On August 28, 2026, Sam Altman revealed a clear timeline of "achieving AGI by the end of 2026" in an exclusive interview with TIME. Mark Chen stated that OpenAI "has completed 80% of the journey," while Greg Brockman judged that in two years, people might view this period as the emergence stage of AGI, a notion that comes much earlier than the previously common betting on "around 2030," directly rewriting the global imagination of the technology growth curve. During the same period, Tencent announced that Hy4 achieved an engineering task score of 2.99/4 in internal blind tests, slightly higher than GLM-5.3's 2.92 and Kimi K3's 2.94, yet gave the cheapest output price among similar models—about 82% cheaper at most. According to informed sources, SoftBank is seeking about $10 billion in loans for refinancing debts related to OpenAI—performance, pricing, and leverage are all upgrading simultaneously, and the AGI race is transitioning from "technological expectations" to "balance sheet games." The market also responded to this new narrative: the Chinese stock market saw a reduced volume pullback that day, with the ChiNext Index and the STAR Market suffering the largest declines, while the Nikkei 225 rose 0.41%, and KOSPI fell 1.79%; WTI and Brent crude oil each rose about $0.5, spot gold surged about $18, and silver rose over $1. Expectations for technology growth, computing power, and energy demand, alongside safe-haven demand and inflation worries began to be lifted simultaneously. Against the backdrop of "AGI countdown" becoming a cross-market consensus, the macro risk aversion demand and risk preference structure are being rewritten, and BTC, often regarded as "digital gold," along with ETH, representing smart contracts and Web3 infrastructure, are being pushed to a new center of inquiry: how will the AI race reshape their pricing framework and premium structure through the redistribution of risk preferences and capital flows?

AGI Deadline from OpenAI and Reassessment of Tech Risks

When Sam Altman first stated "achieving AGI by the end of 2026" as an externally verifiable point in TIME, the market received not just an optimistic declaration but also a shortened structure of the technology growth timeline. The previous consensus surrounding "AGI around 2030" provided a generous waiting period for asset pricing; now, with OpenAI moving the deadline forward by four years, it compresses the future growth premium into a shorter time window, forcing investors to reallocate risk budgets ahead of a potential explosion in computing power, software, and data assets. Mark Chen's statement of "80% of the journey completed" on the internal progress scale signifies that AGI is no longer a distant blueprint but a project nearing high completion; Greg Brockman's addition that "in two years, people might view this period as AGI emergence" marks the current timeframe as a node similar to "the internet in 1995," amplifying the narrative of coexistence of acceleration and uncertainty: on one side, there is greed for the early fulfillment of AI dividends; on the other, fear of institutional, employment, and financial shocks.

This narrative structure directly rewrites the risk premium distribution between technology and macro assets. For high-beta technology assets, the deadline for AGI brings a combination of anticipated cash flow advancement and increased volatility; for gold, silver, and even BTC, which is regarded as "digital gold," there is visible long-tail risk and demand elasticity for the "ultimate safe-haven asset." The cryptocurrency market has fluctuated multiple times alongside US tech stocks and the Nasdaq in recent years, with BTC and ETH seen as part of "technology growth beta" and digital asset portfolios in many allocation frameworks. This exposure makes them naturally susceptible to the repricing of AGI expectations: when funds alternate between betting on "technology assets with early AGI fulfillment" and "hedging tools against AGI black swans," BTC could either be included in the safe-haven basket during panic or viewed as an extension of high-beta computing power and Web3 infrastructure during optimism, while ETH is more purely classified under "AI + on-chain infrastructure" trading structure, its premium begins to shift in line with adjustments to the AGI timeline.

Tencent Hy4's Low-Price Impact and Computing Power Inflation

As OpenAI brought AGI closer to "the end of 2026," the domestic computing power battlefield signaled more directly: what might be rewritten is the price structure of computing and electricity. Tencent announced that Hy4 scored 2.99/4 in an internal blind test involving 163 experts and 203 real engineering tasks, slightly higher than GLM-5.3's 2.92/4 and Kimi K3's 2.94/4, while offering the lowest inference output price among similar models—up to 82% cheaper. This is not just a performance ranking but a company cost sheet: when the inference price of "sufficiently usable" models in real engineering scenarios is reduced to less than one-fifth of the original, AI is no longer merely a "point upgrade" after calculation, but begins to permeate the entire business process.

