Dave Wright

Skilled labor is the bottleneck in industrial ops. Elevāt's platform basically tries to “productize the expert” using machine signals + docs + prior fixes. Now they have $12M to scale. Congrats to them. In industrial AI, the tech is usually the easy part....adoption is the battle. Curious what you think is the real gating factor:  -- trust (techs believe it’s right and won’t get them hurt),  -- workflow integration (it fits into CMMS/ticketing/parts + works offline/on-device), OR  -- incentives (the field team is rewarded for using it vs “just fixing it their way”). Which one kills deployments most often in your experience?

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Excited to share the latest investment I’m leading for us at Eniac Ventures, a $6.5M seed round in Refold AI. If you’ve been inside a large enterprise tech stack, you know the “integration tax.” Every time you add a workflow, upgrade a system, or hit an edge case, a small army of consultants shows up to keep APIs talking. Enterprises spend $350B a year on this and the work is repetitive, brittle, and slow. Refold flips that model. Their AI agents learn how systems interact, generate and maintain the code, and adapt automatically as software changes. Multi-quarter service projects become repeatable, productized software delivered in days. Founders Jugal Anchalia and Abhishek Kumar lived this pain inside massive SAP environments, where a single field change could take days and six figures to fix. Their insight: integrations aren’t magic, they’re patterns. And patterns can be learned, automated, and scaled. Refold already works with 30+ paying enterprises, processes 30M+ API calls per month, and is growing ARR fast. This is not just cost savings, it is removing friction from how businesses connect and unlocking speed and innovation. We believe this is the future of integrations: fast, invisible, and autonomous. Excited to partner with the Refold team to kill the integration tax for good.

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There are countless conversations taking place across #TechWeekBOS, but this is one I'm extra keen to be a fly on the wall for. It will be an honest conversation with early-stage engineers about how they're *actually* navigating the AI tooling chaos/excitement. If you're building right now, you'll want to be in this room!

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Crunchbase highlighted today that $100M-$300M are well below 2021 levels. My prediction: this will become normal. In fact, I think sub $100M exits will become the norm. Why? Because the cost to build a company has collapsed. AI tools. Cloud infrastructure. Open-source software. Today, a small team can build what used to require tens of millions in funding. That’s incredible for founders. But it also means technology advantages disappear faster, and large companies are becoming less willing to pay massive premiums for startups whose tech can be replicated quickly. The result: more exits in the $25M–$100M range. But here’s what people miss: I’m actually more bullish on startups and venture capital than ever. Because founders now have more ways to win. A $25M–$100M exit can be an incredible outcome if the company was built capital efficiently. And the multi-billion dollar outcomes are still there for companies that create something truly new. The real question founders should ask early: What kind of company are you building? → a fast, capital-efficient company with a strategic exit path or → a revolutionary business that creates a new category and could become a multi-billion dollar outcome Both paths exist in venture. And importantly: A multi-billion dollar exit doesn’t automatically mean the company was venture-scale. And a smaller strategic exit doesn’t mean the company couldn’t have been. Different games. Different strategies. At Mastercraft Ventures, we back founders playing either game. Because great companies don’t start with unicorn valuations. They start with clarity about what they’re trying to build.

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Thrilled to share the launch of Criticality, a $65M fund built to back founders solving hard, high-leverage problems at the intersection of technical complexity and real-world urgency. Criticality builds on the foundation we’ve laid at Antler - applying our conviction-driven approach to companies at Seed & Series A. The fund is sector-flexible but applies a deep tech lens to each opportunity, targeting non-obvious, high-leverage problems from the earliest stages of company formation. Grateful to the New Mexico State Investment Council for their support in anchoring Criticality. More to come soon from Cash Allred & I.

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Amazing to finally be able to announce Zenyard out of stealth. This is a real breakthrough for the industry. Welcome to AI reverse engineering!

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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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ICYMI, here's what Scott Sanchez said about margins in the LLM market on our inaugural podcast last week. Recording again this week but here's the link to E1: https://lnkd.in/gie2Quu8

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Check back if you missed it! The world is changing around us and it makes sense to own a piece of it. Remember we have two entry points into deep tech: 1) go direct into the CR Fund 2) write a small check into AIM Company *Must be accredited. Sorry, government rules, not mine.

