According to Woofun AI, macro investor Jordy Visser (Jordy Visser) recently made an anti-consensus judgment, believing that quantum computing is not the terminator of Bitcoin. The real wealth reshaping logic lies in AI's restructuring of productivity and the superflywheel effect in life sciences. Faced with market fears about quantum threats, Wither pointed out that the slow development of hardware makes Bitcoin safe, and that AI-native workflows and the rise of life science giants such as LLY.US (LLY.US) are defining the asset landscape for the next five years.
In response to venture capitalist Chamath Palihapitiya (Chamath Palihapitiya)'s astonishing statement that “quantum computing will return Bitcoin to zero,” Wieser made an in-depth technical and physical analysis. He believes that Parihapitia is good at creating media volume, and that his remarks are often aimed at maintaining topical topics rather than strict technical deductions. From the perspective of physical hardware, there are huge differences in the timeline for quantum computing to crack modern cryptographic systems. Strict scholars estimate that it will take 20 to 40 years, while optimists think it will take 5 to 7 years.
In contrast, what is currently more urgent is the cybersecurity crisis at the software level. For example, there are frequent vulnerabilities in systems built using PHP, and Cloudflare (NET.US) has discussed this in depth; “Axios”, an exclusive report written by senior AI journalist Madison Mills (Madison Mills), indicates that these uncensored AI agents performed unauthorized operations within the account after the customer handed over account rights, but this is not a cyber attack. Wieser has used Bitcoin as the core net worth since 2020, based on a thorough understanding of the underlying technology.
He emphasized that Adam Back (Adam Back), the inventor of Hashcash quoted in the Bitcoin white paper and has a doctorate degree in computer science, believes that quantum threats are constrained by hardware development, and the entire industry has a buffer period of several years to migrate to anti-quantum cryptography (Post-Quantum Cryptography). Traditional financial systems such as JPMorgan Chase (JPM.US) are also moving closer to digital asset security regulation. The Bitcoin community is fully capable of dealing with potential risks through soft forks or network upgrades, so quantum mechanics is essentially a hardware delay problem rather than a destructive end.
Market evidence further confirms Bitcoin's safe-haven properties. Despite sharp fluctuations in macro-liquidity, the price of crude oil surged to $115, and the sell-off in technology stocks, and the price of Bitcoin consolidated strongly at a high level of $80,000 to $85,000.
This performance shows that the core capital of the market does not regard quantum threats as an immediate fatal risk. This high level consolidation is not only a financial haven in the context of hedge fund leverage crowding, but also a market vote on Bitcoin's underlying security logic. In an environment where multiple weaknesses are superimposed, Bitcoin's persistence reveals its resilience as a non-sovereign currency, in stark contrast to the high volatility of traditional financial assets, and highlights its ability to independently price in extreme market environments.
Generational differences in perception constitute another important dimension of crypto asset acceptance. Marc Andreessen (Marc Andreessen) pointed out on a16z's podcast that young people aged 13 to 21 see cryptocurrencies and AI as a natural part of their lives; those aged 25 to 35 focus on using technology to make money; while people aged 35 or even 40 and over often instinctively see them as “evil forces” that disrupt the old order. Wieser used his son's experience as an example. At age 13, he reached $700 to $80,000 in the crypto bull market through information mining on Reddit and Twitter. Although it later retracted, this shows that the younger generation naturally believes in decentralized networks and scoffs at the S&P 500 index. Parihapitia is in a class that earns enough wealth in a traditional framework. Bitcoin does not cater to its inherent system, but this does not affect the advancement of the technical wheel. As Michael Saylor (Michael Saylor) said: It's not you looking for Bitcoin, it's Bitcoin coming to you.
This perception gap reveals the fundamental differences between traditional elites and the new generation of digital natives on the concept of value storage. The younger generation is more likely to trust codes than the promises of traditional financial institutions.
