If It's Not a Clear Yes, It's a No: A Nine-Year Retrospective by a VC Who Survived Four Cycles

Foresight NewsОпубліковано о 2026-06-24Востаннє оновлено о 2026-06-24

Анотація

**"Invest Only When Certain": A Nine-Year Retrospective from a VC Across Four Cycles** IOSG founder Jocy shares hard-earned lessons from nine years and over a hundred investments in Web3. The core challenge isn't identifying successful founders, but understanding why talented founders with solid ideas still fail. Through building a "failed founder database," IOSG identified six recurring failure patterns. **Founder Trait Red Flags:** 1. **Emotionally Unstable:** Founders who react defensively to criticism or publicly lash out under pressure (e.g., 80% drawdowns) often fail. Resilience is key. 2. **Lacking Hunger / Having a Fallback:** Founders with significant safety nets (family wealth, cushy fallback jobs) may lack the "do-or-die" commitment needed to survive crypto's brutal cycles. 3. **Unchecked Ego:** Includes "polished execution machines" who excel in known frameworks but struggle when paradigms shift, and "professor-types" who are technically brilliant but resistant to commercial feedback or coaching. **Project Structure Red Flags:** 4. **Token-First, Not Product-First:** Treating the token solely as a fundraising tool with no real utility or connection to product value is a major warning sign. The project should have value even if the token goes to zero. 5. **No Day-1 Exit Thesis:** Founders must have a clear, staged capital strategy from the start, understanding what each funding round needs to prove to unlock the next. "Exit before entry" is crucial. 6. **N...


Interviewee: Jocy, Founder of IOSG

Author: Joe Zhou, Foresight News


After nine years of investing, we have gradually come to realize one thing: the hardest question to answer is not 'what kind of founder can succeed,' but rather—assuming the right sector was chosen and the direction was sound, why do some exceptionally qualified founders ultimately fail to survive?


Half the answer lies with the founder, and the other half falls on the direction they chose and the timing of their entry. Having experienced four cycles, certain patterns begin to recur—though each founder's story is different, and every market backdrop varies.


But one conclusion has become increasingly clear: successful founders each have their own brilliance, while failed founders are strikingly similar.


Over nine years and across hundreds of portfolio projects, we have witnessed the rise and fall of too many Web3 entrepreneurs. Every failed investment comes at a real monetary cost—anywhere from millions to tens of millions.


To prevent the same mistakes from repeating, I built a 'Failed Founders Database.' The purpose is simple: to stop myself from stepping into the same river twice.


The capital markets never offer a 'do-over' option, but we can choose to turn others' pitfalls into our own road signs. Laying out these failures is both to improve our own hit rate and to help more entrepreneurs avoid unnecessary detours.


6 Archetypes of Failed Founders


I have a habit: each quarter, I individually review every deal with each colleague; every six months, the entire team does a deep alignment; and at year-end, I pull out a list to fully disclose all the successful and failed projects we have invested in.


The first half of 2026 just concluded. Taking advantage of this review, we supplemented and summarized a set of 'Failed Founder Archetypes.' With this database, we hope to turn our past missteps into muscle memory, steering clear of those potentially fatal hidden reefs in advance.


After repeated validation over four cycles, these failure patterns have gradually become clear—though each founder's story is different, and every market backdrop varies, the underlying logic is strikingly similar.


However, before expanding on them, it needs to be clarified: the following patterns fall into two categories. One category is at the founder-trait level, concerning a person's temperament, resilience, judgment, and self-awareness; the other is at the project-structure level, concerning choices like token design and capital strategy. The former is about the person, the latter about the venture.


Founder Trait Archetypes


This category of problems is rooted in the founder's own personality, mindset, and intrinsic drive. They have nothing to do with technology or sector, yet they are often the primary culprits that kill a project.


Type 1: The Emotionally Volatile


This is the most fatal type. When a project faces an 80% drawdown, its community is under concentrated attack, or there's been no progress for three consecutive months, how the founder reacts almost determines whether the project can survive.


Failed founders in these situations get caught up in their emotions—repeatedly justifying themselves on Twitter, erupting into internal conflicts with co-founders, arguing with users in community groups. Successful founders, under the same pressure, are already breaking down the problems and working on Plan B within the first week.


