US Labor Dept Rejects AI Job Doom, Pushes 1 Million Apprenticeships

The Impact of AI on the American Worker With US Department of Labor — Ai4

Ai4

Department of Labor Chief Innovation Officer Taylor Stockton argues AI expands net employment, urging a pivot from traditional college degrees to one million debt-free apprenticeships to meet tech labor demands.

AI-assisted summary of The Impact of AI on the American Worker With US Department of Labor — original source. Check the original for full context.

Source published: 2025-08-28

Detailed notes

## Background Published on YouTube by the Ai4 conference channel on August 28, 2025, this presentation features Taylor Stockton, Chief Innovation Officer at the U.S. Department of Labor. According to the video description, Stockton addresses the department's perspective on how artificial intelligence will reshape the labor market and details the Trump administration's strategy to accelerate research, eliminate regulatory barriers, and expand economic opportunity for the American workforce.

## TL;DR Taylor Stockton outlines the U.S. Department of Labor's proactive, worker-centric strategy for navigating the rise of artificial intelligence across four guiding pillars. Rejecting alarmist forecasts of technological unemployment, he presents evidence showing net job expansion in AI-exposed roles and explains why human oversight and institutional frictions prevent end-to-end autonomous displacement. Stockton introduces federal agility initiatives, including the DOL's new AI Workforce Research Hub and accelerated talent pipelines for physical infrastructure workers like data center technicians and lineworkers. Finally, he calls for dismantling outdated 'train and pray' education programs in favor of universal AI literacy and an expansion of debt-free, employer-driven registered apprenticeships tied to verifiable wage outcomes.

## Atmosphere & style The session kicks off on an engaging, self-deprecating note as Stockton laughs off a chaotic 60-second hotel-room scramble to make the stage alongside a hoarse voice from a late night in Las Vegas. From there, the delivery transforms into a disciplined, confident, and bullishly optimistic policy briefing infused with administration-level momentum. The talk balances empirical labor market statistics with an impassioned, populist critique of traditional higher education, creating an urgent yet reassuring tone designed to rally industry leaders away from apocalyptic narratives and toward practical workforce mobilization.

## Key takeaways - [01:10](https://www.youtube.com/watch?v=FnUJVYhvscA&t=70s) The Trump administration's AI strategy places the American worker directly at the center of the nation's push for global AI leadership and economic competitiveness. - [03:12](https://www.youtube.com/watch?v=FnUJVYhvscA&t=192s) The Department of Labor views AI as an unprecedented opportunity multiplier that will create new job categories, enrich existing occupations, and enhance physical workplace safety. - [04:00](https://www.youtube.com/watch?v=FnUJVYhvscA&t=240s) High-paying emerging AI roles, such as prompt engineering and synthetic data management, provide viable pathways to middle-class mobility without requiring a four-year college degree. - [05:00](https://www.youtube.com/watch?v=FnUJVYhvscA&t=300s) Federal workforce development programs are systematically aligning funding streams to support a presidential executive order mandating universal domestic AI literacy. - [06:35](https://www.youtube.com/watch?v=FnUJVYhvscA&t=395s) Concerns over mass technological displacement are overstated; economic data from PwC shows occupations most exposed to generative AI experienced a 38% increase in net job growth. - [07:30](https://www.youtube.com/watch?v=FnUJVYhvscA&t=450s) End-to-end automation remains impractical because human oversight is essential at both ends of the workflow: framing context and strategy at the start, and evaluating output at the conclusion. - [09:00](https://www.youtube.com/watch?v=FnUJVYhvscA&t=540s) Full AI adoption faces significant real-world friction, including consumer preference for human contact, legal oversight, and multi-year organizational restructuring timelines. - [10:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=639s) The greatest structural risk in the AI economy is the unprecedented velocity of skill evolution rather than aggregate job loss, requiring public institutions to cultivate systemic agility. - [11:30](https://www.youtube.com/watch?v=FnUJVYhvscA&t=690s) The newly launched AI Workforce Research Hub will track occupational task shifts in real time to rapidly translate market data into responsive federal workforce policies. - [12:15](https://www.youtube.com/watch?v=FnUJVYhvscA&t=735s) Federal initiatives are prioritizing accelerated pipelines for foundational technical and blue-collar roles, such as electricians and data center technicians, to support the AI infrastructure buildout. - [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) The DOL is rejecting the legacy 'train and pray' model—where only 34% of trainees find jobs in their field—and challenging the 'college-for-all' movement after 52% of 2023 graduates faced underemployment or unemployment. - [15:00](https://www.youtube.com/watch?v=FnUJVYhvscA&t=900s) The administration aims to surpass 1,000,000 registered apprenticeships by expanding the debt-free model into modern tech fields and enforcing pay-for-performance standards on federal training grants. - [16:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=999s) Leadership emphasizes shifting the national conversation from defeatist fear to workforce empowerment, preparing the next generation of American workers to design, manage, and govern AI systems.

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=FnUJVYhvscA&t=4s) Conference Scramble and Introductory Remarks

Taylor Stockton opens his address with humor and a pair of self-deprecating apologies. First, he explains a chaotic scheduling scramble: having assumed his session was slated for 3:00 p.m., he was sitting in his hotel room when he received an urgent call from the AI4 conference organizers at 11:29 a.m. asking where he was, only to be told his presentation was starting in 60 seconds. Grateful that the session could be moved to an extended slot and that attendees showed up despite the confusion, he also warns the audience about his strained vocal cords, quipping that his hoarseness is definitely "not from a late night at casino last night."

Stockton notes that because the reshuffled schedule offers a slightly longer window, he intends to deliver prepared remarks before opening the floor to audience questions. He explicitly sets the ground rules for the interactive portion, requesting that questions stay focused on his address regarding labor, the workforce, and technology, noting that any broader administrative or political inquiries can be directed to the Department of Labor’s dedicated communications team.

