Daniel Low

During my TechCrunch Disrupt roundtable yesterday, I shared 5 key lessons founders and GTM teams need to keep front and center when it comes to selling AI agents into the enterprise: 1. Don’t get caught up in acronyms. Labels like B2B, B2C, B2B2C or B2C2B or PLG can be confusing. All that matters is identifying your customer and user and understanding if they’re the same person or not. If they are - the best product can win. If they are not, you have to have an engaging product, but you need an even more engaging relationship management strategy. 2. Look for a champion, not just a buyer. Even in large enterprises, you’re selling to people, not just roles. CTO, CHRO, CFO - each has unique motivations, budgets, personalities, and mindsets. Tailor your pitch accordingly and aim to get their personal buy-in, it’s the gateway to organizational buy-in. 3. Business model innovation may open the door, but it’s probably not how you’ll get paid in the long run. Lots of people are talking about using headcount budget for agents… but they’re not. Sell for how people buy software today, but build for how they’ll buy it in the future. Once you demonstrate value, you’ll have a lot more leverage in pricing strategy. 4. Plan ahead for procurement. Procurement processes often rely on AI governance policies that don’t exist yet, leaving you in purgatory for months or even years. Save time by anticipating this - use existing templates and borrow playbooks from other vendors that have successfully navigated the process. 5. Think about your “other ICP” - your ideal channel partner. People prefer to buy from vendors they already know and trust, aligning with existing partners helps build credibility and cut down on procurement time. Can you embed yourself with a 1:many opportunity that your target customers already know and trust? It was a great conversation with a lot of great questions! The intersection of AI and GTM strategies is exciting. Nobody has all the answers, but success requires balancing technology with a deep understanding of people and organizations.

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NYC is losing young technical founders to SF — and it’s not about vibes. It’s about cost of entry. SF has far more entry infrastructure than NYC: i.e. the housing, community, and early support (financial and otherwise) that lets founders start building before they raise capital. This is widening the gap between the two cities' tech sectors. We are specifically calling for ideas (and hopefully action) to address NYC’s dearth of hacker houses: physical spaces that combine housing, workspace, community, and early peer support. I’m convinced that seeding hacker houses, if done right, is a low-cost, high-impact way to attract more young technical founders to NYC — and critically, something this community can actually do. And we should. The data is stark. Venture investment in NYC is ~19% of SF’s total — the lowest since 2017 — and the biggest, most innovative companies (especially in AI) are overwhelmingly being built in San Francisco. People who want to be entrepreneurs tend to work where those companies are. Unsurprisingly, many students and young builders assume they must go west. But do they actually want to — or is it just easier to get started there? I spent significant time in the Bay last year, and one difference stood out immediately: SF is dense with hacker houses and founder residences that help people get from 0→1. These resources are especially critical for recent grads and first-time founders who might lack capital or built-in networks. NYC may have long-term pull, but at the earliest stage, higher upfront costs and friction (recent Economist data suggests ~50% higher rents than SF) push many founders away. Hacker houses may sound anachronistic, but they’re real centers of gravity. We estimate SF has at least ~10x more active hacker houses than NYC, and that these houses have helped foster hundreds of billions of dollars in market value. I’m an NYC tech evangelist. I’ve worked on tech ecosystem development at New York City Economic Development Corporation, helped design national innovation programs for the Obama White House, and now invest in early-stage companies as a GP at Max Ventures. From a dollars-to-impact perspective, seeding hacker houses in NYC is one of the most efficient levers the city can pull. We wrote an overview doc (linked below) that explores this concept — and we'd welcome feedback from the NYC tech community. We are also hosting a small series of conversations — starting with a dinner in February co-hosted with Tech:NYC, Keel (Brent J. Smith), Company Ventures and Inspired Capital — to bring together leaders across tech, real estate, and policy. 👉 If you’re a founder, operator, VC, student, or have built / lived in a hacker house: • Does this resonate? • What would make this work in NYC? • Who’s already doing something adjacent we should talk to? Would love to connect if you’re interested in contributing or joining the conversation.