Actually, you don't have to wait for a drawdown to see it. It can be tested before investing: respectfully push back on (challenge) their core assumptions during due diligence and observe their reaction. Some founders will engage in serious debate with you, standing firm where they should, making adjustments where needed, remaining composed throughout. Others become defensive or even retaliatory when respectfully challenged. What the latter reveals is more telling and more reliable than how they might behave during an 80% drawdown.


Type 2: Lacks Hunger / Has a Fallback


This type is easily overlooked because it's not 'obvious.'


If a founder has a sufficiently comfortable safety net behind them—whether it's family wealth, a high-paying fallback option at a big company, or a mentality of 'it's okay if it doesn't work out'—their choices during the darkest moments often deviate from the optimal path. Entrepreneurship is a life-or-death endeavor; without the foundation of 'total commitment,' it's difficult to weather cycles.


We once discussed a project on an IC (Investment Committee) where there was a huge internal disagreement during the vote. It was a team that Paradigm and a16z were willing to bet on. The founder had an excellent family background, was simultaneously an LP for several US mega-funds (top-tier VC firms with over $5 billion under management), and those mega-funds were all willing to back him. Just looking at the investor lineup, this was one of the most beautiful deals we had ever seen.


But our debate on this project lasted until 1 a.m. that day. In the end, I cast a veto.


The reason was that I believed what this founder wanted to do challenged human nature too much. His idea was to build an encrypted bank for the African market, which required establishing an on-the-ground execution team of over a hundred people locally. The founder himself grew up in the US and China. To succeed, he would have to actually move to Africa, live and work there long-term, and be immersed in the front lines. He repeatedly emphasized his determination in the meeting, saying he would go all-in to penetrate the African market.


But it was precisely this kind of team that 'looked right in every way'—endorsed by top-tier firms, a perfect investor structure, and an impeccable verbal commitment from the founder—that we ultimately pressed pause on. This project later did achieve TGE (Token Generation Event), but it fell far short of their initial vision of becoming an 'African neobank.'


This is the same principle as the 'execution machine.' A team can score perfect marks on every quantifiable dimension: firm backing, structure, resume, determination, plan. But the most crucial thing in entrepreneurship is often precisely the one thing that can't be quantified—the unique, essential fit between this person and the thing they want to do. Checking every box can ironically make it easiest to overlook this question.


We've reviewed this many times since: the problem was never that he wasn't good enough, but that he was too good at everything, good enough to almost make us forget to ask the one important question—does someone who grew up in the US and China truly want to, can they, and are they willing to actually spend the next five years of their life on the front lines of sales in Africa?


Type 3: Unchecked Ego


The external manifestations of this type are often the 'finely decorated execution machine' or the 'professor-type founder.'


First, the execution machine. Founders with exceptionally refined OKR systems, decks that look like McKinsey reports, and who list 'execution' as their top advantage—our data shows these people raise large amounts but attract fewer subsequent investors and have poor exit performance. Because they excel at finding optimal solutions to known problems, but in Crypto, the ground often shifts. A finely decorated house is much more fragile than a rough one.


But to add: whether an execution machine is a problem depends on the sector.


On already validated, mainstream directions, distribution, hiring, and repeated execution are the keys to victory. A finely decorated, execution-oriented founder might be the perfect choice. The problem only arises in emerging, non-consensus directions—there, you need someone more imaginative and willing to navigate ambiguous territory. So this isn't an ironclad rule, but a judgment of founder-market fit.


Now, the professor-type founder. Their technical understanding is usually the deepest in the room and deserves respect. But we pay special attention to two questions: first, whether they truly understand business and are willing to make compromises for commercial viability; second, whether they are coachable—willing to learn and willing to change.


When a professor acts as the teacher and treats VCs as students, the project typically gets stuck. Technical depth does not equate to product judgment, much less to commercial execution.


We have also invested in founders with extremely deep technical backgrounds and sharp commercial acumen. The key isn't the degree, but whether they treat technology as a means and commercial success as the goal, and whether they are coachable.


There's an even more subtle layer: fallback options.


