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Discover cutting-edge insights and trends in the artificial intelligence landscape with our comprehensive industry analysis. Stay informed on market dynamics, emerging technologies, and competitive strategies that shape the future of AI. We monitor the entire ecosystem—from silicon to cloud to applications—highlighting the key drivers of AI growth and its monetization potential in the coming years. Make informed business decisions with our data-driven research and actionable recommendations tailored for industry leaders.

"AI" Data and Research

Every AI dollar starts as a wafer and ends as a token.

We track the whole path. Foundry wafers and CoWoS capacity, compute silicon, HBM, optics, servers, racks, infrastructure spends, AI token demand, economics to use-cases — modelled in one connected dataset, refreshed quarterly, with analyst deep dives on every layer.

  1. Where capacity, not demand, sets the ceiling. Wafer consumption is built bottom-up from every AI compute vendor's roadmap, then checked against node and packaging capacity.

    • Wafer shipments by chip vendor and process node
    • CoWoS-S/R/L, EMIB-T, etc. allocation by vendor
    • Yields, utilisation rates and foundry–vendor roadmaps
    • Foundry allocation across TSMC, Samsung and Intel Foundry
    Covered in-depth further · Foundry Service

Every layer is delivered twice, on a quarterly or semi-annual cadence: once as a sliceable quantitative tracker and forecast, once as in-depth qualitative analysis. Subscribers get both, so a shipment revision always comes with the reason behind it.

Questions this answers

Supply & Capacity

  • Is there enough wafer and packaging capacity, and are utilisation trends strong enough, to support announced AI compute roadmaps — and who gets squeezed first if not?
  • How is wafer capacity and advanced packaging allocated between vendors, and how does it shift each quarter?
  • What are the wafer pricing trends and the future foundry process node and technology roadmaps?
  • What are the memory allocation, pricing and capacity trends by technology – LPDDR vs HBM – by supplier?

Compute & Competition

  • How is demand splitting between merchant GPUs and hyperscaler custom ASIC, and how fast is the mix moving?
  • Which custom silicon programmes are reaching volume, and which are quietly slipping?

Content per Rack

  • What is the memory, optics, power and cooling content per accelerator, server and rack — and the trend?
  • How fast is liquid cooling displacing air, and who captures the dollar content? What are the key trends?
  • What does an AI rack cost, layer by layer, and where is it most exposed to component pricing?

Power & Interconnects

  • How much compute can be delivered inside a fixed power envelope, and what is that worth?
  • How do rack-scale capabilities and deployments compare between NVIDIA, AMD, Google & others?
  • How will scale-up, scale-out and scale-across evolve from copper to optical interconnect technologies?

Demand & Monetisation

  • How is CSP capex allocated between in-house and commercial silicon?
  • Do current token economics support the capex being committed, and at what utilisation does the case break?
  • Is token demand outpacing token generation?
  • How are tokenomics changing between inference and training?
  • Which are the top AI Cities? Is Sovereign AI taking off? Who is winning and who is ahead?

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Jeongku Choi

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