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Boston doesn't just breed startups. It breeds operators who've seen the inside of billion-dollar exits and still come back hungry. That's the DNA behind Jellyfish. Founded in 2017 by Andrew Lau, David Gourley, and Philip Braden, the company set up at 225 Franklin Street with one mission: bridge the gap between engineers building and executives betting. The founders lived that disconnect at Endeca, the Cambridge outfit Oracle scooped up for $1B+. They knew the gap wasn’t abstract. It was personal. Jellyfish was their answer. Eight years later, Jellyfish is back with a launch that pushes its platform closer to an operating system. On Sept 9, 2025, the company released its Model Context Protocol server as a Claude Desktop extension. That may sound technical, but the shift is anything but academic. What used to require API integration is now a desktop download. Ask Claude “Where are we investing engineering effort this quarter?” and the answer arrives conversationally, in real time, safeguarded by Meta's #Llama #PromptGuard2. This isn’t a gimmick. It’s a structural change in how engineering intelligence gets delivered. Jellyfish already powers 500+ organizations, Mastercard, PagerDuty, Priceline, ZoomInfo, Hootsuite among them, but this extension lowers the barrier for every stakeholder. Finance can query allocation. Product can explore delivery. Leadership can ask about throughput. No dashboards, no gatekeeping. Just data in plain language. The MCP push carries Nicholas Arcolano, Ph.D.’s fingerprints. Jellyfish’s Head of Research, a Harvard University-trained mathematician, has published lessons from MCP experimentation since Anthropic unveiled the protocol in 2024. Others are validating too: Jamie Newcomb at Ably has shared how MCP integration evolved from basic retrieval to analysis that shaped decisions. The official blog post was signed by Sophie, a Jellyfish research intern. Light on detail, heavy on intent: take the intelligence already humming inside Jellyfish and make it available in workflows people adopt daily. Momentum backs the move. Jellyfish has raised $114.5M through Series C, tripled revenue and headcount in 2021, launched an AI Impact Dashboard in 2024, and landed on Forbes’ Next Billion-Dollar Startups in 2023. Competitors like LinearB, Swarmia, and Waydev can’t match the same focus. Jellyfish has always been less about vanity metrics, more about business clarity. Now that clarity speaks back. The extension is more than a technical win. Once your engineering intelligence plugs seamlessly into AI workflows, it stops being optional, it becomes indispensable. The only real question is how many teams will be waiting when Jellyfish expands to more platforms. #AI #EngineeringIntelligence #EngineeringLeadership #SoftwareDevelopment #SoftwareEngineering #Integration #Data #Infrastructure #Innovation #TechEcosystem #StartupEcosystem #TechNews If software engineering peace of mind is what you crave, Vention is your zen.

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Great HLTH 2025. Some big trends I saw: - Drug distribution is going direct - Payers want vendors to take risk - Everyone has a chatbot - Behavioral health is maturing - FDA clearance is the new Series B - Post-discharge is a big opportunity - Diagnostics are becoming portable and ROI-oriented - Provider workflows are becoming consumer-grade

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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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Obsessing over AI pricing models and GTM strategies is a convenient distraction from what really matters: product. To paraphrase James Carville, "It's the product, stupid."   Every great company I've watched win in my 16+ years in VC has had a tremendous product, yet so many founders are focusing their energy in every direction BUT product. Let me tell you a quick story about a drunk guy searching for his car keys.   A drunk guy is leaning against a streetlamp in the middle of the night. A policeman walks up and asks what he's doing. "I lost my car keys," the drunk says. "Okay, why are you looking here?" asks the cop. "Because the light's shining here."   This is exactly what's happening with pricing strategies right now. Everyone's obsessed with AI pricing models. Outcome-based pricing is the hot new thing. VCs push founders to optimize pricing before they've even figured out if anyone wants their product.   Why? Because pricing is where the light is shining.   But what actually matters is making your product better. And that's much harder.   I see the natural inclination toward the path of least resistance constantly when a company's growth slows. Suddenly, everyone's analyzing why the salespeople aren't selling well, the marketing isn't converting, and the pricing model isn't working—everything except the root cause. They've fallen out of product-market fit.   The irony is that when your product is exceptional, you can be terrible at everything else and still win. The best salespeople will want to work for you because great products sell themselves, your customers become your marketing team, and your pricing conversations become collaborative rather than adversarial.   Just look at Lovable. They have users who are highly technical and completely non-technical, from Iceland to Madagascar, and from basically every industry. They've broken all the rules of scaling.   But it works for them because they've built a truly great product. And when you have that kind of product gravity, different physics apply.   Yes, pricing matters. Yes, go-to-market matters. But they pale in comparison to product.   So if you're spending more time developing your pricing strategy than improving your product, you need to recalibrate your priorities. Build something so good that customers tell their friends about it. I promise everything else will fall into place.

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No VC funding. 6 months and an $80m exit. We are in the golden age of entrepreneurship. Maor Shlomo bootstrapped Base44 in early 2025. He built it largely alone. 6 months later, Wix acquired it for $80 million in cash. His team at exit? 8 people. And this isn’t a one-off. Zach Yadegari and Henry Langmack built Cal AI from their classrooms at age 17. No venture capital. No bloated team. They hit $30 million ARR in under two years with 7 employees. MyFitnessPal just acquired them for an estimated $100m. The old playbook: Raise capital, hire fast, burn cash, hope for scale. The AI playbook: High leverage, tiny teams, quick path to profitability. The time to market has compressed. Individual leverage has never been higher. Excited? Coterie Capital

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