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There's a bigger hurdle to AI’s impact on biology than automation or compute. When Demis makes a bold claim or we hear about some 100x increase in a narrow bio problem, there’s usually a chorus of pushback that these aren’t really valuable additions. A lot of the pushback is that “biology is hard”, but this doesn’t explain why another 50 IQ points or 20h of task length can’t solve this kind of “hard”. I’ll try to be specific: What we call biology spans multiple physical 'layers of organization' with different behaviors and iteration speeds: Layer 1 = Molecular - e.g. drug molecules binding to proteins, which we need to make medicines. This is AlphaFold etc territory. For DNA, RNA, and proteins at least, we're good at simulating biophysics and experiments can happen as fast as arms can move things around. Layer 2 = Cellular - What's the response to a protein getting blocked? Some activity stops, the cell changes gene expression to compensate, and ends up in a new steady state (or maybe dies) minutes or hours later. New tech like CRISPR and single-cell sequencing apply here. Layer 3 = Organs - What happens to an organ when cells change? Often other cells get involved, and the new steady state might be destructive or ineffective. But this happens as gradual changes and responses over weeks to years. Many of the diseases we hope to cure live at this layer: Heart failure, dementia, aging… The key thing to understand, especially for non-biologists building AI for bio, is that layer 3 & beyond is emergent. The body is not the sum of all cells, thanks to feedback loops within and between cells. And with emergent complexity, scaling 1 and/or 2 alone does not mean 3 gets solved. AI thinking about biology is useful, for sure. And speeding up experiments in layers 1 and 2 helps disease research, for sure. Some diseases live at layer 2: infections, single-gene disorders, some cancers. And indeed the automation and scaling of layers 1 and 2 we’ve been doing for decades has given us decent answers to those already. But until we understand disease progression, any attempt to accelerate it (e.g. 'this protein is the problem, just put in a lot of it in the brain') risks creating a false goal. Neither more intelligence nor speeding up layers 1 and 2 can bypass the feedback loops of layer 3, which is where most diseases we care about actually live. This is the most important reason why we aren’t currently slated to double lifespan even if we get a ‘country worth of geniuses’.

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The Google and NextEra partnership makes perfect sense. For years, the model has been: 1. Find a site. 2. Ask the utility for power. 3. Wait for them to deliver or build it. 4. Complain when they're late and over budget. That model is broken. Utilities can't keep up. (and it's not their fault... "keeping up" with demand is an impossible task in this case). This partnership flips the script. Google isn't asking for power. They're bringing their own. They're partnering with the largest renewable energy developer in the country to co-develop entire campuses. Land. Power generation. Interconnection. All bundled together. This is vertical integration. It's Google taking control of their own destiny. And it solves the biggest problem in the industry right now: ...Who pays for the infrastructure? NextEra's CEO said it himself: hyperscalers need to "bring and pay for their own power generation." That's exactly what this deal does. It's faster. It's more certain. And it doesn't stick ratepayers with the bill. This isn't just another PPA. This is the new model for the future of data center development. My question is... Will this just be a behind the meter design? Or will they connect their power generation to the grid? Comment below!

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Yesterday, two DCVC-backed companies raised about a half billion dollars. In today's funding environment, that's not that exceptional... Together, the two companies just happen to enable gigawatt-scale AI compute, except in the unprecedentedly small space of only about 40 shipping containers, no exotic cooling required. You have to wonder what the US military might do with a gigawatt-equivalent of compute they could fit in a few C5 flights... or a 100MW-compute platform that fits in a single rocket launch... Even that, in DCVC's world, isn't that exceptional. We like to say we help make the impossible and essential, profitable and available. But what is exceptional, and this is a theme with us folks, is that they are each only two of many of our companies delivering robustly on outcomes considered impossible on VC dollars or in reasonable time, at the point when we backed each company from very early on: - a safe, cost-effective, meltdown-proof, truck-portable 1 megawatt nuclear reactor (Radiant, see https://lnkd.in/gF3JAHwS) for American energy dominance - American-made AI semiconductor chips that match or outperform everything else at 100x the energy efficiency, on off-the-shelf models (Mythic, see https://lnkd.in/gWFdvZFT) We were able to make the calls on these, and how they could work together, along with similar calls on all of their brother and sister impossible companies, because of a very deep bench of unified scientific, engineering, operational, and business building experience considered passé by a lot of other folks. It's how we make the exceptional routine.

