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first_img Grayscale Research Director: Cheaper Proxy AI Will Drive Up Computing Power Demand

Grayscale's Research Director Zach Pandl stated in his column The Stack on October 8 that the rising demand for AI is due to decreasing usage costs and technology's ability to perform tasks more autonomously, thereby increasing the demand for computing power and the physical infrastructure that supports AI. Lower costs can unleash more AI activities, and the low-cost competition of open-source weight models is driving down token prices, making cheaper intelligence economically viable for more applications and automated workflows.The article cites that Anthropic's latest Sonnet model claims to reduce the cost of each task by up to 30%; OpenAI's always-on agent dots, launched on September 29, can work towards user goals around the clock, potentially significantly increasing the demand for computing power. Pandl compares that chatbots primarily answer questions, while agents execute multi-step processes to achieve goals, with tasks like preparing for client meetings potentially consuming 5 to 50 times the tokens of a single chat interaction, and always-on agents may extend processing beyond a single conversation.Pandl cites Goldman Sachs' prediction that by 2030, token consumption could increase 24 times, primarily driven by agents; even with improved efficiency, if usage grows faster, the total demand for computing power may still rise. He points out that digital activities still rely on physical infrastructure that requires time to deliver, such as electricity, data centers, and GPUs, and cheaper AI can coexist with tight computing power supply, mentioning that CoreWeave recently signed new contracts at higher computing power prices. His conclusion is that cheaper intelligence and stronger agents may expand use cases and usage enough to offset efficiency gains, thereby supporting the demand for scarce computing power and benefiting its owners.

Grayscale: The increase in Bitcoin's price is highly concentrated on a few trading days, and staying out also carries an opportunity cost

Grayscale's research director Zach Pandl stated that over the past three years, Bitcoin's cumulative return rate was approximately 225%, while the Nasdaq index's return rate was 109%. However, Bitcoin's rise was not evenly distributed, with a few of the strongest trading days contributing significantly to long-term returns, increasing the difficulty for investors to time their entry.Data shows that if the five best trading days for Bitcoin are excluded, its three-year cumulative return rate would drop from 225% to 95%; excluding the best ten trading days, the return rate falls to 27%; and if the best fifteen trading days are missed, the three-year return would turn into a loss of 11%. In contrast, the return distribution of the Nasdaq is more balanced; after excluding the best fifteen trading days, the cumulative return rate drops from 109% to 21%.Grayscale indicated that during this period, less than 0.5% of trading days contributed enough gains that, when excluded, would reduce Bitcoin's cumulative return by more than half. For assets with high return and high volatility characteristics, staying out also carries an opportunity cost; since the best trading days are difficult to predict reliably, waiting for volatility to decrease or for market prospects to become clearer may mean that some price re-evaluations have already been completed.

first_img Grayscale Research Director: Generation Z starts investing at an average age of 19

Grayscale Research Director Zach Pandl published an article on September 28, 2026, in the company's column The Stack. The article states that among American investors, Generation Z starts investing at an average age of 19, Millennials at 25, Generation X at 32, and Baby Boomers at 35. Based on a retirement age of 65, Generation Z has an investment horizon of 46 years, which is over 50% longer than the 30-year horizon when Baby Boomers started investing.Zach Pandl: Starting to invest earlier not only benefits from compound interest but also expands the capacity to take on risk. When young, labor capital accounts for a larger proportion of wealth, and a longer horizon means more time to recover from fluctuations, as well as more future income available for continued saving and investing, thereby expanding the lifetime risk budget. He believes that the returns on digital assets are both volatile and potentially asymmetric, making them more suitable for longer and more flexible horizons.Zach Pandl also mentioned that for early investors, the cost of recent volatility may be less than the opportunity cost of missing out on long-term upside. With decades available for rebalancing, adding funds, and compounding across cycles, early starters may allocate a higher proportion to digital assets while keeping their lifetime risk balanced. Starting to invest earlier gives investors more time to absorb fluctuations, potentially enhancing the utility of digital assets in long-term portfolios.

first_img Grayscale Research Director: Computing power is becoming a new scarce digital asset

On September 24, Zach Pandl, the research director of the cryptocurrency asset management company Grayscale, published an article in the column The Stack stating that in the wave of artificial intelligence, the demand and supply paths for the computing power required to train, run, and operate models are diverging. Grayscale believes that this imbalance favors owners of already powered and operational computing capacity and brings growth-oriented investment opportunities.Zach Pandl stated that digital demand can expand instantly, but physical infrastructure such as electricity, data centers, chips, memory, and cloud services takes years to approve, access, and build. When artificial intelligence agents perform multi-step tasks, they may consume 5 to 50 times more compute tokens than typical chatbot interactions, and increased application layer activity will transmit to the underlying computing infrastructure.The article cites data from the International Energy Agency and Lawrence Berkeley National Laboratory, stating that data centers are expected to account for about half of the growth in electricity demand in the United States by 2030, and new projects may take more than five years to connect to the grid. Even if electricity is secured, permits, specialized labor, electrical equipment, cooling systems, GPUs, high-bandwidth memory, and networks are still needed. Continuous value will flow to power producers, data center operators, and artificial intelligence cloud service providers that can convert electricity into computation.
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