Hilary Mason

Even the most skeptical voices are starting to acknowledge the value of LLM generated code. Not as a replacement for deep expertise, but as a practical tool for acceleration, exploration, and iteration. When someone as famously disciplined about code quality as Linus Torvalds publicly acknowledges that AI generated code can be useful, it signals a shift worth paying attention to. The takeaway is not that AI writes better software than humans, but that good engineers are learning where and how these tools create leverage. https://lnkd.in/eFbNFqYN

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Leaders from the Massachusetts AI Coalition are working with their teams to drive impact across the Commonwealth. Here's a quick update for those interested in following along. - Beacon Taskforce: A Founder Starter Pack is being assembled from the feedback of 30+ founders. Consistent themes are present (fast/flexible capital, free work space, GPU credits, connections to founders/operators), making consistent solutions testable. Excited for the first Founder Starter Pack to go live in early H1. - Tavern Taskforce: Tomorrow's event is ON. Excited for Luke Winston-Almanzar, Co-Founder & CEO of Reservoir, Ryan Jacobs, Co-Founder & CEO of Agency Robotics, and Shilpi Gupta, Founder & CEO of her company will be speaking on the "Industrial AI Builders Panel." - Banners Taskforce: Heads down. Cranking away. Working on a launch of our first big initiative on April 1st. Remember folks: The weather in New England is a feature, not a bug. 😉

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A terrific summary of what is happening for, to, and because of AI. It is the economic gold rush of this era. Some claims will come up empty, others won't yield the desired result, but in the aggregate it will define how we live and work with massive implications.

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Question to those using LLMs every day: Which LLM work best for team collaboration and shared AI workflows? I'm personally split 50/50 between ChatGPT and Claude. But as we look to invest in an Enterprise account for Remento, I'm realizing I definitely have some blind spots... Our primary goal is to easily create reusable AI assistants that any team member can access, trained on our specific knowledge base. For example: 👉 Marketing copy aligned with brand voice 👉 Product feedback based on customer research 👉 Content strategy from audience data For those who've made this transition: what's working?

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Decision modeling is key. Intent + decision traces created by agents need to be modeled to create self learning or ever improving decision quality ( measured via outcomes), while the criteria is non stop shifting. Process modeling - aka crud on system of records will also get eroded and merged with decisions models being stored. This is where Agebt plane and human plane converge.

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⚠️ Founders - consider the below reasons to not use Forward Deployed Engineers (FDEs) to acquire your *first* customers. This signals (1) lack of PMF; (2) miscalculation b/w development and sales timeframes; and (3) mischaracterization of Palantir Technologies timing when they deployed their FDE model. 💡 At the early-stage, it's crucial to demonstrate ability to repeatedly acquire customers with limited resources - capital, team, & time - that supports the narrative of 'more funding will amplify my traction.' Bespoke solutions for one company demonstrates zero repeatability. These custom solutions will not carry over to other customers within the same cohort. 💡 AI accelerates engineering but sales, system integration, and stakeholder buy-ins are perpetually slow. This imbalance results in concentrating all your engineering resources into one company, ignoring other opportunities, and still not going live on an accelerated timeline. 💡 Palantir didn't start using FDEs until year 3 when they got their first *gov't* customer, not their first customer. Gotham was already built and had strong PMF. Gotham allowed Palantir to take GTM risk by using FDEs b/c if it didn't work, Gotham was humming in the background. TLDR: At the early-stage, repeatable customer acquisition is the holy grail. Don't let AI deceive you that it can accelerate human behavior like it has with development cycles. It C.A.N.N.O.T.

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Delve just got accused of selling fake compliance to hundreds of customers. The YC-backed startup, which raised $32M at a $300M valuation last year, is accused in a detailed Substack post of generating fabricated evidence of board meetings, tests, and security processes that never happened - then pushing those fake artifacts through rubber-stamp audit mills to hand customers a "100% compliant" badge. The alleged mechanism: Delve generates the auditor's conclusions before any independent review occurs. That's not automation. That's structural fraud. And the customers - companies that trusted Delve with their HIPAA and GDPR compliance posture - are now potentially exposed to criminal liability and regulatory fines. Delve denies the claims. But the story doesn't stop there. A separate researcher says they found gaping security holes in Delve's own external attack surface. A company selling trust apparently couldn't secure its own. This is the part of enterprise AI adoption that doesn't get enough attention: the compliance and security layer is where the real risk lives. Not in whether the model is accurate enough. In whether the infrastructure around it - the attestations, the audits, the trust pages - actually reflects reality. Enterprises are being sold the appearance of compliance. That's a different problem than being sold a bad product. You can test a bad product. You can't easily audit a fabricated audit trail. The companies that figure out how to build real trust infrastructure - not just the veneer of it - will own the enterprise AI market. The ones that fake it will be case studies. https://lnkd.in/eDusrhRY

