What top creators are saying about OpenAI(MSFT)

Recommended for uploading spending data to identify 'unreasonable' spending patterns.

Demonstrates high value in reducing human error in high-stakes industries like healthcare diagnostics and professional services.

Launching GPT Live and GPT 5.6 (Sol) with focus on real-time voice interaction and agentic productivity; rumored to launch GPT-6 soon to maintain market leadership.

A core asset in the synthetic asset class of mega-private companies with high-fee access structures.

Facing regulatory hurdles with GPT-5.6 release but pursuing vertical integration through custom inference chips with Broadcom.

Startups building on frontier models like GPT-4o will see exponential product improvement as the underlying models advance.

Shifting focus toward enterprise and coding agents to capture high-value markets, though facing compute constraints and consumer pushback following GPT-4o deprecation.

Focusing on mobile remote productivity and a new cybersecurity initiative named Daybreak.

Shifting focus toward agentic workflows, 'Operator' roles, and dominating the General Agent market.

Leverages WorkOS for enterprise-grade features and represents the 'stranded demand' for nuclear power via AI data centers.

GPT-4 is cited as a foundational model for autonomous agents, though the high cost of API tokens is a noted risk.

Facing user friction and competition due to government partnerships and data lock-in concerns.

The release of GPT-4 served as a technical turning point, enabling systems to understand complex context and reason over unstructured data better than traditional deterministic systems.

Currently controls infrastructure for AI delegates, though faces potential conflict of interest risks vs decentralized models.

Useful for static analysis and document summarization, but viewed as 'clever math' rather than true AGI.

Projected to reach $200 billion in revenue with 40% coming from autonomous agents; models are beginning to architect their own successors.

General foundation models struggle with clinical intelligence due to messy medical data, requiring bespoke data sets for deep clinical verticals.

Currently hesitant to integrate crypto due to liability risks and preference for 'safe' experiences.

Pushing toward Opus 4.6 level capabilities, though facing rapid obsolescence cycles in model development.