MSFTMicrosoftNeutralMicrosoftAMDBullishMETANeutralNVDANVIDIANeutralNVIDIAORCLOracleNeutralOracle

1w ago on

AMD $AMD JUST REPORTED 97% DATA CENTER GROWTH. AND MANAGEMENT EXPECTS TOTAL REVENUE TO GROW MORE THAN 80% IN 2027. The AI infrastructure boom is getting even bigger, and NVIDIA is no longer the only company capturing the opportunity. Look at AMD's Q3'26 numbers. Revenue: $13.4B, +45% YoY Adjusted EPS: $2.09, +74% YoY Data Center: $8.55B, +97% YoY Operating profit: $3.92B, +75% YoY Gross margin: 56% And the acceleration is only beginning. AMD expects Q4 revenue of approximately $17 BILLION, implying another 27% sequential increase. Even more impressive, management expects consolidated revenue growth to EXCEED 80% IN 2027. Think about that. A semiconductor company already generating over $50 billion in annualized quarterly revenue is guiding for growth that many startups would struggle to achieve. The catalyst? AI GPUs, EPYC server CPUs and AMD's new Helios rack-scale computing platform. Anthropic, Meta, Microsoft, OpenAI and Oracle are already testing or deploying workloads on Helios systems. Hyperscalers want more computing capacity, more suppliers and greater negotiating power. THE AI BOOM IS BIG ENOUGH FOR MORE THAN ONE WINNER. But there is one number I don't like. Free cash flow collapsed 64% YoY to just $554 MILLION, despite almost $4 billion in adjusted operating profit. AMD is accumulating enormous inventories ahead of its AI infrastructure ramp, while customer warrants introduce potential shareholder dilution. Revenue growth is impressive. Turning it into sustainable cash flow is the real test. Now look at the valuation. 2026F P/E: 62.9x 2027F P/E: 29.0x 2028F P/E: 20.0x That's an extraordinary compression in valuation multiples, reflecting expectations of explosive earnings growth. If management delivers, 20x FY28 earnings could prove very attractive for a company becoming a major supplier of AI computing infrastructure. Of course, NVIDIA still has a formidable advantage in software, networking and platform integration, while AMD must prove that Helios can scale profitably. But I remain convinced that AI infrastructure demand is structural, and the opportunity is much larger than any single chipmaker. AMD DOESN'T NEED TO DEFEAT NVIDIA TO BECOME AN ENORMOUS WINNER IN AI. It only needs to capture a meaningful share of a computing market that continues expanding at an extraordinary pace. LONG AI.

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1w ago on

US Midterms 2026: Democratic Control of Congress, Political Gridlock, and the Implications for Global Markets The Democrats’ projected victory in both the House and Senate represents a significant shift in the American political landscape, particularly because the party managed to capture Senate seats in traditionally Republican states such as Texas, Kansas and South Carolina, suggesting that voter dissatisfaction with inflation, living costs and the administration’s handling of the economy has extended well beyond the usual competitive districts. However, I think the most important implication for financial markets is not that Democrats have gained control of Congress, but that the United States is entering a period of divided government in which legislative gridlock, fiscal negotiations and regulatory scrutiny will become increasingly important, potentially constraining President Trump’s ability to advance new legislation while leaving much of his executive authority over tariffs, foreign policy and existing regulations intact. The new congressional balance takes effect in January 2027, and while the Democrats will gain substantial oversight and legislative powers, they will not automatically gain the ability to reverse existing policies because Trump retains his presidential veto and most Senate legislation remains subject to the 60-vote threshold. From a macroeconomic perspective, the most consequential development could be the return of fiscal policy as a major source of market uncertainty, particularly when the United States is already struggling with substantial budget deficits, elevated interest expenses and an increasingly difficult Treasury financing environment. Democratic control could constrain additional deficit-financed tax reductions or discretionary spending initiatives, which might eventually help moderate Treasury issuance relative to a more expansionary policy baseline, but I would be reluctant to assume that political gridlock automatically translates into fiscal discipline because neither party has demonstrated a particularly strong willingness to address the structural imbalance between government expenditure and revenue. More importantly, negotiations over government funding and the debt ceiling could become considerably more contentious in 2027, introducing risks of shutdowns, delayed fiscal agreements and additional volatility in Treasury markets. For investors, the distinction between reducing future deficits and merely preventing further fiscal expansion is enormous, particularly when the existing debt stock and associated interest burden remain substantial regardless of which party controls Congress. This brings us to the bond market, which I believe will remain a more important determinant of equity valuations than the election result itself, because even if divided government reduces expectations for additional fiscal stimulus, that does not automatically mean long-term Treasury yields will decline. We need to distinguish between weaker growth expectations, which could pull yields lower, and a higher fiscal or political risk premium, which could push longer-dated borrowing costs higher even when growth is moderating. A prolonged confrontation over the debt ceiling could also create substantial volatility in short-dated Treasury bills and government liquidity without necessarily producing the same reaction across the entire yield curve. The real question is whether the new political configuration improves confidence in America’s long-term fiscal trajectory or simply introduces another layer of uncertainty into an already fragile bond market, and I think that distinction matters enormously when equity valuations remain sensitive to the discount rate applied to future earnings. The implications for technology and AI are particularly interesting because a Democratic Congress would have substantially greater authority to investigate large technology companies, examine AI safety practices and scrutinize the environmental and infrastructure costs associated with the enormous data-center expansion taking place across the United States. This could introduce additional uncertainty for hyperscalers such as Microsoft, Amazon, Alphabet and Meta, particularly if congressional investigations increase pressure for stricter model evaluations, greater transparency, data-center