The banking crisis never really ended. It just moved from front-page panic into a slower, quieter phase: commercial real estate stress, unrealized losses, deposit sensitivity, and the growing question of what happens when trust in traditional rails weakens again. For crypto, this is not only a macro story. It is a reminder of why onchain finance exists in the first place👇 ~~ Analysis by @0xAlexCashman ~~ The Crisis Is Not 2008, But It Is Not Gone The mistake is to frame every banking stress event as another 2008. This cycle looks different. The system is more capitalized, regulators are more alert, and the largest banks are not obviously sitting on the same type of mortgage leverage that broke the system last time. But that does not mean the risk disappeared. The current pressure is more subtle: higher rates repriced bond portfolios, commercial real estate remains under stress, depositors are more willing to move money quickly, and smaller banks are still exposed to asset-liability mismatches. The FDIC’s latest data does not show a full systemic breakdown. Problem banks remain within a normal non-crisis range, and no major wave of failures is currently visible. But the same reports also show persistent weakness in certain loan books and elevated unrealized losses. That is the uncomfortable part. A system can look stable at the surface while still carrying slow-moving fractures underneath. Why Crypto Should Care Crypto people often talk about banking risk as if it only matters for banks. That is wrong. The 2023 banking panic showed that crypto is still deeply connected to traditional finance. When Silicon Valley Bank failed, USDC temporarily lost confidence because part of Circle’s reserves were trapped inside the banking system. That moment was important because it broke a simple illusion. Stablecoins may live onchain, but many of their reserves still live offchain. This means crypto can inherit banking risk even when users think they are holding a digital dollar. If the banking partner fails, the stablecoin can face redemption pressure. If payment rails close over the weekend, liquidity becomes fragmented. If regulators step in, the market has to wait for clarity. In other words, crypto does not escape the banking system just by tokenizing dollars. It only changes where the risk becomes visible. The Real Stress Point Is Trust Banking runs are not purely about balance sheets. They are about confidence. A bank can be technically solvent and still fail if depositors no longer believe they can access funds quickly. In a digital world, that trust can disappear in hours. This is where crypto changes the psychology of finance. Onchain markets operate continuously. Stablecoins move 24/7. DeFi liquidations happen automatically. Treasury-backed tokens can be monitored in real time. Wallets do not close for the weekend. That does not make crypto risk-free. But it does create a different expectation: users increasingly want financial systems that are transparent, portable, and always available. When banks stress, crypto’s value proposition becomes easier to understand. Not because “banks are dead.” But because people suddenly remember that access, settlement, and custody are not abstract concepts. They are the whole system. Stablecoins Become The Bridge And The Weak Point Stablecoins are probably the most important part of this story. They are the bridge between traditional finance and onchain finance, but also the place where both risk models collide. On one side, stablecoins give users fast settlement, global transferability, DeFi liquidity, and a programmable dollar layer. On the other side, they rely on reserve management, banking access, short-term Treasuries, custodians, audits, and regulatory treatment. That makes them powerful, but not fully independent. A future banking crisis would likely increase demand for stablecoins, especially outside the U.S., where users may want dollar exposure without relying on weaker local banking systems. At the same time, it would also increase scrutiny around who holds the reserves, how redemptions work, which banks are involved, and whether stablecoin issuers can survive stress without depending on emergency guarantees. The next stablecoin winners may not just be the biggest issuers. They may be the ones with the cleanest reserves, strongest banking relationships, most transparent reporting, and best redemption infrastructure. Bitcoin’s Narrative Gets Stronger, But Not Automatically Every banking crisis helps Bitcoin’s story. A scarce, non-sovereign asset with self-custody and no bank balance sheet behind it becomes easier to explain when banks start looking fragile. But the market does not always move in a straight line. In a panic, investors often sell liquid assets first. Bitcoin can trade like a risk asset before it trades like a hedge. Liquidity shocks can hit crypto hard even when the long-term narrative improves. This is the paradox. Banking stress strengthens the philosophical case for Bitcoin, but it can also create short-term volatility across the entire crypto market. The same applies to DeFi. A banking crisis can make decentralized lending, onchain collateral, and transparent settlement look more attractive. But if the crisis hits stablecoin liquidity or risk appetite, DeFi can also suffer first. The direction depends on whether the market sees crypto as an escape route or as another high-beta asset to sell. Looking Ahead The next banking crisis probably will not look like the last one. It may be less cinematic than 2008 and less sudden than SVB, but more distributed across CRE losses, regional bank pressure, deposit flight, and confidence shocks. For crypto, the lesson is simple. The industry should not celebrate bank stress as if it automatically benefits onchain finance. The relationship is more complicated. Stablecoins still rely on banks. Exchanges still need rails. Institutions still need custody. Users still need fiat on and off ramps. But the direction of travel is clear. Every banking shock makes the case for transparent reserves, 24/7 settlement, self-custody, tokenized Treasuries, and neutral financial infrastructure a little easier to understand. The banking crisis is not just a threat. It is a stress test for the old system and a credibility test for the new one. Crypto does not win by saying banks failed. Crypto wins if it can prove that open financial rails are safer, faster, and more resilient when trust starts breaking elsewhere.

