NVIDIA Corporation NVDA

NVIDIA's CEO Jensen Huang displays products onstage during the annual Nvidia GTC Artificial Intelligence Conference at SAP Center in San Jose, Calif., in March. (AFP via Getty Images)
Nvidia is on a roll. The chip maker’s stock looks set for an assault on record highs and it has a lot to do with the surprising durability of its hardware.

Nvda/ NVIDIA's CEO Jensen Huang displays products onstage during the annual Nvidia GTC Artificial Intelligence Conference at SAP Center in San Jose, Calif., in March. (AFP via Getty Images)
Nvidia is on a roll. The chip maker’s stock looks set for an assault on record highs and it has a lot to do with the surprising durability of its hardware.

Nvda/ actually only down 2%, is the sky falling? Traders today are such pusses!

Nvda/ buying all day, holding for the long term, social security will collapse.

Wall Street’s most recognizable stock commentator just made a call. He wants a company to announce the biggest share repurchase in corporate history. That company is already buying back tens of billions of its own stock.
Jim Cramer made the case on “Mad Money” on Sept. 1. He argued that Nvidia should begin a $500 billion buyback and repurchase shares daily. Yes 500 Billion Buy Back to be. NVDA. Let the Buy Backs Begin !!

Nvda/ white dude reminds me of that gulliver travel guy that always said IT Will NEVER WORK, he said that about everything! LOL!

Nvda/ white dude here is your mandatory reading assignment for today, future assignments will follow.

AI and the Workforce: How and Where AI Is Used Today

April 22, 2026
Jack Malde
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AI is reshaping how people work, and doing so faster than many anticipated. This explainer sets out the current state of AI use in the workplace: how workers are using AI tools today, the extent to which AI is automating tasks versus augmenting human performance, and how use varies across industries, regions, and firm sizes.

This brief is a part of an ongoing series examining the impacts of AI on the U.S. workforce and implications for our education, training, and workforce systems. Explore the AI and Workforce Navigator to learn more.

Writing, coding, and research dominate workplace AI use

There are many types of AI, including generative, predictive, agentic, and physical. Generative AI (GenAI), which produces new content based on patterns learned from large datasets, has captured the most public attention given its widespread use across the economy—roughly 40% of workers use GenAI at work, particularly large language models (LLMs) such as Claude, ChatGPT, Gemini, and Grok. Other types of AI tend to have a narrower set of use cases in select industries.

AI companies OpenAI and Anthropic have published insights on how workers are using their LLM tools, revealing that workers most often use these models for writing, editing, research, coding, and generating ideas. Notably, AI is used as much as an advisor or thinking partner as it is for task completion: around half of ChatGPT interactions involve seeking information or guidance rather than producing a direct output.

Top 5 Most Common Uses of Claude in the United States

1. Assist with software development, debugging, and programming across multiple platforms 12.4%
2. Assist with mathematics, physics, and STEM homework assignments 8.1%
3. Assist with translation, writing, editing, and document formatting tasks 7.6%
4. Assist with academic research, scientific writing, and educational content creation 7.5%
5. Get help with daily life tasks, personal information, and household management 7.3%
   Source: Anthropic Economic Index – Understanding AI’s effects on the economy. Last updated March 24, 2026.
   Another growing use of AI is in professional recruitment: 60% of companies report that they use some sort of AI tool in the recruiting or hiring process to screen candidates, write job postings, draft interview questions, or automate candidate communication.

AI is used slightly more for augmentation than automation—for now

AI generally affects tasks in one of two ways: it may automate them, eliminating the need for human input, or can augment human performance, helping workers complete them more efficiently or effectively.

Data from Anthropic shows that use of Claude leans slightly more toward augmentation (53%) than automation (44%) overall. But this varies significantly by task: Anthropic identified 79% of conversations on Claude Code, an AI-powered coding assistant, as automation. Note that these figures capture personal as well as professional use—ChatGPT usage data suggests that only around 30% of LLM use is work-related.

Use varies widely by sector, region, and firm size

Survey and usage data at the firm level provide insight into how GenAI usage differs across industries, regions, and firm size.

The Census Bureau’s Business Trends and Outlook Survey (BTOS) is updated every two weeks with granular data on AI use based on a large, representative sample of 1.2 million U.S. businesses. It asks the question:

“In the last two weeks, did this business use Artificial Intelligence (AI) in any of its business functions? (Examples of AI: machine learning, natural language processing, virtual agents, voice recognition, etc.)”.

Because BTOS uses a broad definition of AI that may include older technologies and new tools like ChatGPT and Microsoft Copilot, its figures should be interpreted as general indicators of usage trends rather than precise measures.

According to BTOS, the share of firms that use AI tools varies considerably by industry: AI usage specifically in the production of goods and services is most prominent in high-wage, knowledge-based areas, such as the information sector (42%) and the professional, scientific and technical services sector (37%).

Sectors requiring physical work such as agriculture (4%) and accommodation and food services (8%) see the lowest use of AI. This is backed up by a Gallup poll, which finds that white-collar workers regularly use AI more often than blue-collar workers. However, AI is beginning to emerge in a wide range of sectors, such as coordinating robots to move packages in warehouses.

According to BTOS, AI use also differs across states, with particularly high usage in Washington D.C. (33%) and low usage in Rhode Island (7%). Data from Anthropic shows that industry concentration influences the usage of their LLM Claude: for example, usage in D.C. is driven by document editing and career assistance, IT in California, and financial services in Florida. Early evidence suggests that GenAI use specifically is highest in large, high-skilled, urban areas.

AI use also varies by firm size. Firms with 250 or more employees are seeing the greatest use (37%), followed by those with between 100 and 249 employees (29%). Only roughly 1 in 5 businesses with fewer than 100 employees reported AI use. There is less variation in AI use among firms with more than 100 employees, though the highest usage rates tend to be concentrated in the largest firms (more than 500 employees).

The Bottom Line for Policymakers

AI use is concentrated in high-wage, knowledge-based sectors and urban areas. Smaller firms are also lagging on AI use, putting them at risk of falling behind larger, faster-moving competitors. Policymakers can ensure that workforce responses are targeted accordingly, reaching workers in smaller businesses and lower-use regions, and designed to adapt as adoption spreads.

What you talkin bout white dude Willis?

What you talkin bout white dude Willis!

Nvda/ not seeing a pull back, momentum is at an all time high, Jensen is constantly improving the dynamics of this business, too bad he is not our president, he would be the greatest of all time!