AIPreço de Sleepless AI

The narrative of AI investment is shifting from a technology story to a capital expenditure story, and now to a financing story. This marks a "critical moment for capital," as noted by Bridgewater's Greg Jensen. Morgan Stanley forecasts that the total cost of AI infrastructure through 2028 will reach $3.2 trillion, with approximately $1.75 trillion expected to be raised from credit markets. However, free cash flow projections for major hyperscale cloud providers are declining sharply, with Oracle's 2027 forecast nearing -$40 billion. The bond market is already pricing in the rising risks. Apollo data shows AI-related bonds constitute 40% of long-duration supply. Credit spreads for hyperscalers have widened significantly this year—around 25 basis points for high-quality names—while the overall investment-grade market remained flat. Oracle's credit default swaps (CDS) have surged dramatically, indicating particular market concern. Renowned bond investor Jeff Gundlach sharply criticized financing plans using AI assets like GPUs as collateral, comparing them to "issuing 30-year ABS backed by bananas" due to the assets' rapid obsolescence. Although current balance sheets for hyperscalers appear strong, the future funding gap is widening. Morgan Stanley has nearly doubled its 2027 cloud capital expenditure growth forecast to 29%, amplifying the financing challenge. Purchasing commitments have exploded to $982 billion, with significant capital expenditures now existing off-balance-sheet, meaning true funding pressure is being underestimated.

Dario Amodei, co-founder and CEO of Anthropic, predicts that artificial intelligence could enable humanity to cure or prevent most diseases within 5 to 10 years. He shared this view on social media, acknowledging it may seem overly optimistic but considers it realistic if the rapid development of powerful AI systems continues. Amodei clarifies this does not mean a single universal cure, but rather a scenario where AI dramatically accelerates biological research and therapy development. He elaborated on this concept in his essay "Machines of Loving Grace," estimating that sufficiently powerful AI could accelerate progress in biology and medicine by roughly tenfold, compressing a 50-100 year timeline into 5-10 years. Potential outcomes include significantly reducing cancer mortality, preventing or treating most genetic diseases, advancing the fight against Alzheimer's, and improving therapies for cardiovascular, autoimmune diseases, diabetes, and obesity. However, Amodei notes that real-world progress will still be constrained by the need for lab research, clinical trials, and regulatory approvals—steps AI cannot bypass. Anthropic is reportedly expanding its projects in biology and medicine, expecting initial signs of results soon and more substantial achievements in the coming years. The company has launched its "AI for Science" program, providing researchers access to its models for biology, genetics, and drug discovery projects. The statement is also viewed within a broader corporate context. Following a funding round valuing Anthropic at over $960 billion and projecting substantial revenue growth, investors are likely expecting tangible results beyond chatbots and code generation. Breakthroughs in biology and medicine represent a key argument to justify such a high market valuation. The open question remains whether AI-driven acceleration will lead to actual treatments or primarily enhanced data analysis tools for researchers.

In a recent interview, Sam Altman gave a rare and high praise, calling Alec Radford "perhaps the most important, yet least known, researcher in AI history." Widely regarded as the true father of GPT, Radford is the lead author of foundational papers including GPT-1, GPT-2, CLIP, and Whisper, and contributed significantly to GPT-3, DALL·E, Scaling Laws, and GPT-4. Despite this monumental impact, Radford remains highly private, holds no PhD, and rarely gives interviews. Altman credits OpenAI's rise to hiring the then-23-year-old in 2016. Radford's early experiments, like training a model on Amazon reviews, led to the discovery of the "unsupervised sentiment neuron," revealing that models can learn unintended capabilities from simple next-token prediction. His pivotal move was applying the Transformer architecture to language modeling, creating GPT-1 in 2018. This established the "scaling" direction—focusing on increasing model size, data, and compute—which became OpenAI's core strategy, leading to GPT-2, GPT-3, and beyond. Radford later applied the same principles beyond text. His early work on DCGAN laid groundwork for image generation. At OpenAI, he contributed to Image GPT, DALL·E, and CLIP, demonstrating that a simple, scalable training task (like matching images to text) could yield powerful, general capabilities. His work on Whisper applied this to robust speech recognition. Described by colleagues as a "once-in-a-generation genius" and exceptionally kind, Radford is known for his low profile. In late 2024, he left OpenAI for independent research. His latest project, "Talkie," is a 13-billion parameter language model trained *only* on texts published before 1931. This experiment tests if a model with no modern knowledge can quickly learn new skills (like basic Python) from few examples, probing the boundary between memorization and true learning. True to form, as the world catches up, Radford is likely already working on the next big question.

