The AI race isn't monolithic. It's a "jagged frontier" where different companies excel in distinct areas. For instance, Anthropic leads in software engineering, OpenAI in consumer chat, and ByteDance in video. This allows for multiple winners rather than a single dominant player.
AMP is creating a software grid to make today's fragmented compute resources (Nvidia, AMD, different clouds) fungible. This is analogous to how standardizing electricity to AC/DC unlocked a national grid, turning stranded pockets of power into an efficient, interoperable system.
Despite their pedigree from OpenAI, Anthropic's founders faced significant VC skepticism in late 2020. Investors called AGI a "pipe dream," highlighting how nascent the AI investment thesis was just years before the boom, forcing the team to scrape together an angel round.
Publicly announcing the number of GPUs a lab possesses is "bravado" and a poor indicator of its actual power. True capability is measured by model output and performance, as compute utilization varies wildly. Focusing on inputs instead of outputs is a common mistake.
Businesses don't ultimately care about which AI model they use; they want a job done efficiently and securely. The market will evolve towards trusted brands providing abstracted solutions that orchestrate hundreds of different models under the hood to complete a given task.
AI models improve dramatically in domains with objective feedback, like coding (unit tests) or science (lab results). Progress is slower in subjective fields like creative writing where feedback is opinion-based, explaining the uneven impact of AI across different types of knowledge work.
Simply creating an AI "sandbox" is insufficient for risk management. Leaders who lack hands-on technical literacy tend to misjudge AI's capabilities, leading to flawed strategies and employees misusing the tools in ways that are prone to hallucination and other risks.
The advertised per-hour GPU cost is misleading. Because research workloads are spiky and unpredictable, labs over-provision compute. This rampant underutilization means the effective price paid is often 10 times higher than the marketed rate, creating massive deadweight loss.
Exceptional AI user experiences, like Claude Code, are not just a better interface or "harness" on an existing model. They are a "symphony of improvement" where the interface is co-designed in parallel with the model, anticipating its new capabilities to create a seamless whole.
The primary driver for companies like Microsoft designing their own AI chips is economic. When 80 cents of every R&D dollar goes to a single vendor like Nvidia, creating custom silicon becomes a strategic imperative to control unit economics and reduce supply chain dependency.