Factors Influencing Startup Success

I've watched 3 "revolutionary" healthcare technologies fail spectacularly. Each time, the technology was perfect. The implementation was disastrous. Google Health (shut down twice). Microsoft HealthVault (lasted 12 years, then folded). IBM Watson for Oncology (massively overpromised). Billions invested. Solid technology. Total failure. Not because the vision was wrong, but because healthcare adoption follows different rules than consumer tech. Here's what I learned building healthcare tech for 15 years: 1/ Healthcare moves at the speed of trust, not innovation ↳ Lives are at stake, so skepticism is protective ↳ Regulatory approval takes years usually for good reason ↳ Doctors need extensive validation before adoption ↳ Patients want proven solutions, not beta testing 2/ Integration trumps innovation every time ↳ The best tool that no one uses is worthless ↳ Workflow integration matters more than features ↳ EMR compatibility determines adoption rates ↳ Training time is always underestimated 3/ The "cool factor" doesn't predict success ↳ Flashy demos rarely translate to daily use ↳ Simple solutions often outperform complex ones ↳ User interface design beats artificial intelligence ↳ Reliability matters more than cutting-edge features 4/ Reimbursement determines everything ↳ No CPT code = no sustainable business model ↳ Insurance coverage drives provider adoption ↳ Value-based care is changing this slowly ↳ Free trials don't create lasting change 5/ Clinical champions make or break technology ↳ One enthusiastic doctor can drive adoption ↳ Early adopters must see immediate benefits ↳ Word-of-mouth beats marketing every time ↳ Resistance from key stakeholders kills innovations The pattern I've seen: companies build technology for the healthcare system they wish existed, not the one that actually exists. They optimize for TechCrunch headlines instead of clinic workflows. They design for Silicon Valley investors instead of 65-year-old physicians. A successful healthcare technology I've implemented? A simple visit summarization app that saved me time and let me focus on the patient. No fancy interface, very lightweight, integrated into my clinical workflow, effortless to use. Just solved an problem that users had. Healthcare doesn't need more revolutionary technology. It needs evolutionary technology that works within existing systems. ⁉️ What's the simplest technology that's made the biggest difference in your healthcare experience? Sometimes basic beats brilliant. ♻️ Repost if you believe implementation beats innovation in healthcare 👉 Follow me (Reza Hosseini Ghomi, MD, MSE) for realistic perspectives on healthcare technology

This month, China shipped the best open-source LLM ever released. Twice. First came Moonshot AI's Kimi 2 - a 400B+ parameter Mixture of Experts model with up to 2 million tokens of context in proprietary deployments (128K in the open release). Just two weeks later, Alibaba's updated Qwen3 dropped, beating all others across MMLU, GSM8K, HumanEval, and ARC, despite being roughly one-fourth the size. These aren’t just strong Chinese models. They’re better than everything else that’s open. If you're only tracking OpenAI, xAI, Google DeepMind, Anthropic, Meta (OXDAM anyone?), you're missing half the map. Let’s talk about how China’s AI strategy is diverging from the U.S.: (1) Different Foundations. Chinese labs aren’t just fine-tuning Western models - they’re building from scratch. Kimi and Qwen weren’t bootstrapped from GPT-2 or pre-trained in English. They’re native-born models, optimized for chinese-language tasks, long-context reasoning, and mobile-first deployment. Kimi is built for cognitive labor, not chatroom banter. It is a document-native agent - compressing legal contracts, summarizing financial reports, answering across sprawling PDFs. (2) Different Form Factors. The Western paradigm centers on chat-first UX: Copilot, Claude, ChatGPT. In China, LLMs live inside superapps: WeChat, Taobao, DingTalk. The interface is less visible, more embedded - generating invoices, rewriting legal terms, creating marketing copy inside workflows. The user doesn’t always know they’re using an LLM - and they don’t care. The value is functional, not philosophical. (3) Different Constraints. U.S. labs benefit from: - Best-in-class GPUs (A100/H100) - Global API distribution - English-language web data - Loose alignment requirements Chinese labs face: - Export restrictions on advanced chips from the US - No access to OpenAI, Anthropic, or Gemini APIs - Stricter regulatory oversight on outputs But constraints breed innovation. Chinese models are built to be efficient, deployable, and sovereign. And they iterate fast, often weekly. (4) Different Strategic Advantages. China has: - Data access: Massive consumer internet footprint + government records = rich pre-training sources. - State support: Government subsidies for compute, training, and foundation model development. - Enterprise pull: Urgent demand for AI across logistics, finance, manufacturing-sectors where LLMs aren’t toys, but tools. - Centralized velocity: Close coordination between state, academia, and private labs accelerates deployment. The Ministry of Industry and Information Technology (MIIT) has already registered 40+ foundation models for public use - creating a semi-regulated AI stack that scales. While the West continues to chase AGI, China is deploying AI that works - at scale, for billions, inside the operating systems of everyday life. We’d be foolish to ignore it.