[01:10](https://www.youtube.com/watch?v=FnUJVYhvscA&t=70s) The Trump Administration's Worker-Centric AI Vision

Formally introducing himself as the Chief Innovation Officer at the United States Department of Labor (DOL), Stockton outlines his core mandate: spearheading how the agency interprets artificial intelligence’s influence on the broader labor market, assessing its impact on the daily experience of American workers, and modernizing departmental operations to better equip citizens for an evolving economic reality. AI is no longer a distant theoretical technology on the horizon; it has become an active, structural force transforming production, enterprise, and employment across every major domestic industry.

Stockton situates the Labor Department's efforts within the broader industrial and geopolitical policy of the Trump administration. Under President Donald Trump, the administration's stance is that winning the global race in artificial intelligence requires advancing both research development and domestic deployment so that productivity radically improves, economic opportunity widens, and American competitiveness is fortified on the world stage. Crucially, Stockton underlines the foundational tenet outlined in the administration's AI action plan released the prior month: the path to AI supremacy must be built by American workers and must place workers directly at the center to improve their livelihoods. The Department of Labor refuses to adopt a passive or reactive posture in the face of the fastest-moving labor shift in modern history, aiming instead to guide the American workforce system toward proactive, real-time agility.

[03:12](https://www.youtube.com/watch?v=FnUJVYhvscA&t=192s) Pillar 1: AI as a Frontier of Opportunity and Job Quality

Stockton lays out the first of four guiding pillars: artificial intelligence represents an unprecedented frontier of economic opportunity rather than an existential workforce catastrophe. While public discourse routinely fixates on disruption and catastrophic downside risks, the Department of Labor approaches the shift through the lens of upside potential. This opportunity manifests across three primary dimensions: the creation of brand-new job categories, the enrichment of existing occupations, and the physical modernization of workplace health and safety.

In the realm of job creation, fields that scarcely existed just a few years earlier—such as AI prompt engineers, synthetic data specialists, and AI governance analysts—are rapidly scaling across sectors. Stockton points out that many of these lucrative, high-paying positions do not require a traditional four-year college degree, unlocking viable pathways into the middle class and upward mobility for everyday workers. Meanwhile, for existing roles, AI serves as a capability multiplier. Small teams and individual workers can now execute tasks that formerly required entire corporate departments, fostering a resurgence in entrepreneurship and small-business agility.

```mermaid flowchart TD accTitle: Dimensions of AI Opportunity in the Workforce subgraph Opportunities["AI Expansion of Opportunity"] A["New Job Architectures Prompt Engineers & Governance Analysts"] B["Workforce Augmentation Human Capability Multipliers"] C["Workplace Safety Real-Time Hazard Detection"] end subgraph EconomicOutcomes["Tangible Outcomes"] D["Upward Mobility Without 4-Year Degrees"] E["High-Value Human Focus (Empathy & Strategy)"] F["Injury Prevention in Heavy Industry"] end A --> D B --> E C --> F ```

Beyond sheer scale, AI removes administrative drudgery, making work inherently more human. In clinical healthcare, automated tools that draft clinical notes, summarize patient records, and recommend treatment avenues liberate doctors to spend dedicated time on patient bedside interaction, empathy, and relationship building. On industrial worksites—including mining, heavy construction, and advanced manufacturing—AI-driven visual and sensory hardware identifies immediate environmental hazards, preventing fatal workplace accidents before they materialize. To support this wave of capability, Stockton references an April executive order signed by President Trump mandating AI literacy across America, which the DOL is operationalizing across all federal workforce training funding streams.

- Generative AI is projected by McKinsey estimates to inject up to $4.4 trillion annually into global productivity. - High-paying AI jobs (e.g., synthetic data specialists and prompt engineers) bypass traditional credentialing barriers to create non-degree mobility pathways. - Departmental education and workforce funding streams are being systematically aligned with the April executive order on universal domestic AI literacy.

[06:35](https://www.youtube.com/watch?v=FnUJVYhvscA&t=395s) Pillar 2: Debunking the Myth of Mass Technological Displacement

Stockton tackles the pervasive public anxiety regarding technological unemployment, asserting that predictions of mass job displacement are deeply overstated and divorced from empirical labor market realities. Historical transitions—from agricultural mechanization to factory automation and the rise of the commercial internet—consistently triggered alarmist warnings about permanent unemployment, yet each technological wave ultimately generated significantly more jobs than it made obsolete.

Current economic data confirms a similar pattern for artificial intelligence. Citing PwC findings, Stockton notes that occupations with the highest exposure to generative AI have seen a 38% increase in net job growth, propelled directly by the productivity gains those tools unlock. He breaks down three core structural reasons why AI transforms roles rather than eliminating them:

```mermaid flowchart TD accTitle: Human-in-the-Loop Workflow and Adoption Bottlenecks subgraph HumanWorkflow["The Operational Workflow"] A["Human Worker: Frame Goals, Context & Strategy"] --> B["AI Engine: Task Generation & Processing"] B --> C["Human Worker: Evaluate Output, Review & Decide"] end subgraph RealWorldBottlenecks["Societal & Operational Guardrails"] D["Customer Preferences for Human Interaction"] E["State & Federal Regulatory Oversight"] F["Multi-Year Organizational Restructuring"] end RealWorldBottlenecks -. Constrain Complete Automation.-> B ```

First, rather than cutting payroll to produce static output, companies consistently leverage AI to amplify output with existing headcount—generating more content, shipping products faster, and elevating customer differentiation. Second, autonomous systems cannot independently manage end-to-end organizational workflows; human expertise remains essential at the front end (defining goals and context) and the back end (evaluating accuracy and making authoritative judgments). Third, technological feasibility does not equal marketplace adoption. Real-world implementation faces steep bottlenecks: consumer demand for genuine human interaction (notably in education and healthcare), evolving legal frameworks mandating human oversight, and the multi-year friction of rewiring corporate operations. While acknowledging localized disruptions will occur, Stockton argues the focus must remain on transitions rather than sensationalized narratives of doom.

