Startup Innovation Methods

Founders don't care about clean code. They care about predictable outcomes. Once I understood this, everything changed. I used to lose every "we need to do this right" argument. Every time. "We need to refactor for maintainability." "We should follow best practices." "This technical debt will slow us down." Eyes would glaze over. Meetings would end with "just make it work for now." Then I learned to speak their language. Instead of "refactor for maintainability," I said "eliminate single points of failure that could take us offline." Instead of "technical debt," I said "architecture that unlocks 3x faster shipping." Instead of "best practices," I said "future-proofing our growth engine." The conversation that changed everything: CEO: "This refactor sounds expensive." Me: "What's more expensive: two weeks now or losing our best engineer to frustration?" CEO: "When can you start?" Approved that afternoon. Here's what I learned: Engineers sell features. Founders buy outcomes. We talk about code quality. They think about competitive advantage. We worry about maintainability. They worry about velocity. We see technical debt. They see missed opportunities. My framework now: Connect every technical decision to business impact. Quantify the risk in revenue terms. Frame quality as speed, not perfection. Make it about growth, not code. I've used this to convince 3 CEOs to invest in "doing it right." Not because they suddenly cared about clean code. Because I finally learned to sell predictable outcomes. What's the one technical argument you've never been able to win with non-technical stakeholders? #TechnicalLeadership #StartupLessons #EngineeringManagement

Before I sold Quest for $1,000,000,000, I wasted millions trusting the wrong thing: My own ideas. Here's the AI validation framework I wish I had when building Quest Nutrition: Most entrepreneurs fail in the same boring way: 1. Have an idea 2. Fall in love with it 3. Build it for months 4. Launch 5. Discover nobody wants it 6. Repeat This is "build and pray" physics. It's suicide. But there's a better way. One that uses AI to kill bad ideas in 72 hours, not 12 months. My 5-step AI validation framework that has saved millions in wasted effort: 1. Problem Verification Your idea isn't special. Period. The only thing that matters is: are people actively suffering from the problem you claim to solve? Feed Perplexity and ChatGPT with Reddit threads, forum posts, and review sites. Let AI extract patterns of pain. No real pain = dead idea. 2. Market Size Analysis Even if the pain is real, is it widespread enough? Let AI analyze Google Trends, search volumes, and TAM data. Create detailed spreadsheets of potential users. Too small = dead idea. Goals make demands. If the goal is to build a substantial business, the market has to be big enough. 3. Competitor Assessment Feed AI your top 5 competitors' websites, pricing pages, and customer reviews. Have it identify gaps and oversaturation. Create a map of what's missing. No clear advantage = dead idea. Build from physics, not analogy. That's the only way to find a real competitive edge. 4. Zero-Cost MVP Design Most founders build full products before validation. That's the most expensive way to learn. With AI, create "fake door" tests instead: • Landing page that looks real • AI-generated mockups • $50 of ads to see if people try to buy No buyers = dead idea. The market doesn't care how hard you worked. It only cares if you solved a real problem. 5. Early Adopter Interviews For ideas that survive steps 1-4, use AI to: • Draft perfect outreach messages • Generate interview questions that reveal buying intent • Analyze interview transcripts for patterns No enthusiasm = dead idea. This is Physics of Progress in action. Test hypotheses. Follow the data. Kill your darlings fast. The hard truth about entrepreneurship is that 90% of ideas SHOULD die. Your job isn't to build - it's to kill bad ideas quickly. Most entrepreneurs think failure is the worst thing that can happen. It's not. The worst thing is wasting years on something nobody wants. Let AI be your reality check. It's ruthlessly honest in a way your friends, your team, and even you can't be. Ideas are worthless. Validation is everything. PS: I’ve trained an entire GPT to track down the root cause of your next revenue plateau - and help you break through it. It’s built based on 100,000s of data points from my group coaching sessions. Grab it for free here: https://buff.ly/nUri82k