The next global leaders in robotics will definitely come from China. The startup scene in robotics there reminds me of the mobile internet frenzy of the 2010s, but this time, the end-market is global, and the cost advantage of Chinese robotics companies is multiples that of their global peers. It is also very timely, given the rapidly aging society, advances in manufacturing, and acceleration in AI, making it a potential giant industry of the future.   🔹 Unbeatable costs, speed, and innovation If you walk down Huaqiang North Road in Shenzhen, you’ll find the world’s best supply chain for robots and other consumer electronics. For just a few thousand RMB, you can assemble a 100-cm-tall robot with voice control and moving arms. Vendors there can even help you create a prototype within days, at costs and speeds unimaginable elsewhere. The scariest part? Everything is getting cheaper, better, and faster as the supply-chain ecosystem grows.   🔹 The fruit of competition (involution) – positives and negatives Being an entrepreneur in China is tough. I’ve talked to many founders, and the story is the same: they start with humble beginnings (funding in the millions of RMB), a small team of friends (usually classmates), and a focus on solving a specific use case. After iterations and sometimes complete product changes, they eventually find a marketable product. Once in the market, they face intense Chinese competition on price, speed (to the next version), and niche use cases. To survive, they must compete on low/no margin, move quickly to the next product, and find new markets. This loop continues.   The positive side of this “involution”? The winners are globally leading and hard to beat. The negative side? The casualties – companies, entrepreneurs, capital, and an always-on, 24/7 working lifestyle that leads to early burnouts for many. Of course, one can argue that this is not any different anywhere else – isn’t this entrepreneurship? Yes, but it is particularly brutal in China.   🔹 China vs. US? No one talks about it – that’s not what entrepreneurs think about People often discuss the race between the US and China in robotics, but the entrepreneurs themselves don’t. For them, it’s about survival and improving the robot’s functionality and price point to be cost-effective in replacing or supplementing human labor. They are mostly competing against time and technology.   🔹 A humanoid in every home? It will happen in China first We can make fun of the humanoid Olympics, or all these robots that seem clumsy and limited in their dexterity. I think the progress will be faster than we think. For these entrepreneurs, their vision of the future is one where there are armies of humanoids in factories, warehouses, and restaurants. A day will come when we have a humanoid in every home, and that day may not be far away.   Watch this space.

95% of healthtech startups don’t survive their first real market test. Not because of the product. Not because of funding. But because they misunderstood what the real test even is. Let me explain. Most founders think the test is: Will users try it? Can we grow fast? Does the product work? But in healthtech, the true test is this: Can the market trust you enough to use your product in a clinical workflow, at scale, without hand-holding? That’s where 95% of startups fail. Because healthcare doesn’t reward novelty. It rewards credibility, compatibility, and continuity. Here are 4 brutal truths I’ve learned after 25+ years in this space: 1. Healthcare doesn’t buy tech. It buys trust. Even if your ML model is 95% accurate, it won’t be adopted unless people trust how it works and why it works. If clinicians don’t understand it and decision-makers can’t defend it, they won’t risk patient care or reputations on it. 2. Pilots aren’t validation unless they prove real-world value. A controlled trial is only useful if it demonstrates measurable improvements - like saved clinician time, lower readmission rates, or better outcomes. Without that, it’s just a demo, not validation. 3. Integration beats innovation. If your product forces staff to log into a new system, learn new workflows, or switch between screens - it won’t scale. The best products blend into existing tools and workflows, not break them. 4. Good storytelling doesn’t secure funding. Proof does. You can impress with a flashy deck, but serious investors and clinical buyers want published studies, cost-benefit analyses, and evidence of adoption. Credibility beats charisma - every time. So if your startup fails at the first market test - it probably wasn’t bad tech. It was bad strategy. Too many teams build with optimism instead of realism. If you’re pre-launch or pre-scale, ask yourself: Who exactly will use this every day? Who will approve and pay for it? What will they stop doing once they adopt it? Healthtech isn’t about hacking growth. It’s about building trust - through data, design, and delivery. I’ve seen brilliant teams crash because they built for what should work - Not what actually gets used. You don’t need a better product. You need a better go-to-market reality check. Have you seen healthtech ideas die at the last mile? What caused the failure? #healthtech #founders #startups #innovation

