Jigar Shah

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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Honored and excited to partner with Drew Baglino and the incredible team at Heron Power as they re-imagine the electric grid for the modern world. Power in America is not zero sum. We deserve access to cheap and abundant electricity and we should win the AI race. And we must build the capacity to manufacture critical technology here at home. When faced with existential challenges, we deploy technology against them. And we win.

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From our PearX S25 cohort, meet Pravāh, the AI-native operating system for the electric grid When Mohak Mangal, Dhruv Suri, and Aman Gupta looked at the strain on the U.S. electric grid, they saw a system pushed to its limits. Exploding demand and unpredictable supply have made blackouts increasingly common. This costs the economy more than $150B a year, a number that could 10x in the next five years. Mohak Mangal and team founded Pravāh to help fix it. Their platform helps utilities and grid operators make real-time decisions on load, generation, and congestion, reducing blackout risks, optimizing power procurement, and bringing much-needed stability to the backbone of modern life. We’re proud to support the Pravāh team as they take on one of the toughest and most important infrastructure challenges of our time.

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I’ve known David Meister for years and backed him at Sydecar, so I’ve seen his ability to build through complexity. Axiom Trust is taking on one of the most outdated parts of wealth infrastructure, trust administration, by pairing a regulated trust company with AI-native workflows. We're proud to invest again and congrats to the team on the launch!

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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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"Build the event you wish existed" That's what we at Monadical did last week at #NYTW. We wanted an AI Engineers discussion for Engineers. There's always a lot of events out there for VCs to network, or for startups to learn about G2M, but very little on lessons learned from actual engineers in the field. So that's the event we hosted. Big thank yous to our awesome panel, Roy Pereira, Ben Cohen and Corey J. Gallon. Here's the key takeaways and the AI tools we're using. 🚀 All three panelists independently called AI Agents the most transformative LLM application they’ve used. They specifically called out Claude 3.5 Sonnet for its accuracy and reliability. 🪨 We dug into how LLMs are “jagged”, not general. They can be shockingly good at some tasks and completely fail at others. Everyone agreed: good evaluations are critical (and hard.) 🧪 Corey noted how public benchmarks and reality are two different things. Most public evals are saturated or gamed. ♊ Ben emphasized that AI projects are actually two projects: building the tool and building the evaluation process. 🧱 We explored how falling dev costs may impact startup defensibility and labor demand. Roy shared that founders are already shifting strategies in response. ⚒️ In a world of daily AI launches, the panel discussed how they decide what’s worth attention, and what’s just noise. They called out tools like Goose, Aider, Claude Code, and Monadical’s own Cubbi, which helps run agentic workflows safely in dev environments. (links in the comments). CTA: What would you want to hear in an AI Applied Engineering talk you attended?

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The data doesn't lie - brilliant deep tech founders outside Silicon Valley consistently face structural headwinds that have nothing to do with their technology or vision. Too many breakthrough innovations stall because founders can't find investors who understand long development cycles, lack access to operators who've navigated complex regulatory paths, or struggle to build the interdisciplinary teams hard tech requires. The science is there. The ambition is there. We're building the infrastructure to connect them properly. Details coming soon. 

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Martin Eberhard played a key role early at Tesla and even brought in Elon Musk. Then he was pushed out of the company for pennies. Founders get most of the attention in Silicon Valley, but the ecosystem is built on the people who take a chance and join early stage ventures. Unfortunately, those same people are often the easiest to cut out. Founders can dilute early employees, change vesting terms, and restructure equity with little effort and zero consequences (you can literally do it in Carta in 5 minutes). Daniel Kottke was a close friend of Steve Jobs and helped build the Apple I and II computers. He got no equity (Steve Wozniak eventually gave him a few of his own shares). Brian Acton co-founded WhatsApp and stayed through the Facebook acquisition. When he left Facebook over privacy concerns, he had to walk away from $850 million in unvested stock. There’s more awareness now around how vesting works, what happens during a liquidity event, and how to think about re-ups and dilution. That’s good. But the truth is, if someone really wants to screw you, they can. It still comes down to one thing: trust the people you work with. Or don’t work with them at all.

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With RL techniques like GRPO, it’s become easy to fine-tune small models to outperform foundation models on vertical tasks. We’re open sourcing Osmosis-Apply-1.7B: a small model that merges code (similar to Cursor’s instant apply) better than foundation models. Links in comments! Using foundation models for high specificity, low complexity tasks like code merging can be overkill - instead, it’s better to use a specialized model instead for higher performance and latency. We fine-tuned the model on 100K real-world code commits where the model needs to merge a code edit snippet into an original code snippet. On the test dataset (subset of CommitPackFT), foundation model performance ranged between 0.77-0.93 reward score. In comparison, Osmosis-Apply-1.7B achieved a 0.98 reward score - while also being 3X-5X more affordable than the next cheaper model AND ~10X faster! Why this matters: the best AI agents use multiple models to ensure task specialization and reliability at scale - on top of cost and latency benefits as well. Links to the model and full write-up (with reward function, hyperparameters, etc.) are in the comments - reach out if you’re interested in using reinforcement learning!