- PwC research indicates that employment in roles most exposed to generative AI expanded by 38%. - AI adoption is bounded by three non-technical friction points: client preference, regulatory mandates, and organizational restructuring timelines. - DOL's policy focus centers on building rapid transition pathways for impacted workers rather than attempting to artificially preserve stagnant roles.

[10:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=639s) Pillar 3: Building Systemic Agility to Match the Pace of Change

The third core idea centers on the structural responsiveness of public institutions: the single most critical capability the Department of Labor must cultivate is agility. Stockton asserts that the greatest threat facing the workforce in an AI-driven economy is not the aggregate volume of job destruction, but rather the sheer, unprecedented velocity of skill evolution. If public training apparatuses cannot recalibrate continuously to changing market conditions, workers and employers alike will be stranded by widening talent deficits.

To operationalize agility, Stockton points to specific initiatives within President Trump’s newly unveiled AI action plan. Central among these is the launch of the DOL’s AI Workforce Research Hub. This initiative is designed around three interlocking capabilities: real-time tracking of how AI tools alter occupational tasks, rapid translation of real-time market data into federal education and workforce policy, and agile testing and iterative refinement of regional pilot programs.

Simultaneously, the administration is focusing federal attention on securing the foundational blue-collar and technical talent necessary to power domestic AI infrastructure. The DOL is forging accelerated pipelines for electricians, electrical lineworkers, and data center technicians through coordinated industry partnerships and outcome-based funding. Although designed to address immediate bottlenecks in the compute buildout, Stockton characterizes this infrastructure talent pipeline as an operational blueprint for mitigating rapid-onset labor shortages across any future industrial sector.

[13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) Pillar 4: A Paradigm Shift Toward Apprenticeships and Accountability

Turning to his fourth pillar, Stockton calls for a clean break from legacy workforce development methods, decrying what he labels the outdated "train and pray" model. For decades, billions in public subsidies have funded training programs entirely divorced from employer hiring pipelines, leaving trainees with credentials that fail to produce sustainable careers. Stockton cites state-level data showing that a mere 34% of workforce training participants ever secure employment in the specific occupations they were funded to study. In an economy accelerating under AI, he insists that "hope is not a strategy."

To replace this broken paradigm, Stockton champions the findings of *America's Talent Strategy: Equipping American Workers for the Golden Age*, a joint policy report published the day prior by the Departments of Labor, Commerce, and Education. The centerpiece of this strategy is a nationwide revitalization of the registered apprenticeship model, which combines zero student debt with market-aligned, wage-earning experience. Stockton directly challenges higher-education dogmas, declaring that "the college-for-all movement has failed," pointing out that 52% of the college graduating class of 2023 was either unemployed or underemployed a full year after earning their bachelor's degrees.

To meet President Trump’s explicit mandate to surpass one million registered apprenticeships, the DOL is stripping out regulatory red tape, incentivizing business participation, and expanding the model into nontraditional sectors like advanced manufacturing, clinical healthcare, and high-tech infrastructure. Furthermore, the agency is attaching rigorous transparency standards and pay-for-performance requirements to federal workforce spending, ensuring only programs with verifiable employment outcomes receive taxpayer dollars.

[16:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=999s) Conclusion: Reframing the AI Narrative from Fear to Optimism

Stockton closes his address with a passionate call to reshape the national cultural and economic narrative surrounding artificial intelligence. While American youth are bombarded daily with dire headlines warning that automation and robotics will render their futures obsolete, Stockton argues the reality is precisely the opposite: the rising generation of American workers will be the ones designing, deploying, overseeing, and managing these advanced systems.

Surrendering to defeatist prophecies of technological decline would guarantee missing a generational window to expand the American middle class. Stockton reiterates the Department of Labor's pledge to lead this cultural shift through actionable reforms: unlocking high-wage opportunities, embedding AI literacy into every federal training pipeline, hardening structural workforce agility via dynamic research, and scaling debt-free, employer-aligned apprenticeships across the nation.

[17:57](https://www.youtube.com/watch?v=FnUJVYhvscA&t=1077s) Delivering on the Vision for American AI Leadership

In his closing remarks, Taylor Stockton emphasizes that the Department of Labor's strategic actions are designed to fulfill the administration's overarching vision of securing global American leadership in artificial intelligence. He stresses that the primary objective of these initiatives is to ensure that the economic benefits and emerging opportunities of the AI-driven economy are broadly accessible to every American worker, revitalizing upward mobility across the nation and ensuring that "pathways to the American dream" remain "more alive than ever before." He concludes the address to applause from the audience.

## Notable quotes > [00:04](https://www.youtube.com/watch?v=FnUJVYhvscA&t=4s) not from a late night at casino last night — Taylor Stockton

> [01:10](https://www.youtube.com/watch?v=FnUJVYhvscA&t=70s) the path to AI supremacy must be built by American workers and must place workers directly at the center to improve their livelihoods — Taylor Stockton

> [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) train and pray — Taylor Stockton

> [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) hope is not a strategy — Taylor Stockton

> [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) the college-for-all movement has failed — Taylor Stockton

> [17:57](https://www.youtube.com/watch?v=FnUJVYhvscA&t=1077s) pathways to the American dream — Taylor Stockton

> [17:57](https://www.youtube.com/watch?v=FnUJVYhvscA&t=1077s) more alive than ever before — Taylor Stockton

## Mentioned Taylor Stockton, Donald Trump, U.S. Department of Labor (DOL), U.S. Department of Commerce, U.S. Department of Education, AI4, McKinsey & Company, PwC, AI Workforce Research Hub, America's Talent Strategy: Equipping American Workers for the Golden Age

## Video description Taylor Stockton, Chief Innovation Officer, U.S. Department of Labor Abstrancts

Taylor Stockton, the Chief Innovation Officer of the U.S. Department of Labor, will speak about the DOL's perspective on how the rise of artificial intelligence will impact the labor market and the overall experience of the American worker.