Startups don’t fail because founders don’t work hard. They fail because founders think they should already know how to do it. Often, startup founders reveal their desperation to me during coaching sessions: 💔 Failed project milestones due to unforeseen reasons; 💔 Delayed launch plan that didn't work out as planned, 💔 Teammates who did not perform as they should; 💔 Incapability to raise funds, etc. They wonder, "Am I even capable of running a startup?" Here’s actually the real question: Why do we assume that just because we’re passionate about solving a problem, we should instantly know how to build a company? 🎾That’s like saying, "I love tennis, so I should win Wimbledon." Yet, we trick ourselves into thinking that starting a business means we should already have all the answers. And when things go wrong, we take every setback as proof that we’re not good enough. But entrepreneurship is not about knowing. It’s about learning. 🔹We start. 🔹We learn, 🔹We experiment, 🔹We put into practice, 🔹We struggle, 🔹We fail, 🔹We learn from the failure, 🔹We experiment, 🔹We struggle again, 🔹We make mistakes, 🔹We learn again, After hundreds of iterations, we finally succeeded... Not because we knew everything from the start, but because we learned along the way. The real danger? Thinking you already know how to do it. That mindset robs you of growth. Every challenge becomes a failure, every mistake a personal flaw. You cannot learn what you think you already know. The moment you embrace the struggle as part of the process, you stop blaming yourself and start asking: What is this teaching me? That’s when real progress begins. #Entrepreneurship #GrowthMindset #FailureIsFeedback #StartupJourney

