without sacrificing standards, but evidence remains early. The platforms investing in training infrastructure now—when capital is patient—are making bets, not executing proven playbooks. This is where technology could stop being overhead and become moat. 4. Capital Masks True Demand But Reveals Willingness-to-Pay Ceilings Well-funded competitors subsidize SLAs and wages to manufacture density. Customers internalize 30% discounts as equilibrium pricing. Partners expect ₹800-1,000 per job. When subsidies fade, demand collapses and supply churns. Subsidy periods aren't wasted capital if used to run rigorous pricing experiments. Smart operators use growth capital to map elasticity curves, identify which micro-markets will sustain unsubsidized economics, and exit the rest. But contraction is organizationally brutal-the operators who structure incentives around profitable density rather than gross GMV will have the discipline to retreat when needed. In hyperlocal marketplaces, the winners won't be the fastest—they'll be the most deliberate. Build density, earn margins, then layer speed. The platforms with the patience to sequence correctly will own the category when capital discipline returns to vogue.

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A whammy of a simile from the inimitable VC Sammy Abdullah, on the abominable phenomenon of "let's vibe-code an in-house version instead of buying the SaaS solution": "Trying to build your own enterprise class software solution is kind of like BBQ: ask any pit-master how they make their BBQ and they will tell you exactly what to do. They'll tell you the type of wood, where the get their meat, how to make and maintain the fire, etc Why? Because you either won't do it (you're talking 18+ hours of monitoring a smoker), will screw it up, or simply won't have the fortitude required to do it again and again. So, while enterprise software companies in this environment are going to receive the "we're building this in-house" refrain, it's going to be your job to explain exactly what you do, and re-pitch the value of your solution." [Blossom Street Ventures update email, 2026-03-18]

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We got on a call with a firm that backs Fund I emerging managers. I was excited because this was a firm that could write a larger ($1M+) check and wasn’t afraid to back new fund managers. But that changed pretty fast fifteen minutes in, when the allocator said their minimum fund size is $100M. We're raising <$20M. *😞...not again* Overall, it was a great call from a Fund II prospect but not Fund I. And I get it, their check size is $10-20M and the math only works above a certain threshold, but it still sucks to hear it. But they're still calling themselves an emerging manager allocator. That's the structural disconnect nobody talks about. It's not unique to them either. Some of the largest institutional programs defining "emerging manager" draw the line at funds under $2 billion. Others call a VC fund "small" if it's under $500 million. $500 million... “Small.” These allocators weren't built for a <$20M fund and most never were. The "emerging manager" language reflects a real intention that the infrastructure just can't execute on at our end of the market. And the same gravity pulling VC capital toward mega-funds is pulling allocator capital the same direction. Everyone's math points the same way. So the smallest funds piece together LP bases from HNWIs, angels, and operators one check at a time. My peers are all navigating some version of this. What I've come to believe is that the real opportunity isn't waiting for the institutional infrastructure to catch up. It's in people who haven't thought of themselves as LPs yet. Operators. Professionals. People who just need the opportunity presented clearly, the education, and the time to build real trust before they're ready to move. That last part is the hard part. It's not that those people don't exist. It's that finding them and building genuine relationships takes a long time. There's no shortcut. But that's the work. And I think it's worth it. Who else is navigating this as an emerging manager?

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Proud to support Esusu as it enters its next phase of growth with a new $50M Series C fundraise! Wemimo Abbey, Samir Goel and team continue to lead the industry in credit building and economic mobility for millions of renters nationwide. Esusu (Founder Institute San Francisco portfolio company) is setting a new standard for combining economic and societal impact. By helping renters get credit (literally) for paying their rent on time, they are increasing financial access and empowering more people to become homeowners - in fact, they have already empowered over $30BN in new mortgages from renters across their system. I'm especially excited about how they plan to leverage their massive dataset to create even more impact. By making rental payment data a more prominent feature in mortgage underwriting, Esusu will be able to increase the number of people that can buy homes and build generational wealth. This is a truly inspiring company 🙌 Read more here: https://lnkd.in/guUKsGtY

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The double fee layer question for a fund of funds is easily answered: If you deploy $25m+ per annum into Venture you can hire someone qualified + travel expenses, etc. to build a fund of funds portfolio. Otherwise it's more economical to pay the fees.