At one point in our episode with @0xbilly, we went down a bit of a dystopian rabbit hole. The conversation started with creativity, AI, and the speed at which ideas can now become real, but quickly moved into a bigger question: what happens when the world around us becomes easier to generate, manipulate, and personalize? Billy reflected on a future where AI does not just help us create faster, but begins to reshape how we experience reality itself. The line between imagination, media, identity, and environment starts getting thinner, and that creates both incredible possibilities and very real risks.

How will AI reshape our creativity and the world around us? Earlier this year, we opened Season 3 with @0xbilly from @ethereum for a conversation about what happens when the distance between imagination and execution starts to collapse. Billy spoke about a future where ideas can move from thought to form almost instantly, where creativity becomes less limited by tools, technical skill, or production bottlenecks, and more defined by taste, intention, and the ability to see clearly. In this clip, he reflects on how AI may change not only what we create, but how quickly the world around us can be redesigned once more people are able to turn ideas into reality at the speed of thought.

Two days ago, Anthropic launched Claude Fable 5 and Claude Mythos 5. On the surface, this looks like another frontier model release. But I think the more important story is not just capability. It is access. Fable 5 is the public Mythos-class model. Mythos 5 is the same underlying model, but with some safeguards lifted for trusted cyber defenders, infrastructure providers, and eventually select biology researchers. In its early stages, Anthropic is demonstrating what the next phase of frontier AI deployment may look like👇 ~~ Analysis by @punkbennet ~~ I, like many others, have become slightly numb to model launches. Every few months, a new model arrives with better coding, better reasoning, better long-context performance, better benchmark charts, better agentic workflows, better everything. At some point, the launch cycle starts to blur into one long benchmark war. But the Fable / Mythos release feels different. Not because Anthropic is claiming another step forward in intelligence, though it is. Not because the model seems strong at long-horizon coding, scientific reasoning, vision, finance, and complex knowledge work, though that matters too. It feels different because Anthropic is openly splitting capability into two layers: a public version and a trusted-access version. That is the real story. Fable 5 is described as a Mythos-class model made safe for general use. According to Anthropic, it exceeds any model they have previously made generally available and is especially strong on long, complex tasks. The model is available to general users, but it ships with classifiers that detect certain categories of high-risk use. When those classifiers trigger, the request does not get handled by Fable 5. It falls back to Claude Opus 4.8. The covered areas are cybersecurity, biology and chemistry, and distillation. In plain language: domains where the model’s raw capability could create meaningful risk if used badly, or where Anthropic believes unrestricted access could accelerate misuse or capability proliferation. Anthropic says these safeguards trigger in less than 5% of sessions on average, meaning most users should experience Fable as the full Mythos-class model most of the time. But that 5% is where the entire debate lives. Because if you are building a normal app, analyzing documents, writing code, doing finance work, or working on general research, Fable 5 may simply feel like a stronger frontier model. If you are doing security research, advanced biology, chemistry, or frontier model development, the product experience becomes more complicated. That is where Mythos 5 comes in. Mythos 5 is the same underlying model as Fable 5, but with safeguards lifted in some areas. It is not generally available. It is being deployed through Project Glasswing, Anthropic’s initiative with cyber defenders and critical software infrastructure providers. Anthropic says it plans to expand access through a broader trusted program. This is a meaningful shift from “everyone gets the same model” to “capability access depends on trust, use case, and risk category.” I do not think this is just product packaging. It is probably a preview of how frontier AI gets distributed from here. In crypto, we are used to open access as a cultural default. The whole industry is built around permissionless infrastructure, public networks, open liquidity, composability, and adversarial testing. The assumption is that if something is powerful, the network should expose it, and the market should figure out what survives. Frontier AI is moving in a different direction. The most capable systems are becoming too useful to keep entirely closed, but too risky to release without restrictions. That creates a middle layer: broad public access for most tasks, gated access for sensitive domains, and institutional partnerships for the highest-risk capabilities. There is a strong argument for this. If a model is genuinely good at finding and exploiting software vulnerabilities, then unrestricted release has obvious downside. Anthropic previously said