However, the real serious problem in the real world is the deleveraging storm and contagion effects that hedge funds are experiencing. Interest spread arbitrage transactions between French treasury bonds and German treasury bonds were severely liquidated. Many large multi-strategy hedge funds had previously gone long on French treasury bonds and shorted German treasury bonds, earning interest spreads of about 125 basis points, and compounded leverage of up to 20 times.
This strategy relies on the country not defaulting on contracts and stable interest spreads, but once the sovereign credit market fluctuates, extremely high leverage quickly amplifies losses, causing diversified allocations to fail. Goldman Sachs (GS.US)'s recent internal report shows that since the COVID-19 pandemic in 2020, mainstream, short, and diversified strategy funds rarely fell by more than 80 basis points in a single day, but the overall collapse occurred again this week, with a decline of more than 1%. This is reminiscent of the eve of the collapse of long-term capital management companies (LTCM) in 1998 and the “quantified earthquake” (Quant Meltdown) that swept Wall Street in August 2007. When assets that are supposed to be low in correlation fall at the same time, it means that the market is running out of liquidity. Some semiconductor and chip stocks plummeted 5% to 10% in a single day, precisely because giant funds facing additional security deposits sold the most liquid high-quality assets at no cost, and those leading artificial intelligence stocks, which had risen by more than 100% this year, became the first choice. This is not a fundamental reversal of the AI industry; rather, it is simply a violation of financial institutions' leverage.
Data compiled by Woofun AI shows that the AI workflow revolution is shifting from passive tools to active decision-making, reshaping the business case. Wieser shared his own experience: he entered the address and background information of a commercial property that had not been sold for a long time into Grok and asked them to develop due diligence and marketing plans within 45 days. Grok not only completes competitive analysis, but also automatically captures local land transaction records to generate a list of buyers who have recently invested heavily in land, and suggests immediate and accurate outbound sales for specific buyers. In the past, this required weeks of professional consulting teams, but now AI has completely surpassed human experience in business planning, asset valuation, and strategic reasoning. Another case comes from a top New York law school where professors banned students from using AI tools such as Harvey and returned to a paper-only teaching model due to “uncontrollability.” This is in stark contrast to the real world, where law firms and corporate employers value the ability to quickly use AI to solve complex problems. As a junior in college, Wieser's son was able to use Grok and intelligent workflows to build automation solutions within minutes between meetings, and the company even wanted him to stay full time.
This efficiency gap indicates that the gap between native AI talents and those nurtured by traditional education systems is widening at an accelerated pace.
Education gaps and the rise of AI-native companies have further exacerbated the rigidity of traditional systems. Marc Andreessen (Marc Andreessen) emphasized that the gap between AI native companies (AI-native companies) and traditional existing companies is growing at an irreversible rate. Future core talents must have the entrepreneurial engineering ability to quickly transform business problems into intelligent workflows. Currently, most people still use AI in “active mode,” where humans type in instructions and wait for responses.
But what really has disruptive power is the “passive automation model”: meetings are automatically recorded and structured into minutes, action items are automatically distributed and tracked, the underlying data of the business system flows seamlessly, and the number of intelligent agents deployed far exceeds that of regular employees. When these systems completely take over processes in finance, supply chain, and operations, the enterprise's productivity flywheel will spin at an exponential rate. Traditional educational institutions, such as law schools that prohibit electronic devices, are “unable to supervise” the reason that management is ignorant of technological evolution. This rigid model will cause graduates to be uncompetitive in the job market, while AI native companies have achieved extreme optimization of labor costs and a leap forward in decision-making efficiency through deep integration of intelligent agents.
Compound interest thinking and the construction of personal external brains are deep expressions of AI applications at the cognitive level. Looking back on the theory of probability taught by his father when he was young, and a math problem: a penny doubles every day and becomes $10.7 million after 30 days, which made him understand the power of exponential growth in geometry. Moore's Law is essentially exponential compound interest, and AI is currently iterating faster than any underlying technology. To keep up with the age of compound interest, everyone should incorporate AI into mental construction and see it as a life coach or mental reflection mirror.
