Ted Mao

Don't Build a Better Wheat Farm" - Why Defensibility Stakes Are Higher in Deep Tech Just published a new piece on my "Ignore the Confusion" blog, building on thoughtful insights from Eric Ver Ploeg at Tunitas Ventures about startup defensibility. Eric's core thesis: Too many startups pitch like wheat farmers - "huge TAM, slow incumbents, growing market, domain expertise" - but fail to think through long-term defensibility until it's too late. From a deep tech perspective, the stakes are even higher: ** Unlike software, deep tech founders must commit to defensibility strategies from day one - their funding depends on it ** Patent vs. trade secret decisions are often difficult to reverse and shape your entire competitive strategy ** Even "picks and shovels" providers (the tools that make industries more efficient) become commodities without proper moats The key insight that resonates: Defensibility can't be retrofitted. Whether you're building software or deep tech, your moat must be architected into the business model from the start. Thanks to Eric Ver Ploeg for sharing these insights on startup strategy and letting me build on his framework from a deep tech lens. Read the full post: https://lnkd.in/dEj_iF-Q #DeepTech #StartupStrategy #Defensibility #VentureCapital #Innovation

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I’ve come to believe that early experiences carry disproportionate weight - far more than we often acknowledge. For designers and builders, “minimum viable” may suffice for validating core functionality, but it rarely earns trust or sparks genuine delight. While velocity matters, I’ve always struggled with the notion that early versions should be merely viable, especially in a world where attention is scarce and emotional resonance is everything. This extends beyond product. Do you show up as the minimum viable version of yourself in your work, your relationships, your craft? I would encourage you to challenge the conventional MVP framework. Replace it with the Minimum Awesome Product. This doesn’t mean it’s fully-featured or pixel perfect. But it should be worthy of attention. Memorable, even evocative in the way it triggered emotion. The kind of product that prompts someone to say, “I didn’t know I needed this, but now I can’t live without it.” You can refine mechanics over time, but the emotional experience of a first interaction is singular and unrecoverable. Don’t settle for viability. Build something worth remembering… and make it awesome! ❤️‍🔥

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I thought this was a good and very reasonable talk by Eleanor Millman on how to prioritize projects for a platform engineering team: https://lnkd.in/gaYtUHRH A few insights I took from it: - Unlike product features which can be more directly tied to revenue, platform engineering work is a couple steps removed. But that doesn't mean it's unimportant! Far from it, platform engineering projects can make everyone at the company more efficient, able to produce higher quality software, etc. - While "urgency" is always going to be a factor, you don't want to just be putting out fires, you want be able to prioritize work which is "high impact" ... even if that impact isn't felt immediately. - Good rule of thumb: prioritize "highest impact for lowest effort". - The talk has some ideas on what factors to choose for impact, and how to blend them. For instance "speed of development" is one factor, "cloud cost optimization" might be another. The weighting of different impact factors can change over time, depending on the needs of the business. I would say that a lot of companies don't have much methodology here but I can really see the value in codifying it. You can always change the methodology or the weighting if it's spitting out results that don't pass the smell test or you really feel it is leading the company astray. Clarity can give the org more freedom to put resources behind projects that would otherwise never be taken on. When things are unclear, prioritization still happens implicitly, but the decisions tend to be a lot more random and fear-based and the org is worse off as a result.

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One of the reasons early-stage fundraising feels inefficient is that founders are forced to figure out investor fit through trial and error. Different investors back different stages, sectors, and levels of traction. And when that alignment is off, conversations either stall quickly or never really get off the ground. This is one of the pain points that we’re focused on closing at ThatRound. We help founders see which investors actually make sense for their business before they start applying for funding. Fundraising works best when alignment comes first, and this review shows how we’re simplifying the process for UK founders.

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Last week's musings. You'll notice some reoccuring themes... 𝟭. 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗿𝗼𝗯𝗼𝘁𝗶𝗰𝘀/𝗱𝗮𝘁𝗮 𝗳��𝗿 𝗥𝗟 🍇 (𝗿𝗲𝗾𝘂𝗲𝘀𝘁 𝘁𝗼 𝗷𝗮𝗺)  -recurring idea i've been mulling -robotics is happening particularly in industrial setting  -still lots of problems, but the revolution is happening and there are  work arounds until the tech is really there -prices in theory are highest they will ever be (impact of tariffs?) -folks looking for pre-training data, but eventually will also need fine tuned data -what data fidelity is good enough? 𝟮. 𝗧𝗿𝗮𝗻𝘀𝗗𝗶𝗴𝗺 𝗮𝘀 𝗶𝗻𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 + 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗵𝗮𝗿𝗱 𝘁𝗲𝗰𝗵 🍇  -it's an absolute monster, $75B market cap and up 300% over the last 5 years. - aircraft parts, 90% proprietary but high margin - killer aftermarket business  - how many software biz have lifetime value of 50 years?! -has lots of things software investors look for (sticky revenue, high margin, defensibility in GTM and unique flywheel, focus, etc.)  -makes me think way more about hard tech viability 𝟯. 𝗧𝗿𝘂𝗲 𝗳𝗼𝗰𝘂𝘀 - from Neil Shah on Invest Like the Best - incredible listen - few people have maniacal focus and it's so important for founders 𝟰. 𝗦𝗽𝗲𝗿𝗺 𝗥𝗮𝗰𝗶𝗻𝗴 -WTF  𝟱. 𝗖𝗼𝗿𝗹𝗲𝗼 𝘁𝗵𝗲 𝗥𝗼𝗯𝗼𝘁 𝗛𝗼𝗿𝘀𝗲 -Kawasaki released prototype and it's wild! Substack for full thoughts: https://lnkd.in/gzNtkEHk