The macro variable here is the deformation of the demand curve rather than software deflation. In the past when enterprises deployed large models, inference costs were one of the key constraints, determining which links were worth automating; when models like Hy4 reduce the unit price to this level, the question shifts from "Should we use AI?" to "What other computing power can be unleashed?" The overall demand curve shifts rightward: unit computing power becomes cheaper, but total computing power consumption and energy share rise as expected, forming inflationary pressure at the levels of computing power and electricity. This pressure line will naturally extend into the cryptoworld: BTC miners' profits are heavily dependent on electricity and computing power costs. If AI clusters continue to expand due to higher cost-effectiveness, competing for the same batch of data centers, chips, and electricity discounts along with mining, the side with higher marginal returns will gain an advantage in energy bidding, causing miners' cost curves to shift upward, which in turn affects BTC's security budget and block production cost pricing. For ETH and the on-chain capacities, storage, and AI applications built around it, the price war typified by Hy4 raises the narrative heat of "computing power assets," attracting funds to seek on-chain chips tracking AGI and computing power inflation; meanwhile, it sets the actual inference prices in the real world as a new valuation anchor: tokens solely relying on selling computing power resemble utility stocks with low marginal profits, whereas only projects that can integrate on-chain settlement, data sovereignty, or smart contract capabilities into AI production lines may sustain positive contributions to BTC's security premium and ETH's technology growth premium amidst this round of computing power inflation.

Market Divergence and the Swing of Risk Aversion-Driven Capital

The first layer of projection of the computing power narrative in the market is the obvious “dislocation” of global stock markets and commodities on that day. Against the backdrop of trading volume in the mainland Chinese stock market shrinking to about 2.1 trillion yuan, a decrease of 24.2 billion yuan from the previous trading day, major indices collectively experienced slight pullbacks: the Shanghai Composite Index fell 0.11%, the Shenzhen Composite Index fell 0.68%, the ChiNext Index fell 1.41%, and the STAR Market fell 1.85%, which superficially indicates a cooling of risk preferences. However, deep in the order book, there were still over 3,000 stocks rising, suggesting that capital did not withdraw as a whole but instead reduced leverage at the index level and made "maneuvers" at the structural level—technology growth and high valuation sectors came under pressure, while defensive and cash flow more certain sectors gained support. On the same day, stock markets in Japan and South Korea displayed divergence, with the Nikkei 225 index rising 0.41%, while KOSPI fell 1.79%, reflecting regional capital’s different trade-off between the "AI-driven manufacturing and export chain" and "domestic macro and geopolitical risks": Japan is seen as the relative beneficiary of AI hardware and equipment, while South Korea is more directly exposed to fluctuations in global demand and the semiconductor cycle.

The second layer of projection occurs in commodities and safe-haven assets: WTI crude oil rose to about $82.43 per barrel, and Brent to about $87.86 per barrel, each gaining about $0.5, while spot gold jumped about $18 to above $4,600, and spot silver rose over $1 to $70.1 per ounce, forming a combination of "inflation expectations + risk aversion sentiment." Funds are simultaneously pushing down the overall valuations of technology beta in the stock market while increasing hedges on future costs and risks in precious metals and energy, effectively oscillating between "betting on a long-term productivity leap brought by AGI" and "defending against short-term macro and geopolitical uncertainties." Within this framework, BTC is included in the “digital gold” basket of some institutions, along with physical gold and silver, to cater to risk aversion and hedging demand; ETH, on the other hand, is closer to the roles of mainland ChiNext, STAR Market, or South Korean growth stocks, seen as tied to smart contracts and Web3 infrastructure and extremely sensitive to tech stock and AGI regulatory sentiment. As the tech sector in the stock market adjusts positions and precious metal prices strengthen, crypto funds may experience structural divergence: one part might withdraw from high-volatility on-chain growth targets, flowing back into BTC for equivalent gold defense duration, while another part may accept larger valuation fluctuations, continuing to leverage ETH and its ecosystem to bet on the technological growth premium of the “AGI countdown.” This coexistence of risk aversion and offensiveness within the same asset class will become a crucial starting point for observing BTC's relative pricing and capital structure changes concerning ETH in the coming months.

