According to @LexSokolin, technology scale is expanding, compute costs are falling, networks are accelerating, and AI capability is improving, countering the view that the sector is merely a bubble, source: @LexSokolin on X, Nov 12, 2025. Independent data show unit cost per TFLOP declining and GPU performance rising across NVIDIA Ampere and Hopper generations, strengthening AI workload economics and GPU marketplace viability, source: NVIDIA Ampere A100 and Hopper H100 architecture white papers (2020–2022). Global connectivity has accelerated with 5G and fiber adoption, improving bandwidth and latency that are critical for model training, inference, and distributed compute, source: Cisco Annual Internet Report (2018–2023) and Ookla Global Speedtest Index 2023. For trading, these secular efficiencies align with the usage thesis for decentralized compute networks and tokens such as RNDR and AKT that monetize GPU supply and demand, without implying a price forecast, source: Render Network litepaper 2023 and Akash Network documentation 2023.

According to Sam Altman, there is a significant discrepancy between the cost of compute resources and the value they can generate, suggesting that 50 cents of compute can produce 500 dollars of value. This statement emphasizes the potential for high returns on investment in computational resources, which could influence trading strategies within tech and AI-focused cryptocurrency markets. Traders might consider investing in tokens related to AI and cloud computing due to the potential for disproportionate value generation. Source: Sam Altman's Twitter.