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Today marks a milestone for Salesforce Ventures, and a defining moment for enterprise security. Alphabet’s acquisition of Wiz has officially closed. Wiz has been a cornerstone of our portfolio since May 2021, when Nowi Kallen, Kartik Gupta, and team first invested in their Series B at a $1.5B valuation. We saw the potential early and invested deeply, backing the team through their Series C, D, and E rounds. From a $1.5B Series B to a $32B acquisition, one of the largest in enterprise tech history, the Wiz team’s execution has been nothing short of extraordinary. This outcome reflects our mission at Salesforce Ventures: identifying the companies that define the next era of technology and staying behind them for the long haul. But the real credit belongs to the entire Wiz team on the ground. Beyond the technology, it was the relentless grit and day one mentality of Assaf Rappaport and the team that turned a vision into a $32B reality. Huge congratulations to the entire Wiz team on this historic achievement.

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We Invested in Feltsense, Agentic Founders That Build and Scale Startups At Blitzscaling Ventures, we invest in founders building systems that can reshape how markets form and scale. Feltsense is exploring a new frontier led by Marik Hazan. They are building agentic founders that can create, launch, and scale startups autonomously, (yes, you read that right, AI agents building companies) we call this trend Autonomous Organizations. Historically, startups scaled through human coordination to scale. Teams hired people, built products, and expanded step by step. Feltsense introduces a model where AI agents can ideate, build, test, and iterate continuously. This has the potential to dramatically accelerate company formation and innovation cycles. We believe this represents an early signal of a broader shift. Software is evolving from a tool used by entrepreneurs into an active participant in entrepreneurship itself. If agentic startups can proliferate across markets through shared infrastructure, this may create compounding advantages in data, workflows, and distribution. This is a bold bet on the future of entrepreneurship. We are proud to support the Feltsense team as they explore what autonomous company creation can look like at scale.

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In an ecosystem flooded with service-first startups chasing scale through funding, Urban Company stands out by doing the hard (and right) thing—building a real business. Their recent shift from being a pure-play service marketplace to developing tangible, monetizable products is a playbook more entrepreneurs should study. Too many service startups stop at aggregation—thinking scale alone will drive defensibility. Urban Company went a step further: ✅ Leveraging deep consumer insights ✅ Creating integrated solutions ✅ Building new revenue lines rooted in real-world usage This is what scaling with purpose looks like. Instead of chasing round after round of capital to keep the engine running, they’re creating sustainable value by aligning product innovation with the trust and access they’ve already earned through services. For founders building in services: 💡 Don’t just deliver the service. Understand the gaps around it. Own the value chain. Monetize where others overlook. Urban Company is showing what it means to evolve with intent. A solid reminder that great businesses aren’t built on burn—they’re built on depth. #Entrepreneurship #Startups #Innovation #BusinessStrategy #ServiceBusiness https://lnkd.in/dzNHtpBS

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SF VC Takeaway #3: Early velocity is getting companies funded, but durability will decide who lasts. If the last couple years of venture rewarded velocity, many investors are already preparing for a shift back to durability as the early AI experimentation phase starts to come to an end. Right now, the market is still funding: fast revenue ramps, strong early momentum, and compelling AI-native narratives, but there’s a shared understanding that this is a unique moment in time. Series A rounds are happening so quickly after seed that many companies are raising rounds today without having to answer questions like: How durable is this revenue? What does retention look like after the initial excitement fades? Where do gross margins settle once pricing, usage, support, and compute costs normalize? Investors know they’re making investment decisions before these questions are fully answered, but they are still mindful that in the coming years the market and economics of these businesses will settle and mature. They can't miss the wave of current innovation, but that means they are investing with a higher degree of uncertainty. They are eyes wide open about that. Several people framed it the same way: “we’re underwriting velocity now, but watching durability very closely.” Over the next 6–18 months, many investors believe the market will transition from experimentation to expectation and from “how fast did this grow?” to “does this actually hold up?” When that happens, the questions that start to matter more are less about top-line speed and more about: 🏋🏽 Quality of revenue 🔁 Retention through renewal cycles 💸 Long-term gross margin structure This is especially relevant for AI-native companies where: engineering and GTM costs are more fluid, services (ahem forward deployed engineers) are often used as a wedge, and margins can either look great or terrible early on until scale changes their structures. The current market assumes the winners stay winners (often upheld by capital moats), but many investors are already watching closely to see which companies still look strong once the experimentation phase ends. None of this means velocity stops mattering, it just means companies will have a higher bar to raise mega-funding and will have to be both durable and high growth. My take (even though I promised no opinions) is that velocity matters right now, but in the not too far off future (6-12 months), the market will reward the right balance of velocity with durability and some of the more methodical companies will prevail in the long run. I think VCs will rather fund the high quality $2-3M ARR business than the uneasy $4-5M ARR business, but time will tell. That’s my final SF VC takeaway for the week. Next week, I’ll shift to what I learned from operators actually building AI-native companies and how scaling a company today looks much different than even just 1-2 years ago. #venturecapital #SFVCTakeaway

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