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PSA to seed and early-stage founders: stop worrying about competition. I’ve spoken to hundreds of late-stage founders, and they all say the same thing. The biggest mistake they see at the early stage is founders obsessing over who else is in their space. When you are pre-PMF, comparing yourself to other buzzy AI startups is a distraction. They already have a defined GTM motion, some level of product-market fit, and a playbook to execute. Their “big new feature” might just be about expanding revenue or building parity. You’re still trying to land your first 10 paying customers. Here’s the truth: 1. Worry about competitors only when you actively lose a deal to them. THESE ARE THE COMPETITORS TO WORRY ABOUT. 2. When a customer chooses someone else, they will usually tell you why. Nine times out of ten it’s not the reason you think. It’s rarely a missing feature. More often it’s pricing, brand trust, or timing. 3. Competitors often move in completely different directions. OpenAI went after consumers, while Anthropic leaned into enterprise and APIs. Playbooks diverge fast. 4. The only real roadmap at the early stage is customer pain. Every conversation, every demo, every user insight. Talk to as many customers as possible. If you spend your time trying to copy what competitors are doing, you will always be a step behind. If you spend your time talking to users, you’ll know exactly what to build next. The path to PMF isn’t found on Twitter threads about “AI competitors.” It’s found in the feedback from the 5 people actually using your product every single day.

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GTM goals for early stage technical companies: 1. Be referenced often in internal meetings of your prospects (exploratory phase of adoption) 2. Be referenced often in externally facing architecture diagrams of the top companies in your market (first path to production + PMF) 3. Be endorsed often in communities as experts/influencers see 1 + 2 appear without your promotion (your real ARR + NRR story happens here) 4. Be endorsed as the gold standard by communities (moat, hang on for dear life and keep shipping and making money) #2 is v hard as things shift often, but Founder time should be with customers to make this occur as often as possible. #3 + #4 become your roadshow focus over time so you can scale your company.

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a friend just texted me: "Hearing there are 'senior devs' who are very skeptical of AI generated code (aka not using any AI tools) at a startup I’m interviewing with… interesting that they are still employed in a startup against this backdrop [see image]" my response: seems like there are just different games being played those who embrace AI as a medium for expressing ideas are playing the right game — they're not just treating it as a "better tool" if you've been in industry awhile, you may need an intellectual reboot to see it last night i went to a VentureBeat event; mostly enterprise folks… several people (from IBM, Walgreens, etc) asked about “change mgmt” and how to convince their teammates to adopt AI tools… someone raised the question of brown-field vs green-field opportunity w/r/t AI… is it worth it to upskill existing workers or better to just start fresh…? Great question! for people entrenched in a job fiefdom, e.g. where the power players and how to execute reliably, the perceived risks of novelty are infinite. they're not wrong, they're just not interested in changing the game they've mastered. aaand also, developers are the self-disclosed laziest people in the world. but they're incredibly productive because they'll happily spend a week automating some repetitive task so they never have think about it again. this is why generative AI and coding go so well together: the existing developer culture despises inefficiency, and is always looking for ways to cut corners in favor of a more elegant, efficient route. this is because developers are incredibly sensitive to the tiniest impediment to achieving persistent flow states. it's like noticing a typo in that green-on-black matrix screensaver and instead of ignoring it as a minor irritant, they'd prefer to rewrite the screensaver from scratch to fix it. but for non-developery roles, this gameplay is totally foreign. maintaining those impediments ensures job security. since they know how to navigate around known obstacles, their tacit knowledge is an insurance policy against obsolescence — for now. so it makes that sense that some senior devs, whose sunk costs could represent hundreds of thousands or millions of $$$ into existing, _stable_ systems might resist the quasi-non-deterministic output of AI coding tools. except (as they say) there is more than one way to skin a cat. here's a new game to invite resistants to play: have the skeptical senior devs review several blind PRs, all submitted by “new,” unfamiliar contributors (a mix of humans and AIs), and see if they prefer the human or AI-generated code. if the AI code really is as good as many of us believe, the experience might snap them out of their sunk-cost fallacies. because until you surprise yourself with what these tools can actually accomplish—in a blind taste test—you’ll never really see what winning this new game looks like, even if it's happening right in front of your eyes.

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"Because engineers had been through brainstorming and story mapping, they were able to identify the most promising direction and find a way to implement one key piece of it pretty quickly." Want to get your engineers more involved in continuous discovery? Learn how Ellen Juhlin, Senior Director of Product Management at Orion Labs, successfully brought engineers into the discovery process. Key takeaways from Ellen's experience: 🎯 Start by inviting engineers to customer interviews as listeners and note-takers 💡 Include engineers in brainstorming and solution mapping sessions 🗺️ Use assumption mapping to help engineers focus on validating ideas 🔄 Don't worry about following a linear process - iteration is key 🎯 Begin with just one or two engineers rather than the whole team 🤝 Keep engineers connected to business outcomes Check out the comments for a link to the article. ❓ What's one small step you've taken to include engineers in discovery work? Share your thoughts in the comments below.

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