permitting requirements or additional scrutiny of electricity and water consumption. The change in state governments also matters, particularly in Texas and Georgia, where data-center investment has become increasingly important and state-level permitting, electricity costs and infrastructure policy can influence project economics even without major new federal legislation. However, I would strongly distinguish regulatory friction from a deterioration in underlying AI demand, because the commercial incentives for enterprises to adopt increasingly capable AI systems remain intact, while additional permitting constraints could actually increase the value of existing power connections, operational data centers and secured infrastructure capacity. I remain structurally constructive on AI, although the election introduces another variable investors should incorporate into the timing and cost of future infrastructure deployments. Trade policy is another area where I would avoid overly optimistic conclusions because Democratic control of Congress does not automatically mean that Trump’s tariffs will disappear or that relations with China will normalize, particularly when considerable trade authority remains delegated to the executive branch and concerns surrounding advanced semiconductors, national security and technological competition have become increasingly bipartisan. Congress could increase scrutiny of trade measures, challenge particular policies or negotiate restrictions on future executive authority, but meaningful legislative changes would still face presidential opposition and existing legal constraints. Consequently, I would not interpret the election as an immediate catalyst for a substantial reversal in semiconductor export restrictions, a broad normalization of US-China trade or a reduction in geopolitical risk. Similar uncertainty applies to defense spending, healthcare, banking and cryptocurrencies, where congressional investigations and legislative bargaining could change the policy environment, although major statutory changes would require broader political agreement. For equities, I think the immediate reaction could be complicated because political gridlock can reduce the probability of major new legislation while simultaneously increasing uncertainty around budgets, regulation and executive policymaking, meaning that different sectors could experience very different outcomes. Highly regulated industries such as banking, pharmaceuticals and cryptocurrency-related businesses may face greater congressional scrutiny, while certain renewable-energy and healthcare initiatives could receive stronger legislative attention, although the ability to translate those priorities into law remains limited by the presidential veto. Technology companies may face additional headline volatility, particularly given the ongoing reassessment of AI capital expenditure and elevated valuations, but I would caution against assuming that a Democratic Congress necessarily undermines the earnings trajectories of companies benefiting from structural growth in computing, cloud infrastructure, semiconductors and automation. Ultimately, markets will continue differentiating businesses according to revenue growth, operating margins, cash generation, returns on invested capital and valuations, rather than permanently repricing entire industries based on which party controls Congress. My broader conclusion is that the Democratic sweep represents a meaningful political constraint on the Trump administration but does not fundamentally change the structural economic forces driving US markets, particularly the expansion of AI infrastructure, the extraordinary capital requirements associated with that investment cycle, persistent fiscal imbalances and the increasing sensitivity of equity valuations to long-term interest rates. The next two years could involve more congressional investigations, slower legislative progress, contentious fiscal negotiations and greater uncertainty surrounding sector-specific regulation, but those developments should be evaluated according to their actual impact on corporate earnings, capital expenditure and financial conditions rather than through a simple partisan framework. For investors, I would pay considerably more attention to the direction of Treasury yields, the sustainability of corporate earnings and the economics of AI infrastructure than to the political headlines themselves. Washington can influence market sentiment, but over the longer term, earnings, cash flow and the cost of capital will remain the variables that ultimately determine investment returns.

MSFTMicrosoftBullishMicrosoft

1w ago on

MICROSOFT $MSFT JUST DELIVERED A REMARKABLE QUARTER. THE AI BOOM IS NOT SLOWING DOWN. IT IS RUNNING OUT OF COMPUTE. Look at these numbers from Q1 FY27. Revenue: $92.3B, +19% YoY Operating profit: $45.9B, +21% YoY Adjusted EPS: $4.89, +18% YoY Azure: $33.2B, +48% YoY Microsoft Cloud: $63.9B, +30% YoY But here's what really caught my attention. AZURE IS NOW A $133 BILLION ANNUALIZED REVENUE BUSINESS. AND IT IS STILL GROWING 48%. Absolutely extraordinary for a business of this scale. And it gets better. Microsoft expects Azure growth to ACCELERATE to 49–50% next quarter, comfortably above the 44.6% consensus. Why? AI DEMAND CONTINUES TO EXCEED AVAILABLE COMPUTING CAPACITY. Even after spending $39.4B on cash capex this quarter, Microsoft still cannot bring enough infrastructure online to satisfy customers. Meanwhile, operating margins remain close to 50%, and the company generated approximately $18.6B in positive free cash flow. This is what separates Microsoft from many speculative AI businesses. It is already converting enormous AI demand into revenue, operating profits and cash. Of course, there are risks. Commercial bookings declined 12% YoY, RPO missed expectations at $677B, and infrastructure spending will remain enormous, with depreciation and cloud margin pressure becoming increasingly important. But the broader investment thesis remains intact. Microsoft is not betting everything on a single AI model. It owns the cloud, enterprise distribution, productivity software, developer ecosystem, security infrastructure and increasingly the agentic operating environment. EVERY ENTERPRISE WANTS AI. MICROSOFT WANTS TO PROVIDE THE INFRASTRUCTURE, SOFTWARE AND AGENTS THROUGH WHICH THEY USE IT. At 26.8x FY27 earnings and 22.3x FY28 earnings, I think the valuation remains reasonable for the quality and growth of this franchise, although continued earnings delivery is essential. We own Microsoft, and I am extremely happy with the operating performance. The AI revolution is becoming a real revenue and earnings story, not merely a capital expenditure story. And Microsoft is demonstrating that better than almost anyone. LONG $MSFT. LONG AI.

