Data Infra · 04
The economics of the AI buildout as 11 clean panels: LLM prices, GPU rents, token demand, audited hyperscaler capex. Every row carries its source URL and a quality tag, so your analysts cite the primary source, not us.
Accurate · Reliable · Source‑backed
Eleven panels · eleven endpoints
Call one panel or join them all. Every endpoint returns dated rows that carry source_url and quality_tag (company-disclosed / official / press / estimate), so what you pull is what you can cite.
Per-model pricing per million tokens for the major labs, every price cut since 2023.
Public token volumes and usage: monthly tokens, throughput, MAU/WAU/seat counts.
Market rental rates for AI accelerators (H100 and others) over time.
Cost-to-serve estimates per model tier and token demand mix.
Audited quarterly capex from SEC filings since 2019, segment revenue, guidance, neocloud backlogs.
Accelerator datasheet constants plus shipment anchors from Omdia, TrendForce and companies.
Power requirements and constraints behind the AI buildout.
Total-cost-of-ownership comparisons for AI compute.
Frontier benchmarks with saturation, training compute and cost, open-source lag, cost decline at fixed capability.
Documented cases of seat-based pricing moving to consumption pricing under AI pressure.
Press-reported revenue run-rates for private AI labs and infrastructure companies.
The data, not a description of it
Every panel row cites the primary source it came from, with an honest quality tag: company-disclosed, official, reputable press, or estimate.
Bargo Verification Layer · Accurate · Reliable · Source‑backed
A lab cuts its API price. Here is what happens before that number enters the panel:
Every row must trace to a primary source, and the figure must actually appear there. Pricing pages are archived at capture, because they change and vanish.
Company-disclosed, official, reputable press, estimate: the tag rides on every row, so audited capex is never silently blended with rumor.
Cost-decline curves, $/token trajectories and capex growth are arithmetic on the panel, never an impression of where things are heading.
Only then does the row join the panel, carrying its source, its tag and its archive. Facts scattered across hundreds of disclosures, assembled into something you can regress. See the full Verification Layer →
Access
AI-economy questions with receipts: prices, capex, capacity and who captures the margin.
Wire it into Claude, Cursor or your own agents, or pull straight into pandas.
All 11 panels as clean endpoints, source URL and quality tag on every row.
Who runs on it
Audited capex, guidance and backlogs in one panel: where the buildout is accelerating and where it is not.
Every number in your note traces to a disclosure, not to a data vendor’s black box.
Clean dated series that key to tickers where issuers are listed, ready for cross-asset work.
Coverage & delivery
| Coverage | 11 panels spanning prices, usage, hardware, capex, power, TCO, capabilities and disruption. Global where the supply chain is global, anchored to US-listed issuers. |
| Cadence | Rows added on disclosure events: earnings, price changes, shipment reports. |
| History | Capex from 2019; LLM pricing from 2023, the full commercial era; other panels from 2023-2024 onward. |
| Formats | JSON over API, CSV / Parquet snapshots, digest-stamped. |
| Identifiers | Tickers and CIKs wherever the row concerns a listed issuer. |
| Verification | Every row carries its source and its checks. Corrections are versioned, snapshots digest-stamped. How the Verification Layer works → |