People from big tech companies and academia often have excellent fallback options. Once a project starts to decline, they can more easily retreat to the comfort of big tech or academia. This doesn't mean they are weak founders, but it might indicate they lack that hunger of having no way back and having to prove themselves. We value that drive of 'if I lose, I truly have nowhere to go' more.


Finally, there's the path-dependent type—those from big companies or winners from the last cycle directly replicating their playbook. We call this 'using methods from the last cycle to do things in this cycle.' Dai Yusen recently made a similar observation: 'It's difficult to beat ByteDance within ByteDance's own rules.' The same principle applies: winners from the previous era are most likely to lose to the next era.


Project Structure Archetypes


This category concerns how founders understand the underlying architecture of their project—what a token truly is, how capital strategy should be designed, and whether they have personally experienced the harshness of cycles.


Type 4: Token-First, Not Product-First


This is unique to Crypto and also one of the most dangerous types.


It's different from the previous types—the problem isn't with the founder's personality, but with their choice regarding project structure. However, this choice itself reveals what they truly regard as the core.


The typical manifestation is: keeping revenue and equity in a separate corporate entity, with the token merely serving as a fundraising tool, and token holders having no claim to the actual business's cash flow.


We believe whether a token is a fundraising tool or the skeleton of the product determines whether a founder can survive cycles.


The judgment standard is simple: if the token goes to zero tomorrow, does this project still have value? If the answer is no, then the token is their everything, and the product is merely its packaging.


Type 5: No Day 1 Exit Thesis


This is a principle our team has always emphasized—'Exit before Entry.'


If a founder cannot explain on Day 1 how they plan to exit in 3 years (acquisition, token liquidity exit, or the company itself going for an IPO), then their fundraising narrative to investors will constantly be distorted.


Rather than saying a founder must have the future exit path figured out on Day 1, it's more about understanding the sequencing of capital strategy and milestones: what does this funding round need to prove? What data will unlock the next round? How might future investor returns materialize? Early-stage projects are often emergent, and the final exit path—be it acquisition, token liquidity, or IPO—might be uncertain. But 'what this round is for, and what will support the next round' must be thought through.


Failed founders often say, 'We are raising for a larger vision.' Successful founders say, 'I am raising this round today to be able to secure the next round 18 months from now, and the metric for that next round is XX.'


The Final Dimension


The first five types share a common undertone—they are all red flags (Note: Red Flag in investment context means danger signal, warning sign).


Specifically:

  • Founder Trait Red Flags: Emotionally volatile, lacks hunger/has a fallback, unchecked ego.
  • Project Structure Red Flags: Token-first, no clear capital strategy.


But the sixth type is different. It's not a red flag; it's a pricing issue.


Type 6: Never Experienced a Full Cycle


Crypto has a full cycle every 3 to 4 years.


A founder who hasn't personally experienced at least one full bull and bear market will severely underestimate their own vulnerability during their first bear market. This isn't an ability problem; it's an experience problem—you haven't seen it, so you don't know what that pressure feels like.


This has become a hard sizing policy for us: early-stage teams without full cycle experience have their initial investment amount capped at $250k.


The judgment standard is also simple: what were you doing in 2018 and 2022?


But this type differs from the first five.


The first five are red flags, helping us identify 'who to avoid.' The sixth type is not a red flag; it answers a different question: 'for whom can we place a bet, and how big a bet?'


Strictly speaking, lacking full cycle experience is not an automatic veto—it's more of a pricing factor. Those who have experienced full cycles often better understand managing volatility, handling community pressure, and the psychology of downturns. But exceptional geniuses without cycle experience do exist.


So our approach isn't to outright reject, but to hedge with sizing: for early teams without full cycle experience, the initial investment is capped at $250k, waiting to add more after seeing stronger evidence of execution.


Inverting the Failure Archetypes Gives Us the People We Like


Listing failure archetypes isn't about labeling people, but to help us understand more clearly: conversely, what kind of people are worth betting on.


Type 1: Obsession with the Problem.


The best founders aren't just interested in a problem; they are consumed by it. They've thought through edge cases, user behavior, how competitors will react, and second-order consequences. They aren't pitching you a product; they live inside that problem. This is the hardest to fake and the strongest positive signal in reference calls—you can sense whether someone truly spends 24 hours with what they want to build.


