Zaheer Mohiuddin

Founding Engineer hires have been our specialty dish for years now. But what it means to be a Founding Engineer, and what startups should expect from one, is changing rapidly. A Founding Engineer was always responsible for getting the product to market, proving product-market fit, setting the technical bar, and carrying the company through a Seed round on the company's terms. It's never just been 'our first hire that can code', despite how often it's treated that way. But AI tooling has expanded what one engineer can produce to the point where a single person now covers ground that required a small engineering team just 6-12 months ago. This doesn't make the role easier, oh no... It makes it much bigger, far more important, and even more responsible for the company's overall success. In fact, I am convinced we will see one-person technical teams raise Series A rounds in the coming years. And that changes both what founders should demand from this hire, and what they should offer in return. Firstly, if you cannot afford one of the most capable product minded Software Engineers in the country, you do not have the budget to make your Founding Engineer hire yet. Don't be the founder who hires someone average because it's all you can afford. No hire + getting more money from somewhere is better than wasting all your limited runway on someone average. A Founding Engineer who needs meaningful support to get you where you need to go is not a Founding Engineer. They are a wasted hire. Avoid. Next, if someone is taking you from pre-seed to Series A on their own, the equity needs to reflect that. 1%-1.5% is standard today. This isn't enough. Not for the responsibility you're giving them, and the impact you expect in return. You need someone to stay with you for years, likely through many challenges when they could work for literally anyone they like - token amounts like that won't do it. If a single person is responsible for your entire technical operation through the most formative period of your company, what's your genuine objection to 3%, or 5%? When they help you grow to be a $100m company, do you think you'll even care? And finally, the bottleneck in AI-augmented engineering is no longer how fast you can build - it is knowing what to build on the fly, using product judgement and taste, a somewhat rare skillset even among startup engineers. This doesn't mean hiring a product person who can code (unfortunately this is becoming common and is a mistake). It means an exceptional engineer who also thinks clearly about customers and product direction. AI has shrunk the pool here, not widened it. Deep technical skill is still the foundation. But without product judgment on top of that, you'll just find yourself running faster in the wrong direction. Then again, I've only been trusted to place Founding Engineers with startups backed by Blackbird, AirTree, Square Peg, and more... so what would I know about any of this anyway? ;)

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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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"But that revenue target is totally fu$%ing impossible!?" I exclaimed a former boss of mine as I showed him the decades long historical trend to no avail. I had spent weeks pouring over the numbers, trained an ML model on the historical data, and had even hired an analyst on fiver to give me a second opinion. But there was no way around it. Accepting this number would doom me and my team to the inevitability of failure. Maybe it was the pressure or maybe it was the lack of sleep but inevitably the last sinnew that attached me to giving a fu$% broke and I found myself able to see the forest from the trees. As Jake McNeill mentions in this Substack article Systems Over Goals, Goals fail because: 1️⃣ They fail because they tie self-worth to results you can’t control. 😵‍💫🎯 2️⃣ They dangle a finish line that doesn’t exist. 🏁➡️🚧 Even when you hit it, your ego (or your boss/investors) moves the goalposts. 🥴⚽️ If I played the “hit the impossible number” game, I was screwed from day one. No amount of decks, dashboards, or ML models were going to save me. Because the game itself was rigged. That’s what Jake is really saying: Goals are a trap. They look like progress, but they’re really just moving finish lines designed to keep you sprinting until you collapse. Systems? Systems don’t give a sh*t about your boss’s fantasy target. ⚡ Systems focus on inputs you can control. 🎯 📈 Systems compound over time. 🔁 🧠 Systems build self-trust — the kind that doesn’t evaporate when your quarterly number doesn’t. 💪 So what did I do after accepting my 🐄 💩 fate? I focused on building systems that I could control and that I knew would compound. At the time we had no lead routing system so it could take north of two weeks to get someone on the calendar. So I built a gangster version of Chilli piper and made sure we religiously practiced a good outbound cadence. Did we hit our goals? Hello no! But my guys still made money and the systems that were put into place are still paying dividends long after my departure.