In 2011, this guy created booking software for boat tours. In 2018, he sold it for $300 million. He kept all the money because he never raised a dollar. The story of an amazing software business you've never heard of 🧵 Meet FareHarbor. Booking software that 20,000 tourism operators run on. While other competitors raised $20-30 million rounds, they bootstrapped and doubled revenue every year. They took a clever approach. Let's dive in: Problem Brothers Lawrence and Zach Hester grew up in chilly Minnesota. When Lawrence visited Zach at school in Hawaii, he tried to reserve a surfboard and kayak online. He found there wasn't a simple way for these tourism businesses to accept online sales. So they built it. First Customers They sold the first customer without a product. Over 12 months, They got to 25 Hawaii-based clients including parasailing, snorkeling, and horseback-riding companies. They met with all prospects in person, even if it meant hopping on a plane. It worked. Pricing Innovation They realized tourism was transactional. These weren't repeat customers. Instead of charging for the software, they gave it away for free. They charged end-consumers a 6% transaction fee. FareHarbor made $100s to $1,000s per month from each operator. Done-For-You Instead of saying hey you need a website to these busy tourism operators. They would build a website and get it all set up to take orders for them. "In the early days, it was about building a business. It’s about having revenue. It’s not about playing startup.” Go-To-Market They hired young salespeople right out of college and paid them a tiny base salary but half the first-year bookings for the operators they signed. It was great money. One slept in a van and drove around Hawaii until he booked every single operator in the area. All Hands On Deck When a VC-backed competitor went under they swooped in. 90% of the team came to the office through the July 4 weekend. 20 air mattresses were brought in. But they needed more time. Take Advantage Other competitors tried to buy the failing company. They got turned down. Instead, FareHarbor offered $100k just to keep the lights on for 7 days. They agreed. In the end, FareHarbor snapped up 340 of Zerve's 549 clients and 90% of all its transaction volume. Outcome FareHarbor scaled to $50 million in revenue while doubling every year. They went on to sell to Booking. com for $300 million. Since the brothers never raised money, they got to keep almost all of it. Want to get more business breakdowns? Subscribe to my free newsletter below...

In the world of Generative AI, 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) is a game-changer. By combining the capabilities of LLMs with domain-specific knowledge retrieval, RAG enables smarter, more relevant AI-driven solutions. But to truly leverage its potential, we must follow some essential 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗖𝗹𝗲𝗮𝗿 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Define your problem statement. Whether it’s building intelligent chatbots, document summarization, or customer support systems, clarity on the goal ensures efficient implementation. 2️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 - Ensure your knowledge base is 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝘂𝗽-𝘁𝗼-𝗱𝗮𝘁𝗲. - Use vector embeddings (e.g., pgvector in PostgreSQL) to represent your data for efficient similarity search. 3️⃣ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 - Use hybrid search techniques (semantic + keyword search) for better precision. - Tools like 𝗽𝗴𝗔𝗜, 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲, or 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲 can enhance retrieval speed and accuracy. 4️⃣ 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗲 𝗬𝗼𝘂𝗿 𝗟𝗟𝗠 (𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹) - If your use case demands it, fine-tune the LLM on your domain-specific data for improved contextual understanding. 5️⃣ 𝗘𝗻𝘀𝘂𝗿𝗲 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 - Architect your solution to scale. Use caching, indexing, and distributed architectures to handle growing data and user demands. 6️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Continuously monitor performance using metrics like retrieval accuracy, response time, and user satisfaction. - Incorporate feedback loops to refine your knowledge base and model performance. 7️⃣ 𝗦𝘁𝗮𝘆 𝗦𝗲𝗰𝘂𝗿𝗲 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝘁 - Handle sensitive data responsibly with encryption and access controls. - Ensure compliance with industry standards (e.g., GDPR, HIPAA). With the right practices, you can unlock its full potential to build powerful, domain-specific AI applications. What are your top tips or challenges?

