Model builders are funded by hyperscalers, but hyperscalers extract the real economic value, highlighting the power imbalance in the AI supply chain.

The insight that in fast-moving AI markets a single product release can make or break a business underscores the need for constant innovation and risk...Show more

View modern cloud providers like Microsoft Azure not just as infrastructure but as a marketplace aggregating every tech vendor’s software offerings.

Differentiate between investor money flow (e.g., Microsoft, Nvidia, AMD investments) and actual customer payment flow when evaluating AI partnerships ...Show more

Nvidia’s blockbuster earnings illustrate sustained investor confidence in AI hardware and set a high bar for gross margins among compute-intensive tec...Show more

Many developers call out MCP as flawed yet rarely offer concrete replacements, creating a vacuum in structured prompt engineering methods.

Roughly 60–65% of surveyed startups build their products primarily on open source AI models, defying the hype around big proprietary players.

Current US-based open source models like Cogito 2.1 are significantly behind state-of-the-art offerings, though leading companies are increasingly ope...Show more

Adopting AI models is easiest in browser workflows, suggesting browser-embedded experiences can drive faster developer switch-overs than IDE plugins.

Gemini’s overperformance on ARC AGI-2 versus ARC AGI-1 suggests uneven task alignment and warrants deeper analysis of benchmark design.

Coding use cases hinge critically on performance in the SWE bench, making it the primary metric for developer-focused AI model selection.

One consolidated “Soda” model is now the de facto across most benchmarks, favoring a single-model-fits-all strategy over multiple specialized AI model...Show more

Tom Spencer humorously dubbed unsupervised simultaneous editing by multiple agents an “agent orgy” and concluded that uncoordinated multi-agent code e...Show more

Even identical AI models can behave differently depending on the service provider, impacting application performance and integration.

Early-stage AI tools should be judged with reasonable expectations as they are brand new and subject to rapid improvement.

Just because a technology is trending doesn't guarantee its value—always dive deeper into both the AI models and their full stack before committing.

No single model is best for every use case—differences in behavior between Claude, GPT, and Kimi mean choice should align with specific tasks.

Although not exactly GPT-5, Kimi 2 delivers a 'frontier model' vibe for general research tasks and deep agent workflows.

Opening access of agent engineering tools to non-developers democratises the creation and iteration of AI agents beyond traditional developer roles.

Agent builders target automating tasks that developers typically wouldn’t bother building manually, even if they had the technical skills.