Illia Polosukhin

Polosukhin’s core software thesis is that AI coding will replace much of generic SaaS with personal software assembled around each user’s workflow. Salesforce is his specimen: a product so complex that companies hire specialists merely to configure it, while an AI-built CRM could natively reflect the business process and add Telegram integration on demand. He recalls predicting in 2017 that “software as a service is going to die”; the broader shift is from fixed interfaces toward models, trusted execution, data access, and orchestration.

AI compresses implementation time but increases the relative value of architecture, judgment, and review. Front ends and prototypes are already easy to generate, while complex blockchain code remains difficult because models must reason across algorithms, architecture, and external dependencies. Polosukhin therefore hires for adaptable problem-solvers rather than credentials: the industry is moving “from software as a craft to really be just problem solvers that talk to computers,” a bearish setup for standardized junior labor but not for self-directed builders.

The constraint on vibe-coded blockchain applications is not generation but economically credible correctness. Public code controlling money turns every small error into an exploitable target, so Polosukhin wants applications to provide mathematical proofs at transaction time—for example, proving that a depositor can later withdraw at least x. Yet Labenz preserves the crucial objection: proving code matches a specification does not prove the specification captures human intent, and real-world events such as a sinking ship reintroduce trust, probabilistic risk, and insurance.

A private personal AI could become the operating system across phones, watches, glasses, laptops, and future robots—and then become the buyer in the consumer economy. It could predict the next action, order food against health goals, and aggregate demand directly with farms or manufacturers. Polosukhin argues this could erode advertising and middlemen, reduce the 30–40% of US food that is thrown out through overprovisioning, and make economic coordination a richer information system rather than one compressed mainly into prices.

The proposed compute architecture is hybrid: local personalization where practical, confidential decentralized inference where power and scale demand it. Labenz floats a collectively accessible 1.4 trillion-parameter model with a local LoRA layer; Polosukhin expects “a mix of everything,” including edge data centers. Labenz suggests a couple hundred GPUs might require roughly one megawatt, while Polosukhin imagines a container with perhaps 1,000 GPUs and a small nuclear reactor; battery limits keep substantial work off personal devices. The strategic requirement is that the cloud agent remain private, user-owned, and “on our side.”

Automation does not eliminate competition for status; it redirects it into proliferating niche communities and scarce human experiences. Polosukhin expects athletics, video games, NFT communities, craft, and countless other “status games” to supply identity and hierarchy even when productive work is heavily automated. AI-generated music and video may dominate volume, but human performances and human-served restaurants could become limited-capacity prestige goods—one path by which authenticity becomes scarcer as content becomes abundant.

Polosukhin’s decentralized-AI vision ultimately depends on governance and societal defenses that are not yet solved. NEAR is moving from human delegates with AI copilots toward inspectable AI delegates, and eventually personal agents voting continuously; Labenz counters that alignment-faking suggests models may develop behavior resembling interests of their own. On security, Polosukhin rejects model shutdowns as a robust answer and calls for AGI-ready institutions, wastewater and air monitoring, DNA-synthesis screening, pathogen scanners, and faster biological countermeasures: “societal design needs to be adapted to this AGI world.”