Quantum computing is no longer speculative—it’s becoming an investment priority. In 2023, European quantum startups outpaced North America, raising $781 million (three times the $240 million raised in the US). Globally, quantum startups raised $2.2 billion, a massive jump from $522 million in 2019. This isn’t happening in a vacuum. Governments are fueling the momentum. The UK has committed $4.3 billion to quantum technologies, while Germany has pledged $3.7 billion. At the same time, VC interest is holding steady, even as funding dries up in other tech sectors.   Quantum technology will have a wide-reaching impact, from cybersecurity and financial modeling to drug discovery and materials science. Pharma will likely see the earliest impact (drug development and molecular simulations using quantum).   In 2022, Finnish startup Algorithmiq raised $4 million for quantum-powered drug discovery, while Paris-based Qubit Pharmaceuticals secured $17 million for molecular simulations. Another European company, Terra Quantum AG, based in Switzerland, raised $75 million to scale its quantum-as-a-service model, which has direct applications in pharma and beyond.   Big Tech is also all-in. Google, IBM, Intel Corporation, and NVIDIA are pouring resources into quantum hardware and software. Meanwhile, publicly traded quantum companies have seen their stocks surge, signaling growing institutional confidence.   At APEX Ventures, we invest in revolutionary quantum startups. We are partnered with kiutra, enabling the second quantum revolution with easy-to-use and sustainable cryogenics, and planqc, building quantum computers that store information in individual atoms.   For founders and investors, the question isn’t whether quantum will matter—it’s when. The trajectory is clear: capital is flowing, enterprise adoption is accelerating, and governments are fully committed. If AI dominated the last decade, quantum may own the next.   #Venturecapital #AI #Deeptech #Startups  Follow us at APEX Ventures and subscribe to our newsletter for exclusive content on groundbreaking Deep Tech startups:   🔗 https://t2m.io/EV2qHQuo

🇮🇳 India is not just the pharmacy of the world. It’s now the operating room too. A $9B medical tourism industry has quietly taken shape. A heart surgery that costs ₹25L in the US? It gets done for ₹2.5L in India. A liver transplant that takes 6 months in the UK? Gets done in 3 weeks here, with better outcomes. But where is the Gap? There’s no single platform where a patient from Nigeria/Oman or any other country can: ❌Compare hospitals ❌Book treatment ❌Get visa, insurance, and follow-up care All in one place. Some hospital chains like Apollo, Fortis, and Medanta offer in-house international services. But they only solve their part of the journey. What about discovery? Pricing transparency? Cross-hospital comparisons? Post-op stay? 🤡Right now, most patients rely on WhatsApp brokers, embassy referrals, or Facebook groups. (These guys are minting 💰) 🤹What this space needs is a platform that combines: Airbnb’s trust + Practo’s medical directory + Insurance integration. Basically an aggregator that productises trust and removes friction. The first startup to solve this won’t just create a business. They’ll redefine how the world experiences Indian healthcare. It’s a Blue Ocean , no competition whatsoever. If you decide to build this, stay in touch. PS I come across a lot of such ideas, do you want me to keep posting them here? Let me know in the comments.

A mistake I see founders make: thinking startup idea validation is the same as product-market fit 🙅♂️ We validate startup ideas every week at Inaugural. Some ideas stick, some ideas fail. But one thing we're very clear about is that validating a problem and potential solution is very different from achieving product-market fit. Idea validation is: 👉🏼 Proof a problem is worth solving (size of the prize) and exists for more than one person. 👉🏼 Evidence the potential user has either looked for a solution, built a workaround themselves or has resided to the fact the problem can't be solved. 👉🏼 ⁠Proof that a potential user is or will pay for a solution (and getting them to commit). Product-market fit is: 👉🏼 High usage of a product with strong retention metrics. 👉🏼 User acquisition that's scalable and displays little to no friction. 👉🏼 An output perceived to be more valuable than the cost of the product ('don't take the product away from me'). Many founders skip the work required to validate an idea properly and instead search for product-market fit. It's a subtle difference, but making this mistake manifests as necessity-driven pivots. #founder #vc #startup #business

After interviewing more than a hundred founders and spending the last few months building rivva, I keep seeing the same pattern among the ones who build well. They think like operators and design like psychologists. Thinking like an operator keeps you close to the work. You understand the constraints, the trade-offs, and the parts of the system that slow everything down. You see what users actually value rather than what looks good in a deck. It is not about doing every task yourself; it is about building enough judgment to make decisions that reflect reality, not assumptions. Designing like a psychologist keeps you close to your people. Startups run on emotion as much as execution. Fear, energy, trust, and uncertainty shape output more than we admit. When you understand how people behave under pressure, how they respond to change, and what helps them feel safe enough to take risks, the work becomes lighter and momentum becomes easier to sustain. Early-stage building is chaotic. You are not meant to have a neat system for everything. But if you stay close to the work and close to your people, you build a company with rhythm and clarity. The best founders I have met are both operator and psychologist at the same time. That balance is what makes great products and healthy teams possible.