The Trump administration has taken a bold stance on how it intends to make America the global leaders in the development of AI. By accelerating research and development, dismantling regulatory barriers, and strengthening domestic supply chains, the administration intends to usher in a new era of American innovation. For the Department of Labor, unlocking the power of AI represents a new frontier of opportunity for the American worker.

[Source](https://www.youtube.com/watch?v=FnUJVYhvscA)

中文简报

美劳工部驳斥AI就业末日论，推进100万个学徒岗位美国劳工部首席创新官 Taylor Stockton 认为 AI 会促进净就业增长，并主张从传统大学学位转向100万个无负债学徒岗位，以满足技术用工需求。 Taylor Stockton 概述了美国劳工部围绕四大指导支柱应对人工智能兴起的积极且以劳动者为中心的战略。他驳斥了有关技术性失业的危言耸听，列举证据表明受 AI 影响的岗位实现了净就业增长，并解释了为何人类监督与制度摩擦阻碍了端到端的全自主替代。Stockton 介绍了多项联邦敏捷举措，包括劳工部新设立的人工智能劳动力研究中心（AI Workforce Research Hub），以及为数据中心技术人员和线路工等实体基础设施工人加快构建人才输送通道。最后，他呼吁废除过时的“培训后听天由命”式教育模式，转而推行普及型 AI 素养教育，并扩大与可核实薪资成果挂钩、由雇主主导且无负债的注册学徒制。美国劳工部首席创新官 Taylor Stockton 指出，特朗普政府的 AI 战略将工人置于核心，劳工部正推动国家劳动力体系由被动应对转向实时敏捷转型。 (1:10) 劳工部强调 AI 正在创造提示词工程与合成数据等无需四年制大学学位的高薪岗位，相关联邦职业培训资金已全面对接总统关于普及全民 AI 素养的行政令。 (3:12) 大众对大规模技术性失业的担忧被严重夸大，普华永道数据显示受生成式 AI 影响最深岗位的就业净增 38%，全流程端到端自动化仍受制于人工审核与现实落地阻力。 (6:35) 劳工部正打破传统的脱节培训体系，全面推广与企业用人紧密绑定的零学贷注册学徒制，力争使注册学徒人数突破 100 万以填补 AI 基础设施等急缺工种。 (13:06)

Reading summary

Department of Labor Chief Innovation Officer Taylor Stockton argues that artificial intelligence represents an economic catalyst rather than a catalyst for mass unemployment, asserting that policy must prioritize rapid institutional agility, debt-free apprenticeships, and outcome-verified job training. Upside Potential and Non-Degree Pathways (3:12) Stockton frames artificial intelligence as a capability multiplier and job creator, pointing to McKinsey projections that generative AI could inject up to $4.4 trillion annually into global productivity. He emphasizes that newly emerging roles—such as prompt engineers, synthetic data specialists, and AI governance analysts—frequently do not require four-year college degrees, creating viable pathways to middle-class mobility. Additionally, he highlights physical workplace safety applications where sensory and visual AI detects imminent hazards in heavy industries such as mining and manufacturing, backed by federal alignment with an April executive order mandating universal domestic AI literacy. Countering Technological Displacement Narratives (6:35) Addressing public anxieties regarding technological unemployment, Stockton characterizes warnings of systemic job destruction as historically inconsistent and contradicted by current labor data. Citing findings from PwC, he notes that occupations with the highest generative AI exposure recorded a 38% increase in net job growth, as firms use efficiency gains to expand total output rather than reduce headcount. Stockton further argues that total automation is constrained by real-world friction, including consumer preference for human care in sectors like health and education, emerging regulatory oversight, and lengthy corporate restructuring cycles, making worker transition rather than job loss the central policy issue. Institutional Agility and Infrastructure Pipelines (10:39) Stockton contends that the most critical risk facing the labor market is not the sheer volume of lost positions, but the unprecedented velocity of skill evolution outrunning public training institutions. To track dynamic task changes, the Department of Labor is deploying an AI Workforce Research Hub to monitor occupational shifts in real time and translate empirical findings into federal workforce policy and regional pilot programs. Concurrently, the administration is prioritizing physical infrastructure talent by establishing accelerated pipelines for data center technicians, electricians, and electrical lineworkers, treating the AI compute buildout as an operational blueprint for handling rapid-onset labor shortages. Apprenticeships and Pay-for-Performance Reform (13:06) Rejecting legacy workforce development models, Stockton criticizes traditional training approaches where state-level data indicates only 34% of subsidized participants secure employment in their targeted field. Highlighting the joint report 'America's Talent Strategy,' he challenges standard university credentials by noting that 52% of the 2023 bachelor's degree graduating class was unemployed or underemployed a year after graduation. In place of open-ended grant spending, the administration is targeting over one million registered apprenticeships, cutting administrative rules to scale debt-free models across healthcare and high-tech sectors while tying federal funds directly to verifiable job placements. Stockton concludes that the rising generation of American workers will manage and operate AI systems rather than be displaced by them, provided public systems modernize in time. The overarching strategy depends on whether federal agencies can successfully pivot funding toward accountable, employer-led training and sustain rapid structural responsiveness across the national workforce.