Mythos Preview had found thousands of high-severity vulnerabilities, including some in major operating systems and browsers. Even if we treat that as an Anthropic claim rather than independent proof, the direction is clear: models are becoming serious cyber tools. That means the same capability can be defensive or offensive depending on who holds it. A security team using Mythos to audit critical infrastructure is very different from an unknown actor using it to automate exploit discovery. A biology researcher using the model to generate therapeutic hypotheses is very different from someone trying to gain dangerous biological uplift. The difficult part is that the boundary is not clean. Dual-use work is messy. Real security research can look like offensive security. Real biology can overlap with sensitive methods. Real AI research can look like distillation or capability extraction. If the classifier is too narrow, malicious users get through. If it is too broad, legitimate researchers get blocked or silently downgraded. This is why the transparency issue matters. After launch, Anthropic already faced backlash around invisible safeguards for frontier LLM development. The criticism was not only that the model had restrictions. Most serious users understand that frontier systems will have restrictions. The criticism was that some interventions were not visible enough to the user, which makes evaluation harder and damages trust. If a model refuses, that is annoying but clear. If a model falls back to a weaker model and tells you, that is also clear. But if a model quietly changes behavior, limits effectiveness, or routes around your task without making the intervention obvious, then developers cannot properly evaluate it. Researchers cannot know whether they are testing model capability, product policy, or invisible steering. That is a major problem. To Anthropic’s credit, they appear to have recognized this quickly and said they are changing Fable 5’s safeguards for frontier LLM development to make them visible. That is the right direction. Still, the tension does not disappear. The bigger question is whether frontier AI companies can build trust while also reserving the most powerful capabilities for trusted actors. This is not just about Anthropic. It is about the governance model of the entire AI stack. The public wants access. Developers want predictable behavior. Researchers want measurable capability. Governments want security. Labs want to avoid catastrophic misuse. Competitors want fair evaluation. Enterprises want privacy, reliability, and compliance. All of those demands collide inside a release like Fable / Mythos. Another under-discussed piece is data retention. Anthropic says Mythos-class traffic requires 30-day retention for safety monitoring, while also saying the data will not be used to train new Claude models and will be deleted after 30 days in almost all cases. That may be reasonable from a safety perspective, especially if the goal is detecting jailbreaks or coordinated misuse across many requests. But for enterprises, regulated industries, and sensitive research teams, it becomes a real deployment consideration. The more capable the model, the more likely users want to use it on sensitive work. The more sensitive the work, the more important retention policy becomes. So the model is not just competing on intelligence anymore. It is competing on governance. This is probably where the AI market is going. The best model will not simply be the one with the highest benchmark score. It will be the one that offers the best combination of capability, transparency, access control, reliability, compliance, cost, and trust. Fable 5 and Mythos 5 are interesting because they expose that full stack at once. There is the capability story: a model above Opus-class, built for long-horizon tasks and advanced reasoning. There is the safety story: classifiers, fallbacks, red-teaming, limited access, and trusted programs. There is the product story: public users get Fable, vetted users get Mythos. There is the trust story: users need to know when they are interacting with full capability and when safeguards are shaping the output. There is the market story: frontier AI is becoming less like a normal SaaS product and more like critical infrastructure. Personally, I think this release is one of the clearest signs that “open vs closed” is no longer the only useful framing. The new framing is closer to: who gets which capability, under what conditions, with what monitoring, and with what disclosure? That is less clean than the old debate, but probably more accurate. Based on the available information, Fable 5 may become an important public frontier model. Mythos 5 may become an important restricted capability layer for security and science. But the bigger experiment is the access model itself. If Anthropic gets the balance right, this could become a template for deploying very powerful AI safely while still letting most users benefit from the capability. If they get it wrong, it becomes a trust problem: too much opacity for developers, too much restriction for researchers, and too much central control over frontier capability. Either way, this is worth watching. Not just because Mythos looks powerful. Because it shows how AI labs may decide who is allowed to use power at all.

