A technology leader's reflection on the recent shift in AI architecture—moving from vanity metric scaling to true omni-modal problem solving. We've spent the last few years treating text as the universal language of AI. It wasn't. It was just a limitation we had to accept.

A technology leader's 2025 reflection on growth beyond vanity metrics — rate of change, time to solve, presence at home and a focused sprint into 2026. He does not measure growth by net worth or follower counts. After some hard pivots, leadership stretch, a couple of hospital scares, and the reminder that presence at home matters more than any metric, he is closing the year grateful and clearer. Many doors have closed, better ones opened. In 2026, he is sprinting — building where it counts, governing his time, and choosing the right doors on purpose.

In early 2025, DeekSeep took the AI world by storm. Here are the brains behind DeekSeep - DeepSeek-AI published its DeepSeek-V3 technical report in late 2024, unveiling a 671 billion parameter mixture-of-experts (MoE) model that activates just 37 billion parameters per token to strike a balance between scale and inference efficiency. Whereas many leading AI labs like OpenAI maintain closed-weight models and release limited technical disclosures, DeepSeek chose transparency, publishing full architecture details and open-source weights to foster community-driven innovation. This openness contrasts with OpenAI’s strategy, which restricts detailed insights into training pipelines and proprietary optimizations, leaving external researchers to reverse-engineer performance gains. By positioning DeepSeek-V3 as a general-purpose foundation model akin to OpenAI’s GPT-4 series, DeepSeek laid the groundwork for downstream specialization, notably through the DeekSeek-R1 reasoning branch.