Use a tool with a long-term memory architecture, such as Muse or Grok, to conduct a weekly deep review to sort out work progress, reflect on mood and health, and plan goals. The human mind jumps, but by digitizing, structuring, and backing up cognition into Notion or Obsidian, we build our own plug-in superbrain. In the future, people who can deeply coexist with personal thinking and intelligent agents will surpass others by several orders of magnitude in productivity. In the field of physical cutting-edge technology, innovation is also booming.
Warp Sciences, co-founded by Kevin Weil (Kevin Weil), the former chief product officer of OpenAI, is a chemical CRO that uses AI and robots to complete molecular synthesis and testing for pharmaceutical companies; Anduril (Anduril) founder Palmer Luckey (Palmer Luckey) is collaborating with biotechnology teams to combine genetic engineering with advanced sensors to create tactical dogs that serve defense. The valuation of these cutting-edge technology startups is rising at a much faster rate than ever before. The world's innovation engine is rapidly rising along the J-curve to the upper right corner, and the competitiveness of individuals and businesses will depend on their ability to use AI to compound cognition and decision making.
Investments are ultimately directed towards life sciences rather than the semiconductor cycle, and Eli Lilly's superflywheel effect is particularly significant. While technology stocks plummeted, the S&P 1500 life sciences, biotech and pharmaceuticals index bucked the trend and hit a 52-week high, and Eli Lilly set a new record high. Wither asserts that in the next five years, it is likely to surpass Nvidia (NVDA.US) and become the largest company in the US by market capitalization. This is based on the underlying economics of the industry: energy accounts for only a single digit of personal daily consumption expenditure, while healthcare spending already accounts for close to 19% of US GDP. This is a pure non-cyclical expenditure, covered by government benefits and commercial insurance.
Chip companies such as Nvidia and Micron (MU.US) will inevitably have a cyclical semiconductor fate. If power grid construction is blocked and data center expansion slows down, millions of GPUs may experience a digestion period, and shipments will experience a slump. However, pharmaceutical companies have no risk of hardware backlogs, and life cycle cash flow is extremely stable after biomolecules and innovative therapies are approved. More importantly, AI has exploded in the biomedical field. Lilly built a huge supercomputing platform called Tune Lab and collaborated with more than 135 biotech companies to gather molecular structures and exclusive data. Like the “Manhattan Project” back then, it used cutting-edge algorithms to reshape target discovery, molecular screening, and clinical trials.
This superflywheel, which introduces AI into drug discovery, transforms traditional pharmaceutical companies into supergrowth bodies with both high certainty and high growth. Eli Lilly's stock price has been rising unilaterally for 10 consecutive years, with a compound annualized growth rate (CAGR) of more than 30% over the past decade. Even during the Nasdaq crash in 2022, few traditional technology companies can maintain such a long-term non-cyclical bull market.
The ultimate allocation strategy should cover Bitcoin defense, AI offense, and life science balance. On the chassis, Bitcoin is used to withstand the continuous dilution of the fiat currency system, and its decentralized nature provides a secure anchor against macro-uncertainty; on the offensive side, it fully embraces AI native tools to restructure productivity and lays out core technology fields to achieve exponential efficiency improvements for individuals and enterprises; on the defensive side, it places heavy capital on non-cyclical life science leaders with AI support, such as Eli Lilly, to enjoy the long-term stable returns brought by rigid demand and technological compound interest. This complete cognitive framework combines currency hedging, technological revolution, and industrial evolution, and is the best solution for overcoming the current financial turmoil and technological reshuffle. As quantum threats are falsified, hedge fund leverage risks are cleared, and the native AI ecosystem matures, investors need to abandon the short-sighted bubble theory and focus instead on asset classes that can cross cycles and use technological leverage to build deep moats.