The optimal investment strategy in the current AI bubble environment is a dual "leg" approach: going long on both "hubris" (AI tech leaders) and "humiliation" (neglected, underperforming cyclical assets). This aims to capture gains from both sides during the final surge of a nominal GDP-driven bubble, according to a Bank of America report by strategist Michael Hartnett. The bank's Bull & Bear Indicator remains in extreme bullish territory, signaling "sell", yet history shows such signals have limited immediate impact. Current fund flows show structural shifts: gold saw its largest weekly inflow since January, commodities are up 58.9% YTD, while tech stocks experienced their largest weekly outflow in seven weeks. The core thesis is that the final stage of a bubble benefits both the leading theme ("hubris" - AI) and oversold sectors ("humiliation" - like consumer stocks), similar to patterns seen in the 1999 tech bubble and 2007-2008 credit crisis. The report advises shorting "AI bonds," anticipating pressure from massive capital expenditures. Key risks include high concentration, surging bond yields, and cautious voter sentiment. The US debt burden is highlighted, with servicing costs reaching $1.4 trillion. The 10-year Treasury yield breaching 5% is seen as a red line for policymakers. For the "avoid the dollar" theme, BofA recommends gold and Hong Kong property stocks, the latter seen as deeply undervalued. The November US midterm elections, particularly the Texas governor race concerning AI data center expansion, are flagged as a critical political variable that could determine the AI bull market's trajectory. Private client data shows record-high equity allocations (66.4%) and record-low cash levels (9.4%), indicating bullish positioning. The report concludes that while overbought conditions can pause the bull market, ending it requires a combination of excessive positioning, overly optimistic earnings, and policy tightening—a scenario not yet in place.

According to Glassnode, consumer confidence remains at one of its lowest levels in a decade, despite two consecutive months of improvement. This has not stopped households from moving money out of cash, as they expect further cost-of-living increases and a broader economic slowdown. The key question is where this capital is flowing. US stocks hit a new all-time high in early August, primarily driven by trading in AI-related stocks rather than a broad market rally. Bitcoin, historically seen as a hedge against declining trust in traditional finance, has not participated in this movement. Spot Bitcoin ETFs saw outflows of $389.7 million in one week, coinciding with rising equity markets—a divergence that aligns with Glassnode's data on capital flows. Bitcoin is currently trading at roughly half its October 2025 peak, stuck in a narrow range. Meanwhile, AI-related trading continues to attract fresh capital from retail traders, hedge funds, and even crypto-native institutional investors, who are redirecting funds into AI stocks and tokens. The macroeconomic backdrop has not been hostile to Bitcoin, with core inflation at a moderate 2.5% in July. However, Bitcoin's muted response to favorable inflation data is seen as a concerning signal, given its supposed role as a hedge against currency debasement. Spot Bitcoin exchange trading volume has fallen to its lowest since 2019, and recent ETF inflows are only a "fraction of any prior accumulation wave," suggesting institutional buying may have paused. This trend extends beyond trading: some Bitcoin miners are repurposing their power contracts and data center capacity for AI workloads. This appears to be a structural shift that could pressure Bitcoin's status as the default destination for capital leaving cash. The fundamental arguments for Bitcoin as a hedge against inflation or scarcity are not invalidated, but their expected impact has not materialized within the timeline anticipated by crypto optimists this summer.