Clarity

Pluralis is building a decentralized AI training protocol — running live on consumer GPUs with weights no single party can extract — as a structural alternative to a corporate AI oligopoly that would otherwise lock in permanent control over intelligence.

DeepSeek is engineering its models around extreme KV cache compression and hardware-portable kernels so Chinese chipmakers can route around HBM and CUDA, with the payoff being a $10T domestic AI hardware ecosystem and a $1T valuation for DeepSeek itself.

The application layer splits into a 'Yellow Brick Road' the labs will walk and a 'rest of Oz' where defensible value comes from owning the system of work in a specific vertical, with model capability as a fungible input underneath.

Transforming a company with AI means redesigning workflows around a deterministic-agent-human split, not bolting AI tools onto existing operations.

DeepSWE is a long-horizon software engineering benchmark whose original tasks, behavior-grading verifiers, and unmerged solutions claim to separate frontier coding agents that look tied on public leaderboards.

Sundar Pichai concedes that ChatGPT forced Google into an aggressive reorganization while publishers like Condé Nast now plan as if Google search referrals will go to zero, against a backdrop where the pace of AI progress — not the AGI label — is what actually matters and where public anxiety is a rational response to real disruption.

Mythos Preview can now chain primitives like use-after-frees, arbitrary read/writes, and ROP into working exploits at senior-researcher level and prove them by running code, which shifts the defensive priority from patching speed to architecture that makes bugs unreachable.

Putting gigawatt-scale AI data centers in dawn-dusk sun-synchronous orbit looks technically and economically viable because space offers continuous solar power, effective radiative cooling, and an escape from terrestrial permitting bottlenecks.

Nvidia's new Hyperscale vs. ACIE reporting split reveals where it expects to keep capturing value long-term: with customers who buy the full stack, not the hyperscalers trying to reduce it to a chip vendor.

The AI data center buildout points to roughly 150GW of new capacity and $7.5T of spend over 4.5 years, making a Neocloud boom — and trillions in enterprise value for operators beyond the hyperscalers — close to inevitable.

OpenAI has scale but no durable moat: frontier models are converging, ChatGPT's huge user base is shallow and mostly unpaid, and owning the model or the cloud beneath it confers little leverage over what gets built on top.

Trying to rank jobs, companies, or industries by their exposure to AI looks rigorous but is really an attempt to forecast second- and third-order effects that even domain experts have always missed.

Across 30+ studies, AI chatbots reliably improve immediate output quality while degrading the underlying cognition, knowledge retention, and independent judgment of the people using them — but design choices that force active rather than passive use can prevent or reverse the harm.

Fully autonomous AI R&D — models end-to-end training their own successors — has a better-than-60% chance of arriving before the end of 2028, with a first proof-of-concept on non-frontier models likely within a year or two.

Artificial superintelligence turns cognition into a commodity and theoretically solves most domains, but capturing that abundance requires aiming it through mission-driven Moonshots and breaking the institutional 'Muddle' that now blocks the final stage of a four-step revolution.

Treat AI as a normal general-purpose technology like electricity or the internet — a frame that reorients work toward keeping humans in control, preventing dangerous concentrations of power, and confronting systemic harms rather than chasing superintelligence scenarios.