精炼阅读摘要

美国劳工部（DOL）首席创新官Taylor Stockton阐述了特朗普政府以工人为核心的人工智能战略，主张AI将带来经济机遇并重构工作模式而非引发灾难性大规模失业，并呼吁公共部门以政策敏捷机制与免除学贷的登记学徒制全面重塑劳动力培养体系。经济红利与非学历门槛的职业机遇 (3:12) Stockton提出劳工部应对AI转型的首要支柱是将技术变革视作拓展经济机遇与提升工作质量的前沿，而非灾难性冲击。他援引麦肯锡（McKinsey）的研究预测指出，生成式AI预计每年将为全球生产力注入高达4.4万亿美元。这一增长直接体现在新兴职业架构的涌现，诸如提示词工程师、合成数据专家及AI治理分析师等岗位正跨行业快速扩展。关键在于，许多高薪新兴职位并不强制要求传统的四年制大学文凭，从而为普通工人和非传统教育背景劳动力开辟了进入中产阶层的跃升通道。在增强现有岗位能力方面，AI通过剥离繁琐行政事务使劳动回归人性化。在医疗临床中，自动化系统承担病历起草与数据摘要工作，使医生得以投入更多精力在病床前与患者沟通关怀；在采矿、重型建筑与先进制造等高危现场，AI视觉与传感硬件可实时捕捉环境隐患，降低严重工伤事故风险。劳工部目前正系统性推进落实特朗普签署的4月总统行政命令，将普及全美AI素养的要求嵌入所有联邦劳动力培训资金流中。破除大规模失业论与人机协同瓶颈 (6:35) 针对公众对技术性失业的焦虑，Stockton认为全面岗位替代的预测被严重夸大，脱离了劳动力市场的客观规律。历史上从农业机械化到商业互联网普及的历次转型，最终创造的就业岗位均远多于其淘汰的岗位。他援引普华永道（PwC）的数据指出，当前与生成式AI接触度最高的职业净就业反而增长了38%，这直接得益于AI工具所激发的生产力扩张。企业在实际运作中普遍倾向于维持现有雇员规模并借助AI成倍提升业务产出，而非单纯裁减人手换取固定产能。 Stockton进一步归纳了制约完全自动化的三大现实瓶颈。首先，自主系统无法独立完成端到端业务闭环，人类在工作流前端定义战略目标与背景、在后端进行准确性审核与权威决策的“人机协同”（Human-in-the-Loop）不可或缺。其次，技术落地面临三大非技术性摩擦：客户在教育和医疗等领域对真实人际交互的明确偏好、不断严化的法律与监管审查要求，以及企业重构组织架构所必需的多年度周期。劳工部的政策重心在于构建敏捷过渡机制，而非盲目维持停滞工种。政策敏捷机制与底层基建人才管网 (10:39) Stockton强调，在AI经济转型期，劳工体系面临的核心挑战并非岗位净流失总量，而是技能演进的空前速度。若公共培训体系无法根据市场变化快速自我调整，雇工与企业都将陷入人才结构性短缺。为此，劳工部依据特朗普政府新公布的AI行动计划设立了“AI劳动力研究中心”（AI Workforce Research Hub），该机构围绕三大能力运行：动态追踪AI对各职业任务的实时改变、快速将市场数据转化为联邦政策指引，以及在区域性试点项目中进行敏捷测试与迭代优化。与此同时，联邦政府高度关注支撑国内AI算力基础设施的蓝领及工程技术人才。劳工部正通过跨部门协作与按绩效付费机制，加速培养电工、高压线路工及数据中心技术人员。Stockton指出，该基建人才管线不仅旨在破除当前算力扩张的人力瓶颈，更构成了应对未来各类工业部门突发性劳动力短缺的标准操作蓝图。摆脱传统模式并扩大登记学徒制 (13:06) Stockton呼吁彻底打破以往“培训后祈祷”（train and pray）的传统劳动力开发模式。他指出，长期以来数十亿美元的公共资金流向脱离雇主实际需求的培训项目，州级数据显示仅有约34%的受训人员最终能进入所学专业对应的岗位。同时他直言“全民上大学运动已经失败”，援引数据指出2023届大学毕业生在毕业一年后仍有52%处于失业或不充分就业状态，高昂的大学学债未能保障稳定的职业生涯。为替代这一失效体系，劳工部联合商务部与教育部发布《美国人才战略：为黄金时代装备美国工人》报告，将“登记学徒制”（registered apprenticeship）列为核心替代方案。该模式使学徒在免除学贷压力的同时获取由市场需求驱动的有薪在职技能。劳工部正着手精简监管审批，将该模式拓展至先进制造、医疗与高科技基建领域，以实现特朗普制定的使在册登记学徒人数突破100万的目标；此外，联邦拨款正全面推行绩效挂钩机制，确保纳税人资金仅投向具备可验证就业成果的项目。 Stockton总结呼吁扭转针对AI的技术悲观主义叙事，主张年轻一代工人应被视为前沿系统的设计者、管理者与受益者，而非被时代抛弃的对象。他重申劳工部将通过非学历晋升路径、AI素养普及、敏捷数据跟踪与学徒制扩张保障工人利益；然而，该规划能否达成预期，仍取决于其能否有效打破官僚冗余、顺利兑现超百万学徒目标，并确保公共培训机制紧跟极速迭代的技术步伐。