Really enjoyed my conversation with @jonwu_ from @aztecnetwork. We spoke about the way crypto narratives form, why so many conversations in this space become repetitive, and how founders can think more clearly about services, software, distribution, and trust. Jon has a very specific way of taking familiar topics and turning them slightly sideways, not just to be contrarian, but to get closer to what is actually happening underneath the surface. That made the conversation feel honest, sharp, and genuinely useful. Grateful to Jon for the time and perspective. And special thanks to the @web3Rehashed team, as well as to @MaxArt_eth and @hanasukai_eth for helping make it happen.

“Software is going to become the most competitive space in the entire universe.” @jonwu_ does not talk about crypto like it is only tokens, markets, or infrastructure. In this clip, he breaks down why services matter, why software keeps eating more categories, and why founders who understand distribution, trust, and customer pain will probably matter more than people just shipping another tool into the void.

“Most crypto debates get stuck because everyone is reacting to the same surface level framing. The interesting part is usually the thing people are not saying out loud.” This week’s guest Jon Wu (@jonwu_) has a rare ability to take topics that everyone in crypto talks about and rebuild them from first principles. Our host Diana (@onchainhost) sits down with Jon to talk about narratives, services, software, founders, and why the most useful perspective is often the one that goes against the grain without trying to sound contrarian for attention.

“What role does community play when the technology is still hard for most people to understand?” We chatted with Reka, Head of Community at @RiscZero, to unpack the state of zero-knowledge infrastructure and what it takes to bring complex blockchain protocols to market. We discussed Reka’s path from founder to joining RISC Zero, why ZK matters for the next phase of crypto, and how education, community, and clear positioning can help turn deeply technical products into ecosystems people actually want to join. Reka also shared her experience advising crypto projects, the challenges of building trust around emerging infrastructure, and why successful go-to-market in web3 is less about hype and more about helping people understand why the technology matters.

Recently, Polymarket was accused of being “hacked” after a threat actor claimed to have obtained 300K records from the platform. But based on the available information, this looks less like a classic crypto exploit and more like something deeper: the privacy problem of public on-chain markets👇 ~~ Analysis by @MaxArt_eth ~~ Polymarket’s response was direct: it denied that a breach happened and argued that the data was already accessible through public APIs and on-chain records. The alleged dataset reportedly included user profiles, names/images, proxy wallets, base addresses and market-related data, but Polymarket’s position is that this was not stolen private data: it was scraped public data. That distinction matters. A smart contract exploit means funds were drained. A database breach means private internal data was accessed. But this incident appears closer to the third category: public infrastructure being aggregated, packaged, and sold as if it were a private leak. And honestly, this is where Web3 gets uncomfortable. Crypto users are used to the idea that transactions are public. That is part of the pitch: transparency, auditability, open settlement, no hidden books. But there is a difference between “technically public” and “socially understood as exposed.” Most users know their wallet can be viewed onchain. Fewer users fully internalize that their market activity, linked addresses, profiles, comments, timing, trading behavior, and positions can be reconstructed into a readable identity graph. This is especially sensitive for Polymarket because prediction markets are not just DeFi trades. People are not only buying BTC or swapping tokens. They are expressing views on politics, elections, war, regulation, court cases, celebrities, companies, macro events, and sometimes deeply controversial topics. That makes the data more personal. A wallet’s trading history on a prediction market can reveal what someone believes, what they know, what they are exposed to, and sometimes what they may be incentivized to influence. So even if Polymarket is correct that no private system was breached, the incident still highlights a real issue: public data can become dangerous when it is indexed well. This is the same pattern we’ve seen across crypto for years. One isolated wallet address does not always tell you much. But combine it with an API, a profile, timing patterns, known deposits, social handles, comments, and counterparty behavior, and suddenly “public transparency” turns into surveillance. That does not mean Polymarket is uniquely broken. It means prediction markets sit at the intersection of three hard problems: financial privacy, information markets, and human identity. The bull case for Polymarket is that markets can produce better real-time probabilities than media narratives or expert commentary. The bear case is that once those markets become large enough, they create incentives for scraping, manipulation, insider activity, doxxing, and targeted pressure. This incident is not proof that Polymarket failed at custody or settlement. But it is a reminder that “on-chain by design” does not automatically mean “safe for users by design.” The next phase for prediction markets probably needs more than liquidity and better UX. It needs better privacy assumptions. Clearer user expectations. Better separation between public market data and user identity. Better rate limits and API design. And probably a stronger cultural understanding that “public” does not mean “harmless.” Polymarket may be right that this was not a hack in the traditional sense. But the market should not dismiss it completely. Because the more valuable prediction markets become, the more valuable their data exhaust becomes too. And if prediction markets are going to become serious financial and information infrastructure, this is the kind of edge case they need to solve before it becomes a larger one. Based on the available information, this was not a confirmed fund-draining exploit. But it was a useful stress test. Not of Polymarket’s contracts. Of Web3’s privacy model.