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2w ago on

Anthropic: Cheaper Tokens, Exploding Demand, and the $100 Billion ARR Question I am shocked, absolutely shocked, that Anthropic’s token consumption has gone vertical after the company substantially reduced inference prices, except that I am not really shocked at all, because this is precisely the dynamic I have been discussing for months, namely that the AI industry is increasingly demonstrating how falling unit prices do not necessarily translate into falling industry revenues or weaker demand for computing infrastructure, particularly when cheaper intelligence allows companies to deploy AI across a much broader range of workflows, and the latest OpenRouter data provide another compelling example of how making intelligence more affordable can dramatically accelerate the amount of it that customers are willing to consume. The numbers are extraordinary, with Anthropic’s daily token consumption on OpenRouter increasing approximately 3.6x since the launch of Claude Opus 5.5 on September 22, as the seven-day moving average rose from roughly 600 billion tokens per day to approximately 2.2 trillion by October 24, while individual daily observations approached 3 trillion tokens, representing a remarkable acceleration after months in which consumption had remained comparatively stable. What makes the chart particularly interesting is not only the magnitude of the increase but also the timing, because the acceleration began after the introduction of Opus 5.5, continued after Sonnet 5.5 arrived on September 28 and strengthened further following the October 7 launch of Haiku 5.5, suggesting that the combination of improved model capabilities, lower inference costs and a broader range of economically viable workloads is stimulating substantial incremental demand, rather than merely causing customers to substitute one Anthropic model for another. Anthropic has aggressively improved the economics of its model lineup, with Haiku 5.5 costing approximately 75% less to run than its predecessor on average, while Sonnet 5.5 offers materially better price-performance than Sonnet 5, and additional reductions in caching costs have made agentic workloads considerably cheaper, which matters because agents can generate far more inference activity than conventional chatbot interactions as they repeatedly reason, retrieve information, call tools, revise outputs and continue working through multi-step tasks. And the results are increasingly visible in consumption, because the lower cost of each individual interaction appears to be encouraging developers to use models more frequently, to assign them more complex tasks and to embed them more deeply into production workflows that previously would have been considered too expensive or too unreliable to automate. The second chart reinforces the same point from the pricing side, with Anthropic’s blended token price falling toward approximately US$0.80 per million tokens, compared with levels closer to US$1.50 only a month earlier, while OpenAI’s corresponding price index has also declined substantially, illustrating that the effective price of machine-generated intelligence is falling rapidly even as the amount of intelligence being consumed appears to be accelerating. Updated October 25 data put Anthropic’s blended token price around US$0.85 per million tokens, down approximately 43.5% over the preceding month, illustrating how quickly the effective cost of intelligence is declining as model providers cut prices, improve efficiency and customers shift between models, although the precise revenue implications still depend on the mix of models being used, the distribution channel through which customers access them and the extent to which lower prices stimulate additional workloads. This creates what I think is one of the most important economic questions in the entire AI investment cycle: What happens when the price of intelligence falls dramatically, but the quantity of intelligence consumed increases even faster? In traditional commodity markets, a significant increase in supply tends to pressure prices and eventually discourage additional investment, but intelligence is different because cheaper inference can expand the range of economically viable applications, allowing businesses to automate tasks that previously made little financial sense, while increasingly autonomous agents can consume substantial amounts of additional compute without requiring a proportional increase in human users, meaning that the market can grow through greater intensity of usage even before it grows through a comparable increase in the number of customers. Consider a company that previously spent US$100,000 per month on AI inference, perhaps using models only for a narrow set of customer-service, coding or analytical tasks because the cost of deploying them more broadly was difficult to justify. If the effective price of inference falls by 50%, the company could maintain exactly the same workload and reduce spending to US$50,000, which would obviously be negative for the model provider’s revenue if nothing else changed, because the provider would be delivering the same amount of intelligence for half the price. But that is only the static calculation, and static calculations are often the least useful way to think about technologies whose costs are falling rapidly and whose applications are still expanding. If the lower price allows the company to expand AI deployment across customer service, software development, financial analysis, procurement, logistics and internal operations, while also allowing employees to use more capable models for tasks that were previously handled manually, total consumption could increase fivefold or tenfold, meaning that the customer might ultimately spend substantially more despite paying much less for each individual unit of intelligence. That is the economic mechanism I find so compelling, because the relevant question is not simply whether the price of a token is falling, but whether the decline in price is unlocking enough additional usage to expand the total pool of revenue, compute demand and economic activity associated with AI. Lower prices expand the addressable market, which increases consumption, which creates demand for additional compute, which encourages further infrastructure investment. This resembles the Jevons paradox, where improvements in the efficiency or affordability of a resource can increase its aggregate consumption rather than reduce it, although the analogy should not be treated as a mechanical economic law because the outcome still depends on demand elasticity, application development, customer budgets, reliability, latency, integration costs and the many expenses associated with deploying AI beyond the inference itself. And the Anthropic data are particularly interesting because the increase in consumption does not appear to be driven exclusively by the cheapest model, which would make the story more ambiguous because a surge in low-cost tokens could reflect substitution without necessarily