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Y Combinator just dropped their Request for Startups for Fall 2025 💥 It’s a blueprint of what YC wants to fund right now. The ideas they believe could become billion dollar companies. Some are no-brainers. Others are wild 👇

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This a seductive idea; that organization's context fragments, decision logic gets lost, and an organization is prone to tackle collective amnesia. BUT this comes with a lot of caveats!! - MONOCULTURE: When one system becomes the authoritative interpreter of "why things are the way they are", it will collapse pluralism. An organization actually benefits from various "contested memories". An engineer and sales guy remember different things from a discussion and that helps. Employees can stop arguing with the system not because its correct but its "coherent". - OBSERVER's EFFECT: When people know that the reasoning within systems is being captured, they will change what gets recorded. The negotiations will become "safer" and will contaminate raw signals. - CENTRALIZATION: Interpretation gets centralized upwards. It may appear leadership or executives are gaining synthesis and becoming closer to the ground. But there is no comparison to living through the frictions, not even eclectic summaries. LEADERSHIP MUSCLE NEEDS TO PRACTICE LIVING THROUGH FRICTION OTHERWISE IT WILL TURN SENILE. - BIAS AMPLIFICATION: Model's inbuilt biases can combine with company's cultural quirks resulting in misinterpretations and distortions, worse they get reinforced over time. System will learn how the company thinks and then will optimize for continuity. AMNESIA IS SOMETIME A FEATURE. - SEMANTIC TRUTHS: Unlike objective truths this system will play on subjectivity and will build "causal" relationships that are based on semantics. This over time can form mythical decision norms with no external validity. The real question isn't "CAN IT BE BUILT?". Of course it can be. But "HOW IT SHOULD BE POSITIONSED". #llms #leadership #ai

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Now that big tech companies are laying off tens of thousands of employees, which is sadly nothing to celebrate, the silver lining is that more talented young people will once again look to start or join earlier-stage startups. California and the world will benefit far more from fresh entrepreneurial energy and less “ex-Google, ex-Apple” employee energy that had taken over the past decade or so. We need more entrepreneurial mindset and less big-tech employee mindset. Gone are the “a day in the life at big company XYZ” Tik Tok videos and people will need to learn to build something again. Building is much harder than being an overly paid big-tech employee with a cushy non-impactful role, but it’s much more rewarding and a much better learning ground. If you are young, and really want to learn and grow, join a great startup or scaleup. Best advice I ever got when I was in my twenties.

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Everyone's panicking about the proposed $100K H-1B visa fee, but here's how I think it will actually play out, especially for startups and tech jobs. If this fee really goes through, the first group to feel the pain will be early-stage startups in the US. These are the teams that rely on immigrant engineers to ship product fast, and suddenly they'd be staring at massive added costs just to keep talent in the country. Many of them won't pay. They'll simply stop sponsoring visas. What happens next? Two things. First, we'd see a lot more remote hiring. Instead of going through the headache of immigration, companies would just open dev offices in India, Eastern Europe, or Latin America and hire directly. Second, we'd likely see a shift in global talent flow, a kind of "brain drain reversal." Fewer people would dream to move to the US, and more would choose to build from home. In a way, this could accelerate the trend we're already seeing: Silicon Valley is no longer just in California. It's everywhere, especially Bengaluru. If this rule passes, that shift just gets 10x faster.

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Funding doesn’t create growth. It amplifies your weaknesses. After a raise, I see the same pattern repeatedly: Open roles stay unfilled. Product velocity drops. Technical debt compounds. Founders become decision bottlenecks. The issue isn’t funding. It’s hiring structure. Post-funding teams don’t need more resumes. They need: Senior ownership layers. Engineers who scale processes. Remote access to wider talent pools. Hiring locally for global growth targets creates friction fast. If you’ve recently raised capital, what’s your biggest bottleneck right now?

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