Detailed notes — zh

## Background 该视频由行业会议频道 Ai4 于 2025 年 8 月 28 日发布在 YouTube 平台上。内容聚焦美国劳工部首席创新官 Taylor Stockton 的主题演讲，介绍特朗普政府如何通过推动研发、破除监管壁垒及强化本土供应链来确立全球 AI 领导地位，并阐述劳工部如何将 AI 转化为赋能美国劳动者的全新发展机遇。 ## TL;DR Taylor Stockton 在演讲中系统阐述了美国劳工部在 AI 时代的四大核心治理理念，驳斥了“技术将引发大规模失业”的过度恐慌。他指出，AI 正在催生高薪新兴工种、成倍放大生产力并改善高危行业安全，劳工部已将普及全民 AI 素养列为优先战略。针对技术迭代过快的挑战，劳工部正加速组建 AI 劳动力研究中心，并全力破解 AI 基础设施领域的技术工人缺口。此外，政府正打破对四年制大学学位的盲目依赖，全面摒弃“培训后祈祷就业”的脱节模式，通过推动百万注册学徒工计划与结果导向资助，确保广大普通劳动者真正共享 AI 时代的经济繁荣。 ## Atmosphere & style 演讲开场带有即兴与幽默色彩，演讲者风趣自嘲因主办方临时调程险些误场的突发状况，迅速拉近了与台下听众的距离。随后，整体语调迅速转入严谨自信、务实从容的政策阐述，既有宏观国家战略的高度，又有扎实数据与制度细节的支撑。整场演讲的情绪曲线从开场的轻松诙谐平稳过渡到充满使命感的发展叙事，将公众对被机器替代的普遍焦虑，转化为对技术赋能劳动者与重塑“美国梦”的坚定乐观信念。 ## Key takeaways - [01:40](https://www.youtube.com/watch?v=FnUJVYhvscA&t=100s) 美国劳工部的核心使命是以“劳动者为中心”推进 AI 变革，确保美国在全球 AI 竞赛中领先的同时从根本上改善劳动者生计。 - [03:20](https://www.youtube.com/watch?v=FnUJVYhvscA&t=200s) AI 正在催生提示词工程师、合成数据专家等高薪新兴岗位，且大多无需四年制本科学位，为社会阶层流动开辟了新通道。 - [03:20](https://www.youtube.com/watch?v=FnUJVYhvscA&t=200s) 生成式 AI 不仅大幅放大个人与小团队的生产力，还能通过自动化繁琐流程，让医护等专业人员更专注于人际信任与共情沟通。 - [03:20](https://www.youtube.com/watch?v=FnUJVYhvscA&t=200s) 劳工部正落实总统行政令，在所有教育与劳动力资金渠道中优先普及全民 AI 素养（AI Literacy）。 - [06:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=399s) 公众对 AI 导致大规模失业的恐慌被严重夸大；普华永道数据显示暴露于 AI 影响下的岗位反而实现了 38% 的增长。 - [06:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=399s) AI 难以完全取代人力的原因在于多数复杂流程需要“人类在回路（Human-in-the-loop）”，且受到监管合规与人际情感偏好等非技术因素制约。 - [10:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=639s) AI 时代劳动力体系面临的最大挑战是“变革的速度”，劳工部正筹建“AI 劳动力研究中心”以实现政策制定与技能培训的敏捷反应。 - [10:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=639s) 针对电工、线路工和数据中心技术员等关键 AI 基础设施人才短缺，劳工部正联合产业界建立定向培训与输送管道。 - [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) 传统“培训后祈祷模式”导致技能与市场严重脱节；2023 届大学毕业生毕业一年后有 52% 处于失业或不充分就业状态。 - [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) 劳工部大力推动学徒制范式转移，目标使注册学徒突破 100 万人，并推行按绩效付费模式以强化就业结果导向问责。 - [16:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=999s) 社会叙事应从“机器人抢夺饭碗”的恐惧转向乐观机遇，赋能下一代美国劳动者亲手制造、部署和管理 AI 与机器人系统。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=FnUJVYhvscA&t=4s) 开场趣事与美国劳工部创新使命演讲伊始，美国劳工部首席创新官（Chief Innovation Officer）Taylor Stockton 以风趣幽默的道歉拉开序幕。他透露自己原以为这场演讲被安排在下午 3 点，结果在上午 11 点 29 分接到 Ai4 团队的紧急电话询问“你在哪儿？”，当时他还在酒店房间，对方告知“你的演讲 60 秒后开始”。主办方随后对时间进行了调整，Stockton 向受到时间变动影响的观众致歉，并开玩笑解释自己的嗓音有些沙哑绝非“昨晚在赌场熬夜所致”。理清日程后，Stockton 明确了自己在劳工部的核心职责：主导劳工部对人工智能如何影响劳动力市场和劳动者体验的研判，探索现代化与创新路径，帮助美国劳动者在 AI 时代更好地驾驭未来的工作变革。在提问环节设定上，他表示非常期待交流，但会专注于本次演讲的主题，其他更广泛的部门政策问题可转由专门的公关团队对接。 ### [01:40](https://www.youtube.com/watch?v=FnUJVYhvscA&t=100s) AI 浪潮下的国家战略与“以劳动者为中心”的核心原则 Stockton 强调，人工智能已不再是地平线上的新兴技术，而是正在深刻重塑经济、劳动力市场以及各行各业工作模式的现实驱动力。在特朗普政府的领导下，美国不仅致力于成为全球 AI 开发的领跑者，更注重通过 AI 的落地应用从根本上提升生产力、扩大经济机遇并增强国家竞争力。白宫明确指出，实现 AI 领导地位的关键路径在于美国本土的自主创新，而这一切必须由美国劳动者亲手筑就。在上个月公布的 AI 行动计划中，首要原则就是“必须以把劳动者放在核心位置、从根本上改善其生计的方式赢得全球 AI 竞赛”。面对现代历史上最快速的劳动形态重塑，劳工部拒绝处于被动反应的地位，而是主动出击，确保 AI 带来广泛繁荣，并围绕四大核心理念全面推动劳动力体系的敏捷变革。 ### [03:20](https://www.youtube.com/watch?v=FnUJVYhvscA&t=200s) 理念一：AI 是赋能劳动者的全新机遇前沿当前关于 AI 与劳动力的公共讨论往往被“风险与颠覆”的悲观叙事所主导，但劳工部的出发点是“可能性与机遇”。Stockton 指出，AI 正在从创造新岗位、重构现有工作方式以及改善工作环境三个维度释放红利： ```mermaid flowchart TD accTitle: AI 赋能劳动者的三大维度 subgraph S1["1. 