Claude Opus 4.7 has been used to patch over 2,100 vulnerabilities

Anthropic leads OpenAI in business adoption, according to Ramp.

Tools and training are rarely tailored to the ways small businesses operate, and as a result their use often stops at the chat window.

Military personnel and Defense Department civilians have used a version of Google Gemini’s [Agent Designer](https://docs.cloud.google.com/gemini/enterprise/docs/agent-designer) to create over 100,000 semi-autonomous AI agents in less than five weeks since the tool became available

the top conversations we have been hearing from AI leadership (CTOs, VPs, Founders) have all centered around the concept of “Tokenmaxxing” and how leaders want to get their teams using more AI, WITHOUT the downside of incentivizing the kinds of horrendous waste

Ask ten different programmers how they use AI, and you can get ten different answers.

At one extreme, there's [fully giving into the vibes], and at the other extreme, there's [disabling all AI features].

Open Loop + Finite Demand = Utility Tools. Preparing 10-Ks & 10-Qs. Legal contract review. Insurance claims processing. One report per quarter, one contract per deal. AI makes the work faster, but doesn't create new work to do.

Claude is now being deployed to NEC Group employees around the world

As part of its long-running Client Zero initiative, in which NEC serves as its own first customer before offering its technology to clients

coding patterns are bimodal: in 41% of sessions, agents author virtually all committed code ('vibe coding'), while in 23%, humans write all code themselves.

White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates

A new global survey of 3,750 executives and employees across 14 countries, conducted by SAP subsidiary WalkMe for its fifth annual State of Digital Adoption report, finds that more 54% of workers bypassed their company's AI tools in the past 30 days and completed the work manually instead.

This level of penetration in such a short period of time is remarkable since Fortune 500 enterprises are not known to be early adopters of technology. Historically, many startups had to initially sell to other startups to get early momentum, and it was only after a few years that a startup would be able to land its first enterprise contract.

**Coding, support, and search**represent the lion's share of use cases by far (with coding being an order-of-magnitude outlier even among this set), while the**tech, legal, and healthcare sectors** have been the industries most eager to adopt AI.

Based on our analysis, **29% of the Fortune 500 and ~19% of the Global 2000**are live, paying customers of a leading AI startup.

Legal was surprisingly one of the first-mover industries in AI. Legal was historically known to be a difficult market for software, with lengthy timelines and a less tech-forward buyer.

Based on our analysis, **29% of the Fortune 500 and ~19% of the Global 2000**are live, paying customers of a leading AI startup.

Let that sink in.

Visa has deployed a validator node on the Tempo blockchain, designed specifically for Agent-to-Agent payments

公司也优先把资源砸在能直接产生商业价值的 B2B 场景

The share of U.S. adults who used Claude in the past week rose from 3.0% in early March to 4.3% in early April 2026

We present a comprehensive adoption snapshot of the leading open language models and who is building them

They intentionally deploy two or three AI tools for the same use case. Not because of indecision—but by design. Redundancy is policy.

Agents gain credibility by doing. The fastest way to get other people to trust and use your Plus One is to have it execute tasks in public.

Anthropic says Managed Agents is designed to cut the time it takes to move from prototype to production from months to days, with early adopters like Notion, Rakuten, Asana, Vibecode, and Sentry already using it across coding, productivity, and internal workflow automation.

in 2024, 47% of AI solutions were built internally and 53% were purchased; today, 76% of all AI is purchased rather than developed in-house.

in 2024, 47% of AI solutions were built internally and 53% were purchased; today, 76% of all AI is purchased rather than developed in-house.

Within three to four months, you can run a model with similar performance on your laptop; 23 months later, you can run the same model on your phone.

Within ChatGPT Business and Enterprise, the number of Codex users has grown 6x since January.

Silicon Valley has not demonstrated joined-up thinking for deploying AI.

But won’t this stifle innovation, one might worry? Quite the opposite, we think. Europe's competitive advantage in AI is unlikely to arise from pouring hundreds of billions into building the largest foundational models. Instead, it will come from industrial adoption, effectively integrating GPAI into useful downstream applications–an approach that plays to Europe’s true strengths: rich data pools, world-class applied engineering capabilities and dynamic SMEs, which make up 99% of all businesses.