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Most markets go barbell eventually - at one end scale, at the other end specialism. In banking you have GS, MS, JPM at the large end, then the specialists like Allen & Co and Qatalyst. It becomes harder to describe what the middle stands for. (Btw, specialist doesn’t always mean small - Qatalyst does some of the biggest deals in tech M&A. It means being really clear who your audience is and what you stand for.) In hedge funds you have Bridgewater, Renaissance, Millennium etc. then the focused specialists most have never heard of. In PE - AUM concentrates in the likes of Apollo, KKR, CVC et al. while lower midmarket specialists which trade AUM for potential upside are over subscribed. Even consulting has gone this way - McKinsey, Bain at one end, then at the other the more niche consultants which are deep in a specific domain. Talent and AUM aggregates to either end of this barbell. The middle slowly get hollowed out. Private tech markets are going this way too. If you’re stuck in the middle today, it’s worth being very clear in your own mind which end of the barbell you’re heading for.

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The old SaaS pricing playbook doesn’t work for AI. Madhavan Ramanujam and I partnered with Emergence Capital and Jake Saper to write a new one—built for where AI is going, not just where it is today. We lay out what state-of-the-art AI pricing looks like now—and what it will look like: ✅ Hybrid pricing (seats + usage) is the current best practice 🎯 Outcome-based models are the future—pricing tied directly to impact 💰 The best AI companies already capture 25–50% of the value they create As autonomy and attribution improve, outcome pricing will go from rare to expected. Founders who move early will win. Full read here 👉 https://lnkd.in/gwp6tDtp

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Capital formation isn’t just about fundraising; it’s about building trust through transparency and clarity. I just wrapped moderating a panel at SVB’s Venture Connect conference, joined by Erlina Yeo Terwelp, Geoff Taylor, Brian Chen, and Stephen Bluestein. The goal: an interactive discussion on how GPs can build enduring partnerships with LPs in today’s fundraising environment. Key themes and takeaways from the conversation: • Relationships are paramount: Provide access to multiple investment team members across levels. Authentic, consistent engagement is foundational to long-term partnerships.  • Show your fund math: LPs want to see how ownership stakes and exit expectations map to net returns—at the actual fund size. Scaling isn’t a strategy unless supported by fundamentals. Help LPs draw clear conclusions. • Transparency builds trust: Be transparent on valuation methods, revenue multiple assumptions, even succession planning. Consistency and openness go a long way. • Investing in venture: LPs are active, but the bar is higher. Two deliverables I’ll be focusing on in our next cycle: 1. Elevating the operations DDQ. Now the second most-viewed item in any data room, right behind the investor deck. 2. Ensuring our deck clearly communicates fund math. From ownership to exit to net DPI—at the fund size we’re marketing. This experience reaffirmed that transparency and trust-building are critical, especially when the fundraising environment is as bifurcated as the markets we’re investing in. Thanks to Silicon Valley Bank, Bobby Donnelly, and Mark Lau for the opportunity, and to everyone who joined the discussion! 

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Don't Build a Better Wheat Farm" - Why Defensibility Stakes Are Higher in Deep Tech Just published a new piece on my "Ignore the Confusion" blog, building on thoughtful insights from Eric Ver Ploeg at Tunitas Ventures about startup defensibility. Eric's core thesis: Too many startups pitch like wheat farmers - "huge TAM, slow incumbents, growing market, domain expertise" - but fail to think through long-term defensibility until it's too late. From a deep tech perspective, the stakes are even higher: ** Unlike software, deep tech founders must commit to defensibility strategies from day one - their funding depends on it ** Patent vs. trade secret decisions are often difficult to reverse and shape your entire competitive strategy ** Even "picks and shovels" providers (the tools that make industries more efficient) become commodities without proper moats The key insight that resonates: Defensibility can't be retrofitted. Whether you're building software or deep tech, your moat must be architected into the business model from the start. Thanks to Eric Ver Ploeg for sharing these insights on startup strategy and letting me build on his framework from a deep tech lens. Read the full post: https://lnkd.in/dEj_iF-Q #DeepTech #StartupStrategy #Defensibility #VentureCapital #Innovation

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Big milestone for Firmable. We’ve raised $14m Series A led by Airtree. Sales has moved through a few big waves: intuition-led, CRM-led, data-led. We’re now entering the next one – intelligence-led sales. The opportunity isn’t just better data. It’s turning that data into clear direction and action, without adding more work for sales teams. That’s what we’re building at Firmable: a foundation of trusted external data, layered with intelligence that helps sellers know who to focus on and when. Led by Airtree, this round supports our expansion across Asia and into the US – and accelerates the build-out of AI agents that take the admin work off sales teams so they can focus on what they do best. Proud of the team, grateful to our customers and investors. We’re just getting started. Read the exclusive in the AFR. https://lnkd.in/gr66uknb Leigh Jasper | Tara Salmon | Karthik Venkatasubramanian| Chester Thompson| Chath Widanapathirana

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