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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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When I first met Kevin Surace, I immediately understood why his reputation precedes him across Silicon Valley. He’s been building at the frontier of technology for decades and is deeply respected by people across the industry. Kevin was working in AI long before the world caught up to it—pioneering early voice AI technology, building multiple companies, and consistently seeing around corners before the market arrives. He also ‘grew up’ in Silicon Valley with my former co-CEOs, Ben and Marc, from Andreessen Horowitz which made the world feel very small very quickly in the best possible way. 🌎🚀 I was introduced to Kevin by David P Bovenzi at Grand Oaks Capital when I was asked to help bring Token— a company that has spent more than a decade building cryptographic biometric identity assurance technology— into the market in a whole new way… In one conversation I knew I wanted to work with him— and Token, where Kevin is Chairman and CEO. AI has fundamentally changed the identity landscape. Attackers no longer need to “hack in.” They log in—using AI-generated phishing, deepfakes, and automated attacks at global scale and unprecedented speed. AI attacks will continue to be faster and cheaper than anything enterprises and governments have had to defend against before. In Gov and highly regulated industries, identity failure means more than compromised accounts… It’s ransomware attacks on hospitals, critical infrastructure disruption, supply chain shutdowns, financial system compromise, and nation-state intrusion. Identity assurance has become a defining infrastructure challenge of the AI era (Gartner is tracking this emerging category closely). Since joining Token’s mission earlier this year, we’ve relaunched the brand, launched the TokenCore product line, rebuilt the GTM engine, assembled a world-class team, and are bringing a twelve-year innovation into the hands of enterprises, the U.S. government, and allied global organizations who need it most. And I’ve had the privilege of working alongside Kevin every day. In just a few short months I’ve learned an incredible amount from him as a leader, inventor, and operator. As investors we talk about how the biggest breakout companies happen when the technology, the market timing, and the right team all come together at once. That’s what this mission feels like. 🚀 A great read on Kevin’s journey through the early days of AI, finding joy in the process, and lessons on leadership from decades in the trenches: https://lnkd.in/e-iy5iWY Dianne Liu Albert Yanez Khamvong Thammasouk Dan Van Ostrand Paul Wazowicz Christopher DeBaggis Johnny Lam Marcus Yanez Jack Jackson Michael Okland Kortney C. Steve Hunt Ann-Marie Klein FCMA, MBA Ever Wonder The Juno Group Elevate Talent Ventures Torrell F. Elevated Value Partners Monique Shivanandan Robert C.

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Enjoy my latest musings on how the application software industry is forever changing at the speed of AI. In this piece, I dig deeper into what I see as the eventual end of the $750B wasted annually by customers who are just trying to inject their own business context into the software applications they use. And perhaps, horizontal software isn't dead after all.

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Software needs California. Hardware not so much. The US is a large, diverse place and there are so many great places to build factories. You can start a hardware company in California, but don't plan on scaling it there. https://lnkd.in/gEkHc-BM

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Another missive from Chuck Cotter and Daniel Faierman. Thanks guys for continuing to help to ecosystem. If you've not seen their posts, peek at https://lnkd.in/gPiu9Z4z. Topics include: · CPG Co-Manufacturing Contracts · Early Stage VC Terms to Rarely Accept · More Dividends (and Dividend Recaps?) · 5 Raises, 5 Takeaways · A Founder's Guide to Secondaries · Standing Out Pre-Traction · The State of VC · Corporate Governance 101 · Anti-Dilution Provisions · Primer on QSBS · 4 Fund Raising DON'Ts · Liquidation Preferences · Understanding SAFEs · Understanding Convertible Notes · Understanding Prorata Rights · Founders & Athletes · Employee Stock Options Demystified · The Art of Start-Up Valuation · What Is A Term Sheet Anyway? Check it out ...

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AI is changing what “scale” actually means. In CTech by Calcalist’s 2026 VC Survey, Haim Bachar and Lee Ben-Gal share how we’re seeing the scaling metric shift - from headcount growth to output per person. This isn’t about doing more with less. It’s about building companies differently from day one - and rethinking how success is measured. See the full article below 👇

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My investment framework centers on 7 layers. CASIMIR fits into the first layer which is the Observation Layer. Out of 22 billion IoT devices on earth and ~10k satellites in space in 2026 we may have a 6 billion (and growing) ultra low power devices on earth and space that can one day run on MicroSparc 👍🏼 -> Eternal power chip Harold “Sonny” White + Capital Factory #HardThings #deeptech

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