The Hard Truth: 𝟴𝟬% 𝗼𝗳 𝗠𝗲𝗱𝗧𝗲𝗰𝗵 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀 𝗳𝗮𝗶𝗹 𝘄𝗶𝘁𝗵𝗶𝗻 𝟱 𝘆𝗲𝗮𝗿𝘀 even with 7-𝗳𝗶𝗴𝘂𝗿𝗲 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗿𝗼𝘂𝗻𝗱𝘀 After working with 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗱𝗲𝘃𝗶𝗰𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 over the past decade and supporting many 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝗺𝗮𝗿𝗸𝗲𝘁 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝘀. I’ve seen clear patterns that separate the winners from the ones that quietly disappear Here are 𝟳 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗳𝗮𝗰𝘁𝗼𝗿𝘀 that consistently predict MedTech success: 1. 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲 𝗠𝗶𝘀𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 • Most startups underestimate FDA timelines by 𝟳𝟬%+ • They plan for 8–12 months when 𝟭𝟴–𝟮𝟰 𝗺𝗼𝗻𝘁𝗵𝘀 is more realistic • Early Q-Sub engagement can reduce timeline overruns by 𝟯𝟬% 𝗶𝗳 𝗻𝗼𝘁 𝗺𝗼𝗿𝗲    2. 𝗗𝗲𝗹𝗮𝘆𝗲𝗱 𝗤𝗠𝗦 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 • 𝟮 𝗼𝘂𝘁 𝗼𝗳 𝟯 startups try to build their QMS too late • Retroactive documentation costs atleast 𝟯𝘅 𝗺𝗼𝗿𝗲 • Phased QMS rollout leads to up to 𝟰𝟬%+ 𝗳𝗮𝘀𝘁𝗲𝗿 𝗺𝗮𝗿𝗸𝗲𝘁 𝗲𝗻𝘁𝗿𝘆    3. 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗚𝗮𝗽𝘀 • 𝗢𝘃𝗲𝗿 𝟱𝟬% of 510(k)s receive AI requests due to weak data • Average delay: 𝟳+ 𝗺𝗼𝗻𝘁𝗵𝘀 • Engaging KOLs early boosts study enrollment by 𝟯𝟬%    4. 𝗥𝗲𝗶𝗺𝗯𝘂𝗿𝘀𝗲𝗺𝗲𝗻𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗮𝘀 𝗮𝗻 𝗔𝗳𝘁𝗲𝗿𝘁𝗵𝗼𝘂𝗴𝗵𝘁 • Only 𝟭 𝗶𝗻 𝟰 startups plan reimbursement before FDA submission • Average time from approval to revenue: 𝟭𝟯+ 𝗺𝗼𝗻𝘁𝗵𝘀 • Payer engagement during clinical planning can 𝗰𝘂𝘁 𝘁𝗵𝗮𝘁 𝗶𝗻 𝗵𝗮𝗹𝗳    5. 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗩𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 • 𝟴𝟬%+ rely on single-source components • Average shortage delay: 𝟰–𝟱 𝗺𝗼𝗻𝘁𝗵𝘀 • Dual sourcing significantly reduces disruptions    6. 𝗡𝗲𝗴𝗹𝗲𝗰𝘁𝗶𝗻𝗴 𝗣𝗼𝘀𝘁-𝗠𝗮𝗿𝗸𝗲𝘁 𝗦𝘂𝗿𝘃𝗲𝗶𝗹𝗹𝗮𝗻𝗰𝗲 • EU MDR has driven PMS costs up • Most startups allocate just 𝟲% of OpEx to PMS • Real-world need: 𝟭𝟮–𝟭𝟱% in the first 2 years post-launch    7. 𝗜𝗻𝘃𝗲𝘀𝘁𝗼𝗿 𝘃𝘀. 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲 𝗠𝗶𝘀𝗺𝗮𝘁𝗰𝗵 • Investors expect revenue 𝟭𝟴 𝗺𝗼𝗻𝘁𝗵𝘀 𝗽𝗼𝘀𝘁-𝗦𝗲𝗿𝗶𝗲𝘀 𝗔 • Reality? It takes 𝟯𝟭+ 𝗺𝗼𝗻𝘁𝗵𝘀 on average • Companies that align timelines tend to secure significantly more follow-on funding    𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: MedTech success is about 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗮𝗰𝗿𝗼𝘀𝘀 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆, 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹, 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴. Startups that get these 7 factors right often reduce time-to-market by 𝟯𝟬–𝟰𝟬% and increase their 5-year survival rate to 𝗼𝘃𝗲𝗿 𝟲𝟬% 💬 Want to explore how your startup can beat the odds? Let’s talk, drop me a message to connect

Talking about the future of driverless cars… China is not preparing for it - they are already living it. The rest of us? Just watching. While much of the world debates autonomous cars in courtrooms and pilot programs… #China is already operating them at scale. Watch this 1-year-old video, which is not a prototype, but a glimpse into what is already reality there. At Auto Shanghai this year, it was no more about Teslas or BMWs that owned the spotlight. It was BYD’s Denza, Huawei’s luxury sedan, and Pony.ai’s next-gen robotaxis marketed less like cars, more like smartphones. Ultrafast charging. Facial recognition. Self-driving baked in. 🤯 ❇️ And here is the reality check: ▶️ By 2030 → 20% of new cars sold in #China will be fully driverless. ▶️ 70% will have advanced assisted-driving tech. ▶️ 5 Chinese firms already operate 2,300 robotaxis in 30 cities. ▶️ The US? Waymo runs ~700 across just five cities. ❇️Why is China ahead? ➡️ National strategy - AVs treated as a strategic industry, not just a tech experiment. ➡️ City-level competition - Beijing, Shanghai, Shenzhen competing to host pilot zones, fast-track permits, and build AV-ready roads. ➡️ Digital-first infrastructure - AVs need constant connectivity; China’s high-speed telecoms and smart traffic networks make it possible anywhere, even at 15,000 ft elevation. ➡️ Ecosystem advantage - Baidu, Inc., Alibaba Group, Huawei, Xiaomi Technology are integrating AVs into existing digital lifestyles. ➡️ Data flywheel - Looser privacy rules + mass adoption mean more data → better models → faster scaling. ❇️ The cultural edge - 85% of Chinese consumers are comfortable with fully driverless cars. - In the US? 39%. - And vandalism against AVs is far more common in the West. ❇️ Not without risks - Rapid adoption has brought safety scares - fatal crashes, tighter regulations, and bans on flashy “smart driving” marketing. - But rather than slowing down, regulators are building safety frameworks to resume Level 3 approvals by 2026. ❇️ The bigger picture - Chinese AV firms aren’t just dominating at home - they are exporting to Southeast Asia, the Middle East, and Europe. - Emerging markets want to partner with China to leapfrog Western infrastructure and mobility thinking. Meanwhile… much of the West is still stuck treating autonomous mobility as a “future project.” In China, it is already a national strategy. Do you think the West can catch up or will China set the rules of the road for the driverless era? Ref: Rest of World #autonomousvehicles #china #mobility #ai #strategy #ev