So you have a new startup or innovation idea, but how do you test quickly put the key assumptions into one spot? The answer: the Business Model Canvas. I am sharing some tips below - which ones would you add? This is true for both for profit as well as social entrepreneurs, as it forces you to think about key aspects of your business model. There are great resources and blogs publicly available by Strategyzer and others. We also use this or social impact variants of this in our Innovation Bootcamps at the WFP Innovation Accelerator. Here some tips if you've never used this before: 1) Jot down your key assumptions on paper in an open session with your co-founders/key team members Start noting down things you strongly believe in and also open questions. Nobody has all the answers when you do this. And even if you believe you have all the answers, stay open minded to change those, once you start checking with users/customers. Set aside some time to do this, maybe a couple of hours, maybe a day, but don't plan on doing this for weeks. 2) Think about how you would sell your startup/innovation to an investor/funder Sometimes it can help to reframe from "thinking" into other aspects. When you do your pitch to an investor, you will inherently want to mention the key aspects of your startups -> there you go, that's going into Business Model Canvas. 3) Do this as early as possible - before (!!) you have all the answers you want Specifically, if you have been trained in traditional - "I need to do 6 months of field study and build a product for 1 year" before I have any ability to plan, step back and front-load business model canvas. You will realise that some of your critical assumptions require a different set of research or speaking to different people, or co-developing with others 4) Use the method to align with your co-founders/key team members Having the conversation invariably will open up different assumptions and different beliefs. And that's great. It brings different beliefs out in the open. You will be surprised how often these things are driven by pure "gut feeling" or also dogmatic "it has to be that way" opinions, when there is no experience or user testing to back it up 5) Use more than more canvas if that's what it needs Sometimes your startup/innovation will be working on more than just one "product/service". If that's completely different and has a different business model, you might need to have more than one canvas 6) Have somebody in the room who has done this before While the method is simple, it can help to have an impartial expert in the room who helps you facilitate this. Especially if co-founders have difficulties aligning (that's something to watch out for in any case!!), an expert that you hire, or an experienced startup founder can help you focus on what's relevant. Or maybe you also do this while in an Accelerator programme! #startupadvice #gettingstarted #socialimpact #socialentrepreneurship

I built our early MVP on under $150 at Baseline. It had 350 users. Here’s a framework on how to validate your idea as a founder: Too many founders’ default is to raise capital before they’ve proved people want what they’re building. In 90% of cases you should be able to validate your idea through no-tech solutions. It can be make or break for early funding rounds. → You’re building for yourself to solve your problems. → You’re building for someone else. Here's the framework I used with Baseline: 1. Map out the entire problem & solution 2. Map out how different parts of a product solve the problems. 3. Focus on specific problem which is high value. 4. Figure out the lowest cost way to solve that problem. 5. Land on no-code or little code solution. 6. Find no-code solution OR 7. Go on Upwork and find a dev who could build for me for cheap. 8. We landed on an AI companion in WhatsApp. 9. It solved our thirst for education in bipolar. 10. Take our pre-MVP and see if it solved our problem. 11. If yes take our pre-MVP and send it to 100 people. 12. We chatted to those 100 people and be hyper critical. 13. I was prepared to be embarrassed by how sh*t it is. 14. We got the most critical feedback ever. 15. I asked if they’d pay for it. If not, why? 16. This defines your thinking & fundraise. We did this on $130 🤯 Number 4 is where most people f*ck up. They’re scared of putting a crap solution to market & swallowing their ego to get feedback. Number 4 is where most people struggle to comprehend that this is where the earliest part of entrepreneurship is. Number 4 is the start of the journey- if you get past 4, you’re so on your way to finding out whether your idea really has legs. If your early pre-MVP hits the spot, your sell to investors and your brand looks like this: Last month we hit 100 users. Last week we hit 300 users. This week we hit 500 users. That excites me. Please, for the love of god, stop wasting your time by ignoring the proven early validation steps 🙏 P.s If this was helpful or you're knew to my content, follow me: I post about startups twice a week.