AI v/s Governments : What is really going on? | Travis Good, CEO of Ambient

Описание: The US IRS system update is one of the most notoriously bad IT projects in history with billions wasted on fraud and abuse. The IRS was using punch cards and still hasn't gotten off them completely. We expose why governments are incredibly slow moving and not responsive to citizens they're accountable to. If they can't get taxation right, which funds everything they do and should be the most important function, what will they actually be good at? The answer historically in the US has been almost nothing. State and federal governments interact with very local governments using incompatible systems, creating maximum friction.

LLMs can take virtually infinite text content and turn it into question and answer bots that help citizens access complex government services. People might wait 20 years in some cases to get a US green card because there's no fast way to get decisions. The impulse is to automate passport applications, Medicaid eligibility, and Medicare decisions using private software. This makes government decision-making loops dependent on completely opaque entities that run on ads and are essentially unauditable. Governments shouldn't invest in technologies that further segregate the commons or offer private companies benefits from what should be private government data. Before becoming a buyer, governments need to find a gem out of the trash bag and understand the playground first. Should governments be automating life-changing decisions about your passport, healthcare, and green card using private AI systems you can never audit?
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About Ambient.xyz:

Ambient is an SVM-compatible Proof of Work Layer 1 that pairs Solana-style execution with a new consensus mechanism called Proof of Logits (PoL). The goal is simple but non-negotiable: deliver fast, cheap, verified AI inference at scale, and do it in a way developers can integrate into on-chain, cross-chain, and web2 applications.

Ambient’s design centers on one large network model (600B+ parameters) and its fine-tunes, so miners optimize for utilization instead of wasting cycles across fragmented “model marketplaces.” The project positions verified inference as infrastructure: low overhead verification, high throughput, and a path toward open, transparent training over time.

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About Travis Good:

Travis Godwin Good, PhD is the CEO and Co-founder of Ambient.xyz. His public writing and research focuses on trustworthy AI infrastructure, verification vs. “truth,” incentives that degrade model quality, and why agentic systems need auditability down to execution rather than vibes and benchmark screenshots.

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Key words:
verified inference, Proof of Logits, useful PoW, SVM, Solana-style dApps, agentic economy, on-chain AI, open weights, audit trails, model provenance, censorship resistance, privacy-preserving compute, miner economics, decentralized training, Travis Good Ambient, verifiable inference