representing a comparable increase in revenue or economic value. A substantial portion of the incremental volume comes from Claude Opus 5.5, Anthropic’s premium model, which suggests that developers are not merely switching to the lowest-cost intelligence available but are also expanding their use of more capable models when the resulting productivity gains justify the cost, particularly in coding, reasoning and agentic workflows where a more capable model can complete a task with fewer errors, less supervision and fewer costly iterations. That distinction matters enormously, because if all the growth were coming from extremely cheap models replacing expensive models, token consumption could rise dramatically without necessarily producing equivalent revenue growth, and the apparent expansion in usage could conceal a deterioration in the provider’s realized price per unit of output. But if premium models are simultaneously gaining substantial usage, that suggests customers are increasingly willing to pay for more sophisticated reasoning, coding and agentic capabilities where better performance creates meaningful economic value, and it also suggests that the market is not simply becoming a race toward the lowest possible price but is instead becoming segmented according to the value that different levels of intelligence can create. And this brings us to Anthropic’s revenue, where the relationship is straightforward in principle but much more complicated in practice: Revenue growth depends on consumption growth multiplied by realized revenue per token. If token volume rises 3.6x while the effective price per token falls approximately 43%, the simplified revenue implication would still be roughly 2x the original revenue, assuming the same customer mix, billing structure and comparable measurement periods, because a 3.6-fold increase in volume multiplied by approximately 57% of the original price still produces a result slightly above twice the original revenue. That is an extraordinary result, because it means a company can nearly halve its effective pricing and still potentially double revenue if consumption responds strongly enough, which is precisely why falling prices should not automatically be interpreted as evidence that the market is becoming less attractive. However, this calculation is illustrative rather than an estimate of Anthropic’s actual revenue growth, because OpenRouter represents only one distribution channel, its model mix may differ materially from Anthropic’s broader customer base, the timing of usage and billing may not align perfectly, enterprise contracts may have different economics and the Ornn pricing index measures a different population of transactions. So I would not conclude that Anthropic’s October revenue has already doubled simply by multiplying the two charts, because the charts demonstrate a powerful demand and pricing dynamic but do not provide a complete view of the company’s consolidated revenue, gross margins or customer mix. But the direction is undeniably encouraging, particularly because the data suggest that Anthropic is benefiting from both sides of the equation at once, with lower prices encouraging more usage while improved model capabilities make customers more willing to deploy the technology in economically important workflows. And this is where the speculation around US$100 billion-plus in year-end annualized revenue becomes particularly interesting, because the question is no longer whether Anthropic has found product-market fit, but whether the company can convert its rapidly expanding usage into a revenue run rate that would place it among the fastest-growing large technology businesses in history. Anthropic reported US$16.3 billion of revenue during the first half of 2026, while reported third-quarter revenue of approximately US$16.8 billion would imply an annualized quarterly revenue run rate of roughly US$67 billion, assuming that figure is confirmed in the company’s subsequent financial disclosures and represents a comparable measure of revenue rather than bookings, contracted revenue or another operating metric. To reach a US$100 billion annualized revenue run rate by December, Anthropic would need to exit the year generating approximately US$8.3 billion per month, compared with the roughly US$5.6 billion monthly average implied by the reported third-quarter figure, which means that the company would need to increase its monthly revenue run rate by a substantial amount in a very short period. That requires an approximately 49% increase over the third-quarter average monthly revenue level, a demanding target for any business and an extraordinarily demanding target for a company already operating at this scale, because maintaining rapid percentage growth becomes increasingly difficult as the revenue base expands. But is it impossible? I certainly would not dismiss it, because the underlying usage data suggest that Anthropic may be operating in a market where lower prices, better models and expanding agentic workloads can reinforce one another, creating a growth curve that looks less like a conventional software ramp and more like the early stages of a new computing platform. If consumption across Anthropic’s broader ecosystem is accelerating anything like what OpenRouter is showing, while enterprise adoption continues expanding and customers increasingly deploy agents into production environments, then the possibility of a US$100 billion annualized exit rate becomes more credible than it would have appeared only a few months ago, particularly if direct enterprise contracts and cloud distribution channels are growing faster than the public API data alone would imply. Nevertheless, possible is not the same as proven, and the critical missing evidence is whether the acceleration in tokens is translating into a comparable increase in realized revenue across Anthropic’s direct API, enterprise subscriptions, AWS, Google Cloud, Azure and other distribution channels, because usage can grow rapidly while revenue conversion varies depending on discounts, committed-use agreements, reseller economics and the mix between premium and lower-cost models. That is the number I would want to see in the next S-1 amendment, along with clearer disclosure on revenue by channel, gross margins by model family, committed compute obligations, customer concentration and the amount of revenue that comes from recurring enterprise contracts rather than usage that may fluctuate with pricing or experimentation. The other side of this development is arguably even more important for the broader AI infrastructure trade, because the same data that suggest falling prices are stimulating demand also suggest that the physical resources required to produce intelligence remain scarce. GPU rental prices are strengthening even as token prices are falling. The latest Ornn market data show H100 rental pricing around US$3.17 per GPU-hour, with its index approximately 25% higher over the preceding month, while H200 and B200 rental benchmarks have increased by roughly 30% and 21%, respectively, creating a striking