Type 2: Second-time Founder + Non-Consensus Vision


I particularly value second-time founders who have experienced failure.


The failure here refers to setbacks at the project level where they have understood the reasons, not the fatal character flaws mentioned earlier. The two are completely different.


Failure itself means little; the key is whether they can figure out why they failed after the fact.


More importantly, they must have their own non-consensus thesis—not someone following Twitter hype and second-hand information, but someone who truly thinks independently and dares to make anti-consensus judgments.


Type 3: Strong Communication + Controlled Ego


Communication skills deserve special mention because they are so crucial. A founder needs to explain complex ideas clearly—to users, investors, partners, employees, the community. We've seen too many technical genius founders who write beautiful code but can't articulate their ideas. The project ends up in a state: when the team member who can communicate externally is absent, the whole project goes silent.


As for ego, it's more subtle than imagined.


We don't want simply 'low ego.' The benefit of low ego is being coachable and willing to listen to feedback. But for a founder to want to be the best, to prove themselves, to persevere through adversity—these all require some ego as fuel. The real danger is unchecked ego—rewriting the story when performance is poor, always placing oneself on the right side, ignoring contrary evidence.


So the key term isn't 'low ego,' but 'controlled ego': ambitious, but not delusional.


Type 4: Doesn't Evade, Sets No Limits, Strong Willpower


In Crypto, you are perpetually exposed to the public spotlight and high pressure. Without a foundation of willpower, you simply can't withstand cycles and will likely be crushed halfway. Internally, we have a core framework called the 'Key Question': the essence of early-stage investment isn't clinging to a single thesis, but continuously iterating the prior and posterior of each key question.


In simpler terms, it's Bayesian thinking—constantly updating one's judgment and beliefs based on existing information (prior probability) and new evidence (newly observed data), rather than rigidly holding onto a fixed conclusion. You can have strong opinions, but don't be held hostage by them—if the reasons change, the judgment must follow.


Type 5: Three Hard Metrics for the AI Era: Global Perspective, Agency, and Taste


Crypto has been the most globalized tech ecosystem since its birth—capital, talent, community flow in real-time across the globe. In an increasingly fragmented world, founders who build global businesses from day one are themselves a scarce commodity.


Now look at AI. It can solve in-distribution problems, but only humans can pose original, out-of-distribution questions. So we watch for two things: Agency (the ability to proactively break through situations) and Taste (aesthetic and judgment). These are becoming increasingly valuable in the AI era.


Only after a founder's creativity and imagination have been validated can AI become their amplifier, not their life raft.


Three Life-Saving Suggestions for Entrepreneurs: The Cost of Issuing a Token Far Exceeds Imagination, The Ticket is Millions of Dollars


We have an internal habit of being brutally honest during reviews, even to the point of being harsh. We ask ourselves: why did we make that decision then? Where exactly did that fatal mistake occur? How would we change it if we could do it again?


Recently, during portfolio management, we gathered all founders from our portfolio projects for a meeting and gave three harsh but potentially life-saving suggestions:


First, cash flow is far more important than narrative. The projects that will survive this round aren't relying on TVL or MAU, but on real, tangible cash flow.


Second, don't issue a token just for the sake of issuing one; a token is actually a heavy liability.


In the last cycle, a large proportion of newly issued tokens broke their issue price, with our internal statistics showing over 80% broke issue price.


So we advise our portfolio projects: don't issue if you can avoid it, delay issuance if possible. Why? Because the hidden costs after token issuance are far heavier than most imagine.


We've done the math: the hidden costs after token issuance, including market makers, liquidity, compliance, exchange relationship maintenance, etc., far exceed most people's imagination. In this cycle, this is a liability costing millions of dollars. If you haven't raised funds on that scale in the past few years, you simply can't afford to issue a token.


Third, respect liquidity.


Sell at the best times, buy at the worst times. The valuation at which a project raises funds today determines what performance it needs to deliver over the next three years to justify the next round of investment. If it can't justify it, it shouldn't raise that money. Additionally, decisively sell tokens when liquidity is at its best, and buy back to support your own protocol when the price falls below the issue price.