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The billionaire is partly backing a new AI startup called Project Prometheus, has raised $6.2 billion in funding, and will take on duties as co-chief executive of the new venture. https://lnkd.in/eWHUUqhs

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The AI you're using today will be retired in 18 months. Not upgraded. Retired. Vista Equity just published what institutional investors need to know about agentic AI. They're polite about it. I'm not. Why are we still celebrating chatbots? 👀 Meanwhile: Vista's portfolio companies are deploying agents that plan, execute, and iterate—without prompts. Zero human-in-the-loop for 80% of workflows. The shift from "AI responds" to "AI operates" isn't theoretical. It's already priced into $100B+ of enterprise software valuations. But here's what the Vista article won't tell you: Autonomy without governance is a $10M mistake waiting to happen. The companies scaling agentic AI right now have: - Immutable audit trails (not optional) - Kill switches at the infrastructure layer - Human gates on materiality thresholds, not human gates on every decision You're either building the governance stack now, or you're explaining to the Board why an autonomous agent just leaked PII. Vista Article: https://lnkd.in/eMwAAsvF If you're still prompting instead of architecting, you're not behind—you're solving the wrong problem.

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Reading Metronome’s Monetization Operating Model, I kept coming back to one idea: pricing has become product. Software now delivers outcomes, not access. Yet most companies still charge as if they’re selling seats or licenses. That disconnect creates friction: for customers, unpredictability; for companies, stalled growth. The paper’s argument is simple but sharp—monetization isn’t a late-stage decision. It’s strategic infrastructure. Pricing needs the same ownership and iteration as any feature. Treat it like a surface that customers touch, not a spreadsheet buried in finance. If value is continuous and dynamic, pricing must be as well. That means product, GTM, finance, and engineering working from one system of truth. How many of us still treat pricing as an afterthought—when it should be a growth engine? https://lnkd.in/gnH7WzYf #Monetization #ProductStrategy #AI

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Trying something new this week - a studio industry roundup with my take on what matters. The studio model is no longer an experiment being run in a handful of places. It is infrastructure being built across geographies, sectors, and institutional types. 5️⃣ stories from the past seven days:  1️⃣ OSS Ventures hit a EUR 40M first close on a EUR 75M follow-on fund -- capital raised specifically to back the 30 companies their studio created since 2019. The "build then fund" lifecycle is maturing. B'More Venture Studio with a similar thesis: studio operations first, then dedicated capital to scale what's working.  2️⃣ Delta40 raised $20M from 54 investors across 13 countries to launch Africa's first integrated venture studio and fund. The LP base is almost entirely DFIs and foundations. Impact capital is discovering the studio model as a deployment vehicle, something I seeing across geographies (conversations with Michael Bob Starr and Damias McDonald this week alone) 3️⃣ Start Holdings launched Start.vc out of NYC and Barcelona after a $200M EV infrastructure exit. Another operator-turned-studio-founder. This is the pattern we see quite often in our work at 9point8 -- experienced operators like Jason Goldsmith who've built and exited deciding the studio model is how they want to build next.  4️⃣ UBC HATCH admitted 10 new deep-tech spinouts for Winter 2026 -- mostly climate tech. University venture builders are quietly becoming one of the fastest-growing segments. Follow Evan Allen and Jaimie Testai to keep track of its rapid evolution.  5️⃣ The INVEST Act is advancing through the Senate. If passed, it expands qualifying VC fund size from $10M to $50M and loosens general solicitation rules for accelerator and university events. For studio-funds -- and for folks in the The Venture Studio Forum community tracking policy -- this could be the most significant capital formation legislation since the JOBS Act. If this roundup format is useful, I'll keep it going. What other noteworthy news hit your desk this week, and what stood out to you?

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