Vivold Consulting

Startup Extropic raises the bar with AI-optimized chips that mimic thermodynamic principles

Key Insights

Extropic, a new hardware startup, claims it can massively reduce the power demands of AI data centers by applying thermodynamic computing principles. Its chips promise faster parallel processing with lower heat output—potentially rewriting the economics of AI compute scaling.

Stay Updated

Get the latest insights delivered to your inbox

Betting on Physics to Beat the AI Energy Crunch

As AI workloads explode, data center energy use is ballooning—and so are the bills. Extropic’s pitch is radical: build chips that mimic thermodynamic processes, allowing bits of data to behave more like molecules exchanging energy.

Why this matters


- Current GPUs are approaching physical efficiency limits; incremental design gains won’t keep up with model scale.
- Extropic’s architecture could yield multi-order-of-magnitude power efficiency gains by letting noise, rather than precision, drive computation.
- Investors are circling: venture interest in AI hardware startups focused on energy efficiency has grown 60% year-over-year.

If Extropic delivers, it could signal a new class of 'analog-inspired' AI accelerators, reducing both cost and carbon impact for hyperscalers.

Related Articles

An AWS knowledge-graph deployment turned 6-month research cycles into 3 weeks - and the blueprint transfers far beyond pharma

An AWS GraphRAG deployment in pharmaceutical research cut R&D cycles by 87% - initial discovery that took six months now closes in three weeks - by fusing siloed internal databases and public literature into one queryable knowledge graph on Amazon Neptune Analytics and Bedrock (running Claude). Every answer comes with verifiable citations and a mapped reasoning path, which is exactly what regulated industries need for compliance. The architecture is modular and, crucially, transferable: any enterprise drowning in fragmented legacy data can copy this pattern.

SpaceX, Anthropic, and OpenAI listings will out-value every US VC-backed exit since 2000 - reshaping vendor economics for everyone

The new NVCA-Pitchbook Venture Monitor dropped a stunning claim: the pending OpenAI and Anthropic IPOs, together with SpaceX's listing, will generate more value than every US VC-backed exit since 2000 combined. SpaceX is already public at $1.77 trillion, and with both AI labs pushing toward trillion-dollar debuts, the trio should land north of $4 trillion - against roughly $70 billion in total US IPO proceeds last year. For anyone buying AI services, the labs' shift to public-market scrutiny will reshape pricing, transparency, and vendor stability.

A 14-person open-source team just became the default way 8.9M developers run local AI - and a lever for slashing inference bills

Ollama, the open-source tool that lets developers run open-weight AI models on their own machines in minutes, raised a $65M Series B led by Theory Ventures ($88M total), revealing it now serves 8.9 million developers monthly and sits inside 85% of the Fortune 500 - with just 14 employees. Founders Jeff Morgan and Michael Chiang previously built Docker Desktop, and they're repeating the play: abstract away the hardware pain, then monetise a cloud tier priced on GPU time rather than tokens. The backdrop is the industry's loudest cost debate: every company with heavy inference bills is under existential pressure to shift routine workloads to open models.