催生全新岗位"] A["高薪新兴职业 (Prompt 工程师 / 合成数据专家)"] B["打破四年制大学门槛 (促进阶层跃升)"] end subgraph S2["2. 重塑现有工作"] C["放大个人与小团队产能 (麦肯锡预估年增 4.4 万亿美元)"] D["使工作更具人情味 (自动化繁琐任务，释放共情与信任)"] end subgraph S3["3. 改善工作场所安全"] E["实时传感器识别隐患 (采矿、建筑、制造业事故预防)"] end S1 --> S2 --> S3 ``` - **催生高薪全新工种**：如 AI 提示词工程师（Prompt Engineer）、合成数据专家（Synthetic Data Specialist）、AI 治理分析师（AI Governance Analyst）等新兴职业快速涌现。这些岗位报酬优厚，且多数无需四年制本科学位，为扩大中产阶层和促进社会阶层向上流动开辟了新通道。 - **放大个体能力与释放人情味**：麦肯锡（McKinsey）数据显示生成式 AI 每年可为全球生产力贡献高达 4.4 万亿美元。个体与小团队能完成过去整个部门才能处理的工作，加速了小企业创业。同时，AI 处理临床病历、分析病史等繁琐事务，让医护人员等专业人士能专注于“建立信任、展现共情与人际互动”等以人为本的核心工作。 - **保障高危行业现场安全**：在采矿、建筑和制造业等高危领域，搭载 AI 的传感器能够实时识别安全隐患，在事故与伤亡发生前进行预警与干预。 - **全民 AI 素养计划**：为确保全员享受技术红利，劳工部正落实总统 4 月签署的行政令，在所有教育与劳动力资金渠道中优先普及 AI 素养（AI Literacy）。 ### [06:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=399s) 理念二：大规模失业恐慌被严重夸大针对公众最常提出的疑问“AI 是否会直接取代我们？”，劳工部明确指出，对大规模失业的恐慌在很大程度上被夸大了，与劳动力市场的实际走向并不相符。从农业机械化、工业自动化到互联网普及，历史上的历次技术变革最终创造的就业岗位都远多于其淘汰的岗位。普华永道（PwC）的数据表明，自生成式 AI 问世以来，表面上暴露在 AI 影响下的岗位反而增长了 38%，其核心在于生产力红利带动了岗位本身的扩容。AI 重塑工作而非替代工作的深层逻辑包括： | 维度 | 常见误解 | 实际运行规律 | |:--- |:--- |:--- | | **生产力与用工需求** | 企业生产力提升后会裁员，用更少的人维持原产出。 | 企业倾向于维持用工规模，借助 AI 制造更多内容、更快上线新产品并提供更优质的服务以击败竞争对手。 | | **工作流完整性** | AI 能独立端到端完成所有复杂业务流程。 | 多数场景必须保持“人类在回路（Human-in-the-loop）”，前端需要人类设定目标与背景，后端需要人类评估产出并做最终决策。 | | **技术落地瓶颈** | 只要技术可行，AI 就会立刻在市场上取代人力。 | 实际阻碍在于非技术因素：客户对人际交往的情感偏好（如医疗/教育）、合规与监管对人工审查的强制要求，以及企业重组流程所需的漫长时间。 | Stockton 强调，劳工部的目标不是去阻挠劳动力市场的正常演变或局部岗位置换，而是建立完备的制度护栏，帮助受影响的劳动者顺利转轨至更有前景的高质量就业机会中。 ### [10:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=639s) 理念三：构建劳动力体系的“敏捷性”核心肌肉 Stockton 认为，AI 时代劳动力面临的最大挑战可能并不是岗位流失，而是“变革发生的速度”。人才与技能需求瞬息万变，如果劳动力体系无法实现实时动态调整，就会面临脱节与落后的风险。为此，劳工部在落实总统 AI 行动计划时，着重强化了应对快速变革的反应能力。首先是筹建“AI 劳动力研究中心”（AI Workforce Research Hub），实时追踪 AI 对岗位的改变、将数据洞察快速转化为教育与用工政策，并敏捷测试验证新型创新试点项目。其次是针对关键的“AI 基础设施岗位”搭建人才输送管道。面对电工（Electricians）、线路工（Linesmen）以及数据中心技术员（Data Center Technicians）的紧缺，劳工部正联合产业界建立技能识别、定向培训与成果资助机制，这一模式也将成为未来应对全经济领域人才短缺的标准化蓝图。 ### [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) 理念四：告别“培训后祈祷”，掀起以学徒制为核心的范式转移传统劳动力发展模式存在长期的结构性脱节。在部分州，政府花钱资助的培训项目中只有 34% 的学员最终进入了对口行业。Stockton 将这种脱离雇主实际需求的陈旧做法斥为“培训后祈祷模式”（Train and Prey Model）——即培训机构闭门造车，学员结业后只能盲目祈祷能找到好工作，而在剧烈变革的时代，“希望绝不是一种策略”。为此，劳工部联合商务部和教育部发布了重磅国家战略报告《美国人才战略：为黄金时代装备美国劳动者》（America's Talent Strategy: Equipping American Workers for the Golden Age），推动全面范式转移： - **反思“全民大学”运动**：数据显示，2023 届大学毕业生在毕业一年后有 52% 处于失业或不充分就业状态，四年制学位已不再是进入专业职场的铁饭碗。 - **突破百万注册学徒工大关**：学徒制将带薪在岗实践与定向技能培训结合，是一种高效、免除学生债务的替代路径。总统已向劳工部下达明确指令，要求注册学徒人数必须突破并超越 100 万人，重点覆盖先进制造、医疗健康及 AI 基础设施领域。 - **制度松绑与结果导向问责**：精简繁文缛节，提高雇主参与收益，推动学徒制向新兴现代行业灵活延展；同时终结过去数十亿美元培训资金缺乏成效评估的弊端，推行按绩效付费模式（Pay for Performance），全面公开就业去向数据。 ### [16:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=999s) 总结：从恐惧走向乐观，赋能下一代劳动者在演讲的最后，Stockton 呼吁全社会完成对 AI 与劳动力关系的叙事转变。虽然当下的年轻一代每天都被“机器人将夺走饭碗”的耸人听闻标题所包围，但未来的真实图景是：下一代美国劳动者将拥有亲手制造、部署和管理这些机器人与 AI 系统的历史性机遇。如果社会陷入岗位流失与衰退的悲观叙事，就会错失扩大中产阶级、培育未来高薪人才的窗口期。美国劳工部将坚定致力于站在这一转变的最前沿，通过拓展 AI 新机遇、普及 AI 素养、以敏捷科研应对变革，并以高质量学徒制深化技能对接，全面筑牢劳动者在 AI 黄金时代的繁荣基石。 ### [17:57](https://www.youtube.com/watch?v=FnUJVYhvscA&t=1077s) 落实总统愿景与开启美国梦的新机遇 Taylor Stockton 在演讲结尾对美国劳工部（DOL）在人工智能时代的使命与行动进行了总结与展望。