According to Greg Brockman, building pretraining infrastructure spans complexity management, abstraction design, operability/observability, and deep systems plus ML expertise, underscoring the operational intensity of large-scale model training (Source: Greg Brockman on X, Sep 7, 2025). For traders, this reinforces that AI training remains compute- and tooling-heavy, consistent with elevated GPU demand and hyperscaler capex reported in 2024 that have influenced AI-exposed equities such as GPU suppliers and cloud providers (Sources: NVIDIA Q2 FY2025 earnings release, Aug 28, 2024; Microsoft FY2024 Q4 earnings call, Jul 2024; Amazon Q2 2024 results, Aug 2024). The post adds no new product, spend, or timeline disclosures and includes no crypto references, suggesting limited immediate price impact absent follow-up announcements (Source: Greg Brockman on X, Sep 7, 2025).

According to Miles Deutscher, using ChatGPT to define risk parameters before entering cryptocurrency trades can help traders limit downside risk and maximize potential gains (source: @milesdeutscher, Twitter, May 20, 2025). By leveraging AI tools like ChatGPT, traders can systematically set stop-loss levels, position sizing, and risk-reward ratios tailored to volatile crypto assets. This AI-driven approach empowers users to make more disciplined and consistent trading decisions, which is crucial for capital preservation and long-term profitability in the crypto market.