As somebody who has worked in venture capital, at leading battery startups, and now at one of the United States' most technologically advanced EV companies - there is more in this article that I agree with than disagree with. Anybody who sets boots on the ground in China's battery sector inevitably returns with the realization that 90% of the early stage prototypes in the US have already been attempted at scale in China. And usually at half the cost thanks to advanced equipment, lights-out factories, and a robust supply chain. In other words, it's not just cheap labor and government policies. We need more conversation around this in our energy venture capital community. Our venture capital portfolios have become a graveyard of moonshots—each one riskier and more speculative than the last. I worry this increasing risk taking is not because of bold vision, but because of structural desperation. As previous bets fail to return capital and our gap to state-of-the-art technology grows, funds are forced to chase newer or more improbable outcomes, cornering themselves (and our country) into a cycle of diminishing relevance. The inability to engage with manufacturing technology, to build from the ground up, has left the U.S. ecosystem with little more than pitch decks and prototypes while China ships product. In fact, some of the famous startups you've heard of are actually importing white-labeled batteries from China to kickstart their "novel" battery technology stack. Others are giving up in the face of manufacturing hurdles by simply licensing key pieces of their tech for others to make. Until we recalibrate our investment theses to reward industrial competence and iterative progress, we will continue to fall behind—not just in market share, but in the very knowledge required to lead. Tag your favorite energy investor. https://lnkd.in/dFUWQEs5

"Cycle time", the key concept to Chinese success Our current tariffs fixation is mainly due to the increasing commercial competition between the US and China. Thomas Friedman has produced a sobering paper about this competition after visiting China: https://lnkd.in/gVFG_hsH Here is my personal analysis which emphasizes one element of Friedman’s paper: speed, or more precisely, "cycle time". When I was the CEO of NASDAQ-listed company ILOG, in 2008, we had a subsidiary in China with 80 brilliant employees. Because our optimization technology powered over 50% of supply chain vendors at that time, we had also created a joint venture with Bao Steel to build their own supply chain management system. The 8 Chinese engineers of that joint venture would be at Bao Steel in the morning, come back to their office in the afternoon, gather around a white board and hammer the changes in their software that were required to address their customer’s requests, then go code it in their cubicles, until late at night when they would redeploy a new version of the software to be tested by the customer the next morning, and the cycle would repeat five days a week. I had never seen such a rapid rate of improvement for application software. After we sold ILOG to IBM, I attended a private conference of MIT alumni around 2010 where I met a MIT-trained Chinese VC. He explained to me that he was no longer investing in US start-ups but only in Chinese ones. I asked why, and he responded: “westerners have too long cycle times”. Up to this moment, I had only interpreted the formula “cycle time” as a technical supply chain management term, only applicable to plants. This is when it dawned on me that the concept could apply to complete civilizations: from new products elaboration to customer service to administrative action, the idea that faster turn-around of anything leads to higher productivity and lower costs is an essential one in modern civilizations. It is at the heart of the success of Elon Musk for SpaceX for example. It is also one of the key impediments to European success: when a civilization has no regard for “cycle time”, it will be subject to the diffuse loss of performance that complex regulations, slow administrative processes and paralyzing strikes impose on itself. As AI enables us to automate many white-collar tasks, and humanoid robots will do the same for many more manual tasks, a focus on “cycle time” will pay increasing dividends, creating the possibility of an exponential advantage to those who master it. Tariffs can temporarily recreate a level playing field. However, not paying attention to the long-term competitive advantage brought by scientific knowledge and cycle time is a recipe for long-term failure.