In today's market, many are turning to AI, many are fleeing Web3. Founders need encouragement, practitioners need support—everyone needs to gather together as a beacon of light. So we will continue to output research and judgment in this industry, offering the most genuine advice.


How Do We View Founders? Borrowing Three Frameworks from Zhang Yiming


The judgment criteria above didn't come out of thin air. We borrowed many external references, and I have been deeply influenced by Zhang Yiming personally.


He has a metaphor I always remember: empathy is the foundation, logic and tools are the middle layer, and imagination is the sky. Translating this to investment:

  • Foundation—Empathy: Can they treat people as ends, not means? Can they get along with the team, attract top-tier co-founders, and demonstrate true leadership? This is what we call 'emotionally stable, low neuroticism.'
  • Middle Layer—Logic and Tools: Can they use tools effectively and think structurally?
  • Sky—Imagination: Can they see those things that 'could exist but haven't appeared yet'?


Zhang Yiming's favorite interview question is: 'On what important matters do you hold a different view from the majority?' This question tests whether the person is an independent thinker, not just a 'repeater of mainstream media.'


Over half of people cannot answer it. We also often borrow this question during reference calls—if a founder cannot articulate three things they disagree with consensus on, they likely don't have a non-consensus thesis.


Beyond this, Zhang Yiming values two other points we also borrow: first, strong curiosity and hunger—willing to spend time envisioning those 'things that could exist but don't yet,' rather than staying in already validated areas making marginally diminishing small improvements. Second, long-chain thinking ability—the ability to push a problem to its logical conclusion independently without external feedback. Corresponding to the Crypto industry, this means whether they can thoroughly think through a thesis alone over 18 months without user feedback.


Final Thoughts


What we've summarized over nine years isn't how to find the best founders, but how not to misjudge.


But at the end of the day, these methodologies are just tools. Internally, we have an iron rule: even founding partners cannot casually push a project through the investment committee.


If it's not a clear yes, it's a no.


This sounds simple, but this is the entire secret to surviving cycles.


The foundation of Crypto is rebuilt every three years. What allows you to traverse cycles isn't one or two brilliant judgments, but whether you can repeatedly press that 'don't invest' button.

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QAccording to the article, what are the two categories of failure patterns observed in founders and projects?

AThe failure patterns are divided into two categories: Founder Trait-based issues (related to a person's character, mindset, and internal drive) and Project Structure-based issues (related to choices regarding token design, capital strategy, and cycle experience).

QWhat is the author's team's 'sizing policy' for early teams without full cycle experience, and why is this policy in place?

AFor early teams without full cycle experience, the initial investment amount is capped at $250,000. This is because lacking firsthand experience of a complete market cycle (e.g., 2018, 2022) means a founder may severely underestimate their own vulnerability and the pressure during a bear market. It's not an automatic veto but a pricing factor to manage risk.

QThe article mentions 'Token priority' as a dangerous failure pattern. What simple question does the author suggest to identify this pattern?

ATo identify the 'Token priority' pattern, ask: If the token went to zero tomorrow, would this project still have value? If the answer is no, then the token is the project's entire focus, and the product is merely its packaging.

QWhat are the three core pieces of advice the author gives to founders for survival?

AThe three key survival suggestions for founders are: 1) Cash flow is far more important than narrative. 2) Do not issue tokens just for the sake of it, as they represent a heavy liability with hidden costs (market making, liquidity, compliance, etc.) costing millions. 3) Respect liquidity: sell at the best times and buy at the worst to support your protocol.

QWhich three-part framework from Zhang Yiming (founder of ByteDance) does the author borrow and adapt for evaluating founders?

AThe author adapts Zhang Yiming's framework of: 1) Foundation - Empathy: Treating people as an end, not a means; having stable emotions and low neuroticism. 2) Middle Layer - Logic and Tools: Using tools effectively and structured thinking. 3) The Sky - Imagination: The ability to see things that 'could exist but do not yet exist'.