他强调，劳工部所推进的这一系列举措，旨在全面贯彻落实总统关于确立“美国在人工智能领域全球领导地位”的宏伟愿景。其核心目标不仅在于推动技术本身的创新与突破，更在于确保每一位普通美国劳工都能切身享受到 AI 经济蓬勃发展所带来的红利与机遇。通过消除制度障碍、加速技能转型以及打造包容性的发展环境，劳工部致力于为全美劳动者开辟通往繁荣的新通道。正如 Stockton 所展望的那样，这些行动将“让通往美国梦的路径比以往任何时候都更加充满生机”（"make pathways to the American dream more alive than ever before"）。他在全场热烈的掌声中结束了本次演讲。 - **核心愿景**：践行总统战略，确立并巩固美国在全球 AI 发展中的领军地位。 - **劳工赋能**：确保 AI 经济不仅服务于顶尖科技创新，更能让全体美国人共享技术红利。 - **最终使命**：重塑与拓宽通向“美国梦”的职业发展路径，赋能全美劳动力。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=FnUJVYhvscA&t=4s) 你的演讲 60 秒后开始 — Ai4 团队 > [00:04](https://www.youtube.com/watch?v=FnUJVYhvscA&t=4s) 主导劳工部对人工智能如何影响劳动力市场和劳动者体验的研判，探索现代化与创新路径，帮助美国劳动者在 AI 时代更好地驾驭未来的工作变革 — Taylor Stockton > [01:40](https://www.youtube.com/watch?v=FnUJVYhvscA&t=100s) 必须以把劳动者放在核心位置、从根本上改善其生计的方式赢得全球 AI 竞赛 — Taylor Stockton > [03:20](https://www.youtube.com/watch?v=FnUJVYhvscA&t=200s) 高薪新兴职业如 AI 提示词工程师、合成数据专家、AI 治理分析师等新兴职业快速涌现，多数无需四年制本科学位 — Taylor Stockton > [03:20](https://www.youtube.com/watch?v=FnUJVYhvscA&t=200s) 在所有教育与劳动力资金渠道中优先普及 AI 素养 — Taylor Stockton > [06:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=399s) 自生成式 AI 问世以来，表面上暴露在 AI 影响下的岗位反而增长了 38%，其核心在于生产力红利带动了岗位本身的扩容 — Taylor Stockton > [10:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=639s) AI 时代劳动力面临的最大挑战可能并不是岗位流失，而是变革发生的速度 — Taylor Stockton > [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) 培训后祈祷模式——即培训机构闭门造车，学员结业后只能盲目祈祷能找到好工作，而在剧烈变革的时代，希望绝不是一种策略 — Taylor Stockton > [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) 2023 届大学毕业生在毕业一年后有 52% 处于失业或不充分就业状态，四年制学位已不再是进入专业职场的铁饭碗 — Taylor Stockton > [13:06](https://www.youtube.com/watch?v=FnUJVYhvscA&t=786s) 注册学徒人数必须突破并超越 100 万人，重点覆盖先进制造、医疗健康及 AI 基础设施领域 — Taylor Stockton > [16:39](https://www.youtube.com/watch?v=FnUJVYhvscA&t=999s) 下一代美国劳动者将拥有亲手制造、部署和管理这些机器人与 AI 系统的历史性机遇 — Taylor Stockton > [17:57](https://www.youtube.com/watch?v=FnUJVYhvscA&t=1077s) 让通往美国梦的路径比以往任何时候都更加充满生机 — Taylor Stockton ## Mentioned Taylor Stockton, 美国劳工部 (U.S. Department of Labor), Ai4, 特朗普政府 (Trump administration), 麦肯锡 (McKinsey), 普华永道 (PwC), 美国商务部 (U.S. Department of Commerce), 美国教育部 (U.S. Department of Education), 《美国人才战略：为黄金时代装备美国劳动者》 (America's Talent Strategy: Equipping American Workers for the Golden Age), AI 劳动力研究中心 (AI Workforce Research Hub) ## Video description Taylor Stockton, Chief Innovation Officer, U.S. Department of Labor Abstrancts Taylor Stockton, the Chief Innovation Officer of the U.S. Department of Labor, will speak about the DOL's perspective on how the rise of artificial intelligence will impact the labor market and the overall experience of the American worker. The Trump administration has taken a bold stance on how it intends to make America the global leaders in the development of AI. By accelerating research and development, dismantling regulatory barriers, and strengthening domestic supply chains, the administration intends to usher in a new era of American innovation. For the Department of Labor, unlocking the power of AI represents a new frontier of opportunity for the American worker. [Source](https://www.youtube.com/watch?v=FnUJVYhvscA)

Source quotes

[0:04] Ai4 团队: 你的演讲 60 秒后开始 [0:04] Taylor Stockton: 主导