divergence between the falling price of AI output and the rising price of the computing capacity required to generate it. This is an extraordinary divergence, because customers are paying less for the intelligence produced by AI systems while the market is simultaneously assigning greater value to the computing resources required to produce that intelligence, which suggests that demand for inference and training capacity is expanding faster than supply can adjust. And this is precisely why I have consistently argued that token price deflation should not automatically be interpreted as evidence of weakening AI demand, because in a market with strong demand elasticity, lower prices can stimulate enough additional usage to increase the total amount of compute required even as the cost of each individual output declines. It can mean exactly the opposite, because lower token prices make more applications economical, those applications generate additional inference workloads, more inference increases the demand for accelerators and supporting infrastructure, and the resulting competition for scarce computing capacity can sustain or even increase GPU rental rates. The distinction is that intelligence is becoming cheaper at the application level while the physical capacity required to produce intelligence remains scarce, and that is a very different phenomenon from a conventional demand collapse in which falling prices reflect excess supply, weak utilization and declining willingness to pay. But there is an important qualification here, because rising GPU rental prices alongside falling token prices are not automatically positive for every company in the AI ecosystem, and the same market conditions that create strong demand for infrastructure can also create intense pressure on the companies that must purchase or lease that infrastructure. For infrastructure owners, higher rental rates can be beneficial when utilization remains strong and financing costs are controlled, because each additional hour of GPU availability can generate more revenue and the scarcity of capacity can improve pricing power, although the economics still depend on depreciation, power costs, networking, maintenance and the risk that a new generation of hardware makes existing equipment less competitive. For semiconductor manufacturers, sustained demand for additional compute supports equipment orders, capacity expansion and potentially attractive pricing, particularly when customers are competing to secure advanced packaging, high-bandwidth memory and leading-edge manufacturing capacity. For model developers, however, the relationship is more complicated because falling realized token prices combined with higher compute costs can compress gross margins unless improvements in model efficiency, batching, caching, hardware performance and utilization more than offset the pricing pressure, meaning that a model provider can experience explosive revenue growth while still facing difficult questions about the profitability of each additional unit of usage. This is why I would distinguish between the growth of the AI market and the distribution of profits within the AI market, because a larger market does not guarantee that every participant captures an attractive share of the value created. AI consumption can explode while individual model providers struggle to retain pricing power, particularly if customers can switch between competing models or if cloud platforms use their distribution advantages to negotiate lower prices. GPU demand can increase while certain infrastructure operators earn disappointing returns because they financed equipment at excessive valuations, overestimated utilization or underestimated the speed at which newer hardware would reduce the value of their existing fleets. And the semiconductor supply chain can enjoy strong orders even while the companies purchasing the hardware compete aggressively enough to reduce their own margins, because the strategic importance of AI can encourage businesses to spend heavily before they have fully demonstrated that the resulting workloads will generate attractive returns. These outcomes are not mutually exclusive, and in fact they may all occur simultaneously as the industry expands. That is also why Anthropic’s IPO prospectus becomes even more interesting when viewed alongside these charts, because the company has demonstrated extraordinary commercial growth, improving operating leverage and an expanding base of large enterprise customers, but it has also committed enormous amounts of capital to securing computing capacity for years into the future. If intelligence consumption continues expanding at the current pace, those commitments could prove extraordinarily valuable because Anthropic is effectively securing production capacity ahead of a potentially enormous demand curve, and the company may benefit from having access to compute that competitors or new entrants cannot obtain as easily during periods of scarcity. But if token prices continue collapsing while the cost of compute remains elevated, the company will need to become dramatically more efficient at converting GPU-hours into commercially valuable output, because revenue growth alone will not be enough if each additional dollar of revenue requires an even larger increase in infrastructure spending. The critical metric is not simply tokens consumed, but gross profit generated per unit of compute. And this is precisely where the AI industry is becoming increasingly interesting from an investment perspective, because we are beginning to see the emergence of two powerful forces operating simultaneously: rapid technological improvements are reducing the cost of producing intelligence, while expanding adoption and increasingly sophisticated applications are increasing the quantity of intelligence demanded. The interaction between those two forces could create a much larger market than investors currently anticipate, because every improvement in price-performance can make new categories of work economically viable, while every new category of work can create additional demand for models, data centers, networking, memory, power and specialized hardware. My base case remains that AI consumption will continue growing rapidly as inference becomes cheaper, models become more capable and agents begin performing larger portions of economically useful work, while the physical infrastructure required to support that consumption remains one of the most important bottlenecks in the global technology supply chain. That is why I remain constructive on NVIDIA, TSMC, ASML, Broadcom and the broader AI infrastructure ecosystem, while also recognizing that the ultimate profitability of individual model providers will depend heavily on pricing power, cost efficiency, customer retention, capital allocation and the ability to convert rapidly increasing usage into durable gross profit. As for Anthropic, the latest data make me more optimistic about its commercial momentum, but I would still want to see actual revenue conversion before treating US$100 billion in