The Workforce in Motion: Skills and pathways to future jobs in the United States report from McKinsey Global Institute is out. Some key findings.
  1. The US economy could create more jobs in the next decade than will be replaced through automation, but the change may require the largest and most sustained workforce transformation in US history. We estimate that roughly 11 million workers in declining occupations may need to transition to new jobs, ranging from six million to 16 million depending on the pace of automation adoption and its impact on labor demand.
  2. Jobs of the future, particularly growing jobs, could pay more on average but require more skills. Roughly 60 percent of growing employment could be in the top two wage quintiles, with much of the increase concentrated in healthcare, construction, and management occupations. Conversely, more than 70 percent of declining employment could be in the bottom two quintiles, primarily in office and administrative support, retail and sales, and transportation and logistics occupations.
  3. One in seven workers could have a direct pathway into growing jobs, but almost half may face an unpaved pathway. While every transitioning worker has a route into growing work, only about one in seven can make the move with no or very limited retraining. Barriers, particularly credentials or certifications, which roughly 85 percent of growing jobs require, can twist or block otherwise viable pathways. 
  4. Overall, more than 70 percent of workers could require some level of reinvention because new tasks may require more than 15 percent of their time. Whether or not workers change jobs, most of them will need to continually build new skills as most occupations reinvent regardless of whether employment grows, declines, or remains stable.
  5. Three types of skills help workers move, move up, and keep adapting.“Essential” skills widen career options, while “enabling” skills unlock higher-paying jobs. “Empowering” skills prepare workers for continuous change, and demand for them has already started to surge. The demand for AI fluency has increased 11 times since 2022, as demand for adaptability has increased fivefold and demand for resilience, curiosity, and willingness to learn has tripled.

Comments

Post a Comment

Popular posts from this blog

Image
Under the Joint Artificial Intelligence Governance model, frontier AI companies must demonstrate their ability to self-regulate. This is a recent example of actions towards achieving this goal. OpenAI, Anthropic probe tens of thousands of AI model security incidents As for its part of establishing standards, licensing, auditing and liability, government is in the stage of publicly probing AI companies nationally and internationally. These are sample probes into AI.  Senior leaders from Anthropic, OpenAI, Google and Meta testified under oath during a New York City Council hearing. U.S. FTC to Compel Testimony from AI Executives in Sweeping Probe of OpenAI and Anthropic U.S. Will Now Examine National Security Implications of New AI Models, Pre-Release OpenAI Faces Congressional Probe Over Swarm Hacking Incident Australia Summons OpenAI, Anthropic CEOs to AI Probe
Read more
Image
Financial Sector Governance could be the closest model to follow for Artificial Intelligence governance. How does the Financial sector work and how does it help in the case of AI? The financial-sector model essentially says: Private power + public authority + shared infrastructure + systemic risk requires joint governance . AI increasingly has exactly the same characteristics. Consider the parallel: Financial Sector AI Ecosystem Banks AI Companies Financial Markets AI Platforms/Models Capital Compute/Data/Investment Regulators AI Regulatory Authorities Central Banks Strategic/National AI Institutions Financial Infrastructure Cloud, Chips, Data Centers Systemic Financial Risk Systemic AI Risk Regulatory Capture AI Regulatory Capture Financial Stability AI/Strategic Stability Public/Private Governance Government–Industry AI Governance The most important lesson from Financial Sector Governance ...
Read more
Image
Artificial intelligence is developing along two major—and increasingly interconnected—paths: Commercial/Economic AI and Strategic AI. They require different governance philosophies. Commercial/Economic AI The guiding principle is: Regulate the risks while preserving competition and innovation. Its institutional foundations resemble those developed for other powerful economic systems: Financial regulation Antitrust law Consumer protection Environmental regulation Occupational safety Privacy law The objective is to allow innovation while preventing companies, markets, or technologies from imposing unacceptable costs on society. Strategic AI Strategic AI presents a fundamentally different challenge. Its guiding principle is: Maintain human control and strategic stability while preventing catastrophic misuse or escalation. Its institutional ancestors are therefore different: Nuclear arms control International humanitarian law Military command structures Intelligence oversight ...
Read more