Пов'язані матеріали

DRAM ETF Issuer: Samsung, SK Hynix, Micron All Surpass $1 Trillion, the AI Era of Memory Chips Has Only Just Begun

Authors: Dave Mazza, Thomas DiFazio | Source: Deep Tide TechFlow The article, written by Roundhill Investments (issuer of the DRAM ETF), responds to Morningstar's caution about investing in memory chip stocks. Morningstar warns of the sector's history of boom-bust cycles, a lack of economic moats, and potential momentum-driven overvaluation. Roundhill argues the current situation is structurally different due to AI. Key points in Roundhill's rebuttal include: * **Changed Demand & Supply Dynamics:** AI infrastructure, not consumer electronics, is now the primary growth driver for memory demand. New, strict long-term supply agreements with hyperscalers reflect the high capital intensity of advanced manufacturing. * **Existence of a Moat:** High-Bandwidth Memory (HBM), essential for AI, has extremely high manufacturing barriers. The market is dominated by Samsung, SK Hynix, and Micron, with new entrants blocked by technological complexity and long lead times for equipment like ASML's EUV machines. * **Strong Fundamental Outlook:** Analyst consensus projects the three companies will rank among the world's most profitable by 2027, with combined profits of $704 billion on over $1 trillion in revenue. Their operating margins have already reached record highs. * **Valuation Re-rating:** Despite significant stock price gains, memory stocks trade at attractive valuations (e.g., a median NTM P/E of 8.37x for the DRAM ETF) relative to projected explosive EPS growth. Roundhill suggests historical valuation frameworks may no longer apply given the new profitability paradigm. Conclusion: Roundhill contends the rally is justified by fundamentals, marking a structural shift for the memory industry into a new era of sustained, AI-driven demand against constrained supply, rather than a repeat of past cycles.

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EF's Epic Reorganization: 20% Layoffs, Budget Halved, Is Ethereum Gearing Up for a Leaner Future?

The Ethereum Foundation (EF) has announced a major organizational restructuring, involving a 20% staff reduction (approx. 54 employees) and a division into functional clusters like Protocol, Access, User, Community, and Institutional layers. Co-founder Vitalik Buterin further revealed plans to cut the EF's budget by around 40% over the coming years, aiming to reduce its annual spending rate from about 15% to roughly 5% by 2030, transitioning to an endowment-driven model. This overhaul is seen as a long-overdue correction to the EF's ambiguous role. As Ethereum grew, the foundation faced persistent criticism over ETH sales, perceived lack of execution, and unclear strategy, often becoming a focal point for community frustration amid ETH's price stagnation. The reform aims to redefine the EF's boundaries, narrowing its focus to core protocol research, public goods funding, and ecosystem coordination, while offloading more applied development work to the broader market. Concurrently, ecosystem forces like the newly formed Ethlabs (founded by ex-EF researchers) and other independent groups are stepping in to fill the space, signaling a shift from a centralized model to a more distributed, collaborative ecosystem structure. The move was notably praised by Solana co-founder toly, who viewed a "leaner" EF as potentially more decisive and agile.

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EF's Epic Reorganization: 20% Layoffs, Budget Halved, Is Ethereum Gearing Up for a Leaner Future?

Odaily星球日报1 год тому

Dragonfly Partner Haseeb: The Fastest-Growing Companies of the Future May All Get Stuck at 149 Employees

Dragonfly partner Haseeb explores the distorted economics of AI model pricing, drawing parallels to tax policy. He notes that startups and small teams (under 150 users) enjoy heavily subsidized, fixed-price AI subscriptions (like Claude Code), where the marginal cost of an additional token is effectively zero. This creates a powerful incentive for them to maximize token usage ("token-maxxing") and innovate aggressively with AI automation. In contrast, large enterprises (over 150 users) are forced onto "Enterprise" plans, paying per-token API fees with high (~75%) markups. This acts like a steep "tax" on AI-powered labor, disincentivizing marginal automation and experimental use, and encouraging them to retain more human workers. Haseeb argues this pricing creates a "150-person cliff," a regulatory notch similar to labor laws in France that discourage firms from growing past 50 employees. He predicts the fastest-growing future companies may deliberately cap their headcount at 149 to avoid the punitive enterprise pricing. This would foster an "AI-first" management philosophy obsessed with automation and outsourcing to stay lean. While not intentionally designed, this bifurcated pricing could become one of the most influential de facto tax policies, shaping how AI replaces labor—not through mass layoffs at big firms, but through agile, AI-native startups outcompeting them.

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