year-end annualized revenue as a central-case assumption, because the OpenRouter data are powerful evidence of accelerating usage but are not, by themselves, a substitute for consolidated financial disclosure. The company could very well reach that milestone, particularly if enterprise adoption, premium-model usage and agentic workloads continue expanding at anything close to the pace suggested by the latest data. The OpenRouter data certainly make the scenario more plausible, although they should be interpreted as evidence of a powerful demand signal rather than as definitive proof that Anthropic’s total revenue is already accelerating at the same rate. But the bigger conclusion is much more important than whether Anthropic exits December at US$80 billion, US$100 billion or some other annualized figure, because the broader economic implication is that falling prices may be expanding the market rather than destroying it. The world appears to be responding to cheaper intelligence by consuming substantially more of it, as developers use models more frequently, companies deploy them across more departments and increasingly autonomous systems generate workloads that would have been impossible to justify when inference was more expensive. And if that behavior persists, then the economic opportunity in artificial intelligence could continue expanding even as the price of each individual token declines, because the total amount of intelligence consumed may grow faster than the price per unit falls. The market is still debating whether AI infrastructure spending has gone too far, and that debate is entirely reasonable because capital intensity, valuation risk and competitive pressure are all real concerns. Meanwhile, developers are consuming more intelligence, companies are finding new applications for it and demand for computing capacity remains strong, which suggests that the underlying adoption curve has not yet reached the point at which lower prices are causing the market to contract. That is not evidence that every AI stock is attractively valued or that every dollar of capex will earn a good return, because the industry can grow rapidly while investors still overpay for individual assets or companies. But it is powerful evidence that the underlying technology is moving rapidly from experimentation toward large-scale economic deployment, with AI increasingly becoming part of the operating infrastructure of businesses rather than remaining a tool used only for isolated demonstrations. Cheaper intelligence does not necessarily mean a smaller AI market. It may be precisely what creates a much larger one. And that is why I continue to believe the biggest mistake investors can make is confusing falling token prices with falling demand for AI, because the more important question is whether lower prices are unlocking enough additional usage to increase total revenue, compute demand and economic value across the ecosystem. The price of intelligence is collapsing, the consumption of intelligence is exploding and the infrastructure required to produce it is becoming more valuable. Long AI.

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10/9/2026 on

The more I look at these charts, the more I think investors are still using an outdated mental model when they describe technology as simply one sector of the economy, because what is happening now is much larger than another strong earnings cycle for software and semiconductors, with technology increasingly becoming the dominant source of capital formation, corporate earnings growth, margin expansion, market capitalization and even incremental industrial activity across the United States, which is why I think the phrase “Tech Is the Everything Cycle” is actually much closer to reality than it initially sounds. The first chart may be the most important one because technology-related investment now represents roughly 55% of all U.S. capital spending, up from only around 15% in 1960, and that tells us something fundamental about how the structure of the economy has changed, because technology is no longer simply selling products into the economy but is increasingly determining where the economy itself invests its next dollar of capital, whether through semiconductors, software, R&D, data centers, networking equipment, servers, power infrastructure or the enormous physical buildout required to support AI. This becomes especially important in the current AI cycle because the distinction between “technology spending” and “industrial spending” is beginning to disappear, since building an AI model requires GPUs, but deploying those GPUs requires servers, networking, data centers, transformers, transmission lines, cooling systems, backup generation, gas turbines, copper, construction workers, financing and enormous amounts of electricity, which means one dollar of AI spending can create a chain of activity that extends far beyond the traditional technology sector and into industrials, utilities, commodities, construction and capital markets. That is why I increasingly think the AI boom should not be viewed merely as another software cycle, because it is becoming an industrial investment cycle built around computing, one in which digital intelligence is creating demand for an enormous amount of physical infrastructure and pulling capital into parts of the economy that would otherwise appear completely separate from technology. And this helps explain why an economy can remain surprisingly resilient even with the U.S. 10-year Treasury yield above 5%, because while higher rates are clearly tightening conditions for housing, leveraged private equity, commercial real estate and weaker companies dependent on cheap refinancing, the largest technology companies are simultaneously deploying hundreds of billions of dollars from internally generated cash flow into physical infrastructure, effectively creating a private-sector fiscal impulse that partially offsets monetary tightening elsewhere. That is a very unusual configuration, because normally higher rates suppress capital investment as companies become more reluctant to borrow and projects become less attractive as hurdle rates increase, but the AI hyperscalers are not behaving like ordinary leveraged companies because Microsoft, Alphabet, Meta, Amazon and the broader technology complex possess extraordinary balance sheets, enormous operating cash flow and increasingly urgent strategic reasons to invest, which means their capital spending is being driven less by the cost of external financing and more by the perceived opportunity cost of not having enough compute. This is one reason I think people asking why the Nasdaq can trade at all-time highs while the 10-year yield sits above 5% are sometimes looking at only half of the equation, because the discount rate has unquestionably risen, but the earnings, cash flows and addressable markets of the companies dominating the index have also changed dramatically, and if the numerator in an equity valuation equation grows faster than the denominator, stocks can continue rising even as rates remain uncomfortable. The second chart reinforces the same point from another angle because technology represents only roughly 35% of the world’s 100 largest publicly traded companies by company count but approximately 60% by market capitalization, which means tech companies are not merely becoming more numerous among the global corporate elite but are becoming disproportionately valuable once they reach scale, and that is not an accident because the economics of the best technology businesses are fundamentally different from those of many traditional industries. Software and digital platforms can scale revenue much faster than physical capital and labor requirements, network effects can strengthen competitive advantages as businesses grow, incremental distribution costs can approach zero, global markets can be accessed almost instantly and successful platforms can reinvest enormous amounts of cash into adjacent markets while maintaining extraordinarily high returns on capital, which means that once a technology company achieves sufficient scale, its economic value can expand far more rapidly than its physical footprint. Now AI is adding another layer to that advantage because the world’s largest technology companies already possess the distribution, customer relationships, data, cloud infrastructure, balance sheets and installed user bases necessary to deploy AI faster than almost anyone else, meaning that the companies that benefited most from the previous computing era may also possess structural advantages in the next one, particularly because they can fund development internally, distribute new products through existing platforms and absorb early losses while smaller competitors are still trying to build the necessary infrastructure. This is why market-cap concentration by itself does not automatically mean bubble, because concentration becomes dangerous when market capitalization separates from economic value creation, whereas these charts show that technology’s growing market weight has occurred alongside a growing share of profits, earnings growth and margins, which means at least part of the concentration is being driven by fundamentals rather than simply multiple expansion. That distinction matters enormously because the third set of charts may be the strongest evidence of all, with technology now accounting for close to half of S&P 500 profits, technology earnings having increased by roughly 3.8 times more than non-tech earnings over the measured period and tech appearing to have contributed approximately three quarters of total earnings growth, which tells us that the market’s increasing concentration in technology is not happening in isolation from the income statement but is instead reflecting where the profits are actually being generated. The index is becoming more technology-heavy because the profits are becoming more technology-heavy, and this is the part of the discussion that often gets lost when investors compare today’s market concentration with previous bubbles, because concentration in 2000 was accompanied by enormous speculation around businesses that frequently had limited revenue, negative cash flow and uncertain business models, whereas today’s dominant technology companies include some of the highest-margin, highest-cash-generating and most financially powerful corporations ever created. That does not mean valuations cannot become excessive, because of course they can, and NVIDIA can become overvalued, Microsoft can become overvalued, Meta can become overvalued and Alphabet can become overvalued, since even the greatest business in the world can still be a terrible investment at the wrong price, but that is fundamentally different from arguing that the entire technology leadership cycle must reverse simply because technology has become a large percentage of the market. Size alone is not a bear thesis, because the relevant question is whether the earnings supporting that size continue to grow, and so far, they are. The margin charts make the same argument even more clearly because S&P 500 margins themselves are historically elevated, but the expansion in technology margins has been particularly dramatic, with tech approaching roughly 20% plus profit margins versus something closer to high-single digits or low-double digits for much of the rest of corporate America, which is one of the reasons technology can support far larger market capitalizations without necessarily requiring absurd valuation assumptions. The market ultimately values cash flows, and a business producing US$100 of revenue at a 20% margin is economically very different from one producing the same revenue at a 5% margin, especially if the higher-margin company also grows faster, requires less incremental capital and can reinvest profits at superior returns, which is why the combination of growth, scale, margins and capital efficiency can justify a much larger share of total market value. And AI could widen this difference further because the conventional AI discussion has focused heavily on how much technology companies are spending, while I think the more important medium-term question is what happens to corporate margins when AI begins reducing the cost of knowledge work, given that software development, customer service, marketing, legal work, financial analysis, administration, recruiting and countless other white-collar processes contain enormous labor costs that can increasingly be augmented or partially automated by AI agents. The first stage of the AI cycle is therefore capex intensive, but the second stage could become margin expansive, because companies are spending heavily to build intelligence infrastructure today, while that infrastructure may eventually allow them to produce more output with fewer incremental workers, shorten development cycles, automate customer support, improve advertising conversion, optimize logistics and increase employee productivity, meaning that the payoff could appear not only in AI revenue but also in lower operating intensity across the entire economy. That is why I think AI may ultimately matter more for margins than people currently appreciate, because the market is obsessed with the question of whether hyperscalers can monetize their US$100 billion-plus capex programs directly through AI subscriptions and cloud revenue, while some of the return may instead appear indirectly through higher productivity, better monetization, lower unit costs and stronger margins inside existing businesses. Meta is a good example because AI does not need to become a standalone US$100 billion subscription business for Meta to earn a return on AI investment if recommendation quality improves, engagement rises, advertising conversion increases, creative generation makes advertisers more effective and personal agents eventually create new commerce opportunities. Alphabet does not need every Gemini interaction to generate subscription revenue if AI improves Search monetization, Cloud consumption, Workspace retention and advertising relevance, while Amazon does not need every dollar of AI infrastructure to be monetized through AWS alone if AI improves fulfillment, inventory management, advertising and retail efficiency, which is why measuring the AI return purely through direct AI revenue may substantially underestimate the economic impact. The final chart is also important because technology and energy have been leading S&P 500 earnings growth while the strength is simultaneously broadening, with roughly 94% of companies meeting or beating EPS expectations, which tells us that this is not currently a situation where a handful of technology companies are growing while the rest of corporate America is collapsing. That does not guarantee the breadth will persist, and earnings beats partly depend on how conservative analyst expectations were before reporting season, but it does weaken the argument that the equity rally is being held together entirely by five or six mega-cap technology companies, because the AI cycle may actually be creating a broader multiplier in which technology buys semiconductors, semiconductor fabs buy equipment, data centers require construction, construction requires steel and cement, power demand requires turbines, transformers and transmission, higher electricity demand benefits utilities and energy producers, networking expansion benefits optical suppliers, cooling requirements benefit industrial equipment manufacturers, data-center financing generates business for banks and private credit and, eventually, AI productivity benefits companies outside technology that adopt the tools. That is why the AI ecosystem keeps broadening even as the largest share of the economic value continues accruing to technology, because in some ways AI is beginning to function like the railroad, electricity and internet buildouts simultaneously, requiring huge physical infrastructure today while creating a general-purpose productivity layer that can eventually affect nearly every industry. This is also where I think investors need to distinguish market concentration from economic concentration, because if technology were 60% of market capitalization but only 20% of earnings and 15% of investment, I would be extremely uncomfortable, but when technology simultaneously represents a growing share of capital expenditure, profits, earnings growth, margins, R&D and market value, then the concentration is telling us something about the changing structure of the economy rather than merely reflecting speculative enthusiasm. The economy itself is becoming more technological, with a bank increasingly functioning as a software company with a balance sheet, an automaker increasingly operating as a software, battery and semiconductor company wrapped around a manufacturing operation, retailers increasingly competing through algorithms, logistics automation and digital advertising, drug discovery increasingly depending on computational biology, manufacturing increasingly relying on robotics, digital twins and AI optimization and media increasingly depending on recommendation algorithms and generative tools. Every company is gradually becoming a technology company because technology is becoming embedded inside the production function itself, and AI accelerates that transition dramatically, which is why I think saying “technology is too big” is becoming increasingly similar to saying electricity was too important during electrification or the internet was becoming too central during digitization, because at some point something stops being a sector and starts becoming infrastructure, and technology is increasingly reaching that point. There is, however, an important bearish interpretation of these charts that should not be ignored, because if technology now accounts for 55% of capital spending and the overwhelming majority of incremental investment, then the U.S. economy is becoming increasingly dependent on the continuation of the AI capex cycle, which means a significant slowdown in hyperscaler investment could have much larger macroeconomic consequences than it would have five years ago. This is the reflexivity investors need to watch, because the stronger AI investment becomes, the more industries expand capacity around it, the more data centers are built, the more power plants, transformers, networking equipment and semiconductor capacity are justified, and the more financing is raised, the more capital becomes dependent on continued utilization. Eventually, if AI monetization disappoints and hyperscalers simultaneously decide they have enough infrastructure, the slowdown would propagate far beyond NVIDIA, which is the genuine bear case, not simply that technology stocks are expensive but that AI capex eventually outruns economically useful AI demand. That is why I continue watching cloud backlog, GPU utilization, token consumption, inference volumes, agent adoption, data-center lease rates, GPU rental prices, residual values, hyperscaler capex guidance and the revenue generated per dollar of AI infrastructure, because these indicators should reveal whether the investment cycle is creating durable economic value or merely building capacity ahead of demand. So far, however, the evidence still points in the opposite direction, with GPU rental economics remaining resilient, older GPU residual values holding up, hyperscalers continuing to discuss insufficient capacity rather than excess capacity, cloud AI demand continuing to grow, token costs continuing to fall and making more use cases economic, agents dramatically increasing compute intensity and the largest technology companies continuing to produce extraordinary earnings growth despite spending unprecedented amounts on infrastructure. That does not look like the end of the cycle, because it looks like the cycle is broadening. This is why I remain LONG technology, particularly the companies controlling the bottlenecks, infrastructure, distribution and monetization layers of AI, because I think the biggest analytical mistake investors can make today is assuming that technology is simply repeating another historical sector boom when the evidence increasingly suggests something structurally different is happening. Technology is becoming the largest source of U.S. capital formation, producing an increasingly disproportionate share of global corporate value, generating close to half of S&P 500 profits, driving the majority of incremental earnings growth and operating with structurally superior margins, while AI is now pulling semiconductors, power, industrials, commodities, construction and finance into the same investment cycle. So perhaps the reason technology keeps outperforming despite high rates, enormous capex and constant bubble warnings is simpler than we think, because the market is not merely assigning a higher multiple to technology; the economy itself is becoming more technological. And if AI turns intelligence into an increasingly abundant, cheap and scalable input to production, then this transition may still be much closer to the beginning than the end, because tech used to be a sector inside the economic cycle, while increasingly, tech is becoming the cycle itself. Long tech.