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• 2. ⚡️ OpenAI challenges Claude
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• 5. ✍️ Today’s Policy Corner
• 6. 🗞️ The News Bulletin
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Latest in AI
OpenAI has launched a new $100/month ChatGPT Pro tier, filling a pricing gap that power users and developers have long requested. The mid-tier plan sits between the $20/month Plus option and the $200/month top tier, and targets developers who rely heavily on Codex for coding.
The $100 Pro plan offers 5x more Codex capacity than Plus, while the $200 tier provides 20x higher limits. OpenAI says both Pro tiers include the same core features, with rate limits being the main difference.
OpenAI is directly challenging Anthropic, which has offered a $100/month Claude plan for some time. The company claims that Codex delivers more coding capacity per dollar than Claude Code, particularly during active coding sessions.
The model maker reports that over 3 million people globally now use Codex every week, up 5x in the past three months, with usage growing more than 70% month over month.
For developers and teams who hit rate limits regularly on the $20 Plus plan but don't need the full capacity of the $200 tier, this fills an obvious gap. The timing and pricing make clear that OpenAI sees Anthropic as the competitor to beat in the professional coding market. With Codex usage up 5x in three months, OpenAI is capitalizing on growing demand for AI-assisted development tools. Note that higher rate limits are available through May 31, so early adopters should expect those to tighten afterward.
The AI Report Podcast
THE BUSINESS BRIEFING: HR & TALENT (powered by Upscaile)
A national staffing provider (30,000 placements/month, 20+ states) was losing 25-40 minutes per applicant on manual document review and compliance checks. Incorrect uploads triggered multi-day back-and-forth cycles. During peak hiring, verification backlogs stretched 2-4 days, killing fill rates and client satisfaction. They deployed Firstwork to automate document validation, identity verification, and multi-state compliance workflows.
Tool used: Firstwork -- AI-powered onboarding automation that detects document errors, verifies work authorization, and syncs verified workers into deployment systems.
Result: 75% reduction in onboarding time. Verification dropped from days to minutes. Recruiter workload cut by 60%, eliminating multi-day backlogs during peak season.
The lesson: Automation only works when integrated into existing deployment systems. The staffing firm synced Firstwork directly into scheduling and client placement tools so verified workers moved instantly from approval to assignment without manual handoffs.
Steal this: Map every manual touchpoint in your current onboarding process. Flag steps where humans wait on documents, compliance checks, or status updates. Those delays are your automation targets.
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THE POLICY CORNER
Federal and state courts punished at least three separate incidents of fabricated case law traced to generative AI tools. Oregon applied a per-citation formula: $500 per false case, $1,000 per fabricated quotation. The Sixth Circuit went higher, ordering $30,000 in direct fines and requiring sanctioned attorneys to cover opposing counsel's full appellate costs. All three cases involved attorneys who failed to verify AI-drafted citations before filing.
Deadline: In effect now. ABA Formal Opinion 512 (issued July 2024) already requires personal verification of every AI-generated citation under Rules 1.1, 3.3, and 5.3.
Your move: Run a citation audit on any AI-drafted filing from the past 90 days. If your firm uses AI for legal research or document review, implement mandatory manual verification before any court submission, the Oregon formula makes the math clear: 15 fake citations cost one attorney $15,500.
AI News
🏦 Oracle acquires AI investigation tech from Lucinity: Embeds AI agents into Financial Crime and Compliance Management platform to automate case workflows, surface context, and guide investigator actions, launching within 12 months. FULL STORY
💻 Intel and Google expand multiyear AI infrastructure deal: Intel Xeon 6 processors to power Google Cloud's AI orchestration and inference workloads, plus co-development of custom IPUs to offload networking and security functions at hyperscale. FULL STORY
📊 Gemini adds interactive 3D simulations: Google rolls out real-time 3D models with adjustable variables to all Gemini Pro users—visualize orbital mechanics, pendulums, or Doppler effects with rotatable models and live parameter sliders. FULL STORY
🏢 Field study proves workflow redesign beats task automation: Startups taught to redesign end-to-end workflows around AI generated 90% more revenue than peers who only used AI to speed individual tasks, solving the "mapping problem" unlocks profitability. FULL STORY
Trending AI Tools (The Full 2026 Tool Stack PDF Included Here)
A curated look at the AI tools quietly transforming how teams work.
Apollo — AI sales platform where users book 46% more meetings using the AI Research Agent
Kadoa is an AI web scraper for automated data extraction and transformation without coding
SWMS AI automates safety risk assessments and compliance documentation for high-risk tasks
⚡️ Looking for the exact AI tools our team uses to run The AI Report?
We just launched the 2026 AI Tool Stack…
The Money: AI inference capacity becomes the new arms race
Meta's $21 billion CoreWeave expansion and QTS's $4.6 billion green bond signal a fundamental shift: the AI infrastructure bottleneck has moved from training to inference. With Meta's combined CoreWeave commitment now at $35 billion through 2032, hyperscalers are locking in dedicated inference capacity years ahead of deployment.
Deals to know:
CoreWeave (Customer Expansion, $21B) -- Dedicated inference infrastructure for Meta featuring early Nvidia Vera Rubin deployments across multiple sites. Investor: Meta
QTS (Green Bond, $4.6B) -- AI data center build-out funded through sustainable debt. Investor: Blackstone-backed
Signal: Capital is shifting from model training to serving models at scale. Inference infrastructure providers with locked-in hyperscale contracts will command premium valuations as AI usage compounds faster than capacity can be built.
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