The CLARITY Act just cleared a key Senate stage. On May 14, the Senate Banking Committee advanced the Digital Asset Market Clarity Act by a 15–9 vote, moving it toward the full Senate. For crypto, this is not just another “regulation is coming” headline. It is one of the clearest signs yet that the US is moving from enforcement-first crypto policy toward a formal market structure framework👇 ~~ Analysis by @hanasukai_eth ~~ For years, the biggest problem in US crypto regulation was not simply that rules were strict. The bigger problem was that the rules were unclear. Builders, exchanges, funds, token issuers, DeFi teams, and even public companies had to operate in a system where the same asset could be treated as a security by one regulator, a commodity-like instrument by another, and a legal grey zone by everyone else. That uncertainty created a strange market. Capital wanted exposure to crypto, but legal departments were cautious. Founders wanted to build in the US, but many had to consider offshore structures. Institutions wanted clear custody, listing, disclosure, and market rules, but the framework was fragmented. CLARITY is trying to address that core issue. Based on the available information, the bill’s central purpose is to define how digital assets should be classified, where SEC authority ends, where CFTC authority begins, and how digital commodity exchanges, brokers, and dealers should be registered and supervised. That sounds boring. But in crypto, boring regulation can be extremely important. The market does not need every token to be blessed. It does not need every project to survive. It does not need politicians promising that crypto will “change everything.” What it needs is a map. If CLARITY becomes law, the US could finally move closer to a system where market participants know what they are allowed to do, what disclosures they need, which regulator they answer to, and what protections apply to users. That matters for several reasons. First, it reduces legal uncertainty for serious builders. Second, it gives institutions a cleaner path to participate. Third, it makes it harder for low-quality projects to hide behind ambiguity. And fourth, it puts crypto closer to becoming a regulated part of the financial system, not an outside experiment constantly fighting the same legal battles. But this is not law yet. The committee vote is a major step, not the finish line. The bill still needs to pass the full Senate. It also needs to be reconciled with the House version and with related Senate work on digital commodities. There are still political disputes around AML rules, stablecoin rewards, and whether government officials should face stronger limits around crypto-related conflicts of interest. That last point matters. A good crypto bill should not only protect innovation. It should also protect the legitimacy of the market. If the public sees digital asset legislation as a gift to insiders, exchanges, donors, or politically connected actors, the regulatory win becomes weaker. So the real question is not just “will CLARITY pass?” The real question is whether the final version creates durable rules that can survive market cycles, scandals, lobbying pressure, and future administrations. My view: this is structurally bullish for the industry, but not in the simple “price must go up tomorrow” way. It is bullish because serious regulation lowers the cost of participation for serious capital. It is bullish because the US appears closer to accepting that crypto will not disappear. It is bullish because the conversation is moving from “should this industry exist?” to “how should this industry be regulated?” That is a very different stage of maturity. Still, the market should be careful with overreaction. Regulatory clarity does not remove bad tokenomics. It does not fix weak products. It does not make every altcoin investable. It does not guarantee that liquidity immediately floods into the sector. But it can change the foundation. And in markets, foundations matter. The CLARITY Act is not the end of crypto’s regulatory fight in the US. But it may be one of the first real signs that the fight is entering a more institutional phase.