SoftBank's $10 Billion Leveraged Bet on the OpenAI Track

On the same day that the market raised expectations around the "AGI countdown," informed sources reported that SoftBank is seeking about $10 billion in loans for refinancing debts related to OpenAI; specific terms remain undisclosed, but the direction is sufficiently clear: this is not a simple roll-over but a continued leveraged amplification of its risk exposure in the AGI track based on its already heavy investment. In the past tech cycles, SoftBank has influenced the valuation ranges of global tech stocks through large-scale leveraged investments; it's willing to bet another $10 billion of liabilities on the AGI node, effectively giving a price signal on the capital level for "AGI landing around 2026." In stark contrast, on the same day, mainland stock indices generally pulled back: the Shanghai Composite Index fell 0.11%, the Shenzhen Composite Index fell 0.68%, the ChiNext Index fell 1.41%, and the STAR Market fell 1.85%, with trading volume around 2.1 trillion yuan, a decrease of 24.2 billion yuan from the previous trading day. Amidst the backdrop of the secondary market actively lowering duration and reducing leverage, SoftBank chooses to use higher leverage to lock in further-out technology cash flows, this capital-level divergence is rewriting the discount rates and acceptable valuation ranges for technology assets.

When primary long-term capital is willing to use $10 billion in loans to exchange for a higher participation in AGI infrastructure, the market’s tolerance for “high uncertainty, high duration” is being forced to recalibrate, and this transmission chain will not stop at the equity end, but will extend to the pricing logic of AI + Crypto. In the last cycle, the financing wave dominated by large funds and traditional institutions significantly elevated the valuations and risk premiums of on-chain computing power and data-themed tokens; now, if similar scale capital continues to chase AI infrastructure around entities like OpenAI, the ETH ecosystem will be reinterpreted: it is not just a smart contract platform, but a potential settlement layer and resource scheduling layer in the AGI era. As demand for computing power and data assets is viewed as "AGI infrastructure allocation," the risk premiums of these tokens might transition from speculative factors to technology cycle factors, experiencing increased volatility while overall valuation ranges elevate, duration extends, ultimately altering the risk premium hierarchy of ETH and data-related tokens in the broader crypto market.

BTC and ETH in the 2026 AGI Race

When OpenAI presented the "AGI window by the end of 2026" and stated "80% of the path completed" to the market, the AGI countdown is no longer a distant vision but a mainline time anchor for the next one to two years; Tencent's signal of leading in engineering task scores and offering inference prices up to about 82% cheaper directly binds this time anchor to computing costs and energy demand. On the day when Chinese, Japanese, and South Korean stock markets showed sector divergence, with the Shanghai Composite Index experiencing volume pullback, and the STAR and ChiNext markets leading the decline, along with gold, silver, and crude oil moving strongly, it indicates that capital is repeatedly testing between "betting on high growth" and "returning to safe-haven." Within this framework, BTC and ETH are being reevaluated: the former is seen as "digital gold" within increasing numbers of allocation systems, catering to risk aversion and inflation expectations under AGI uncertainty alongside spot gold; the latter is regarded as technology growth beta, bound to computing power, data, and Web3 infrastructure, directly exposed to tech stock and AGI regulatory sentiment. For traders in the next one to two years, what truly needs attention are not just on-chain indicators but three sets of cross-asset observations: the evolution of the correlation between tech stocks and BTC prices, determining whether BTC appears more like gold or the Nasdaq in the "risk asset family"; the compression of computing power and energy costs on miner profits and supply rhythm, determining whether the AGI computing power price war tightens the inflation path for BTC; and the substitution and complementarity of gold and BTC in safe-haven functions, determining whether, in the event of macro shocks, capital chooses traditional metals or digital safe-haven assets. In the midst of technological uncertainties and regulatory risks brought by the AGI race, BTC and ETH could either gain additional valuation premiums due to the "digital safe assets" narrative or face pressure from technological substitution, bubble concerns, and policy tightening, with the key still resting on how these cross-market indicators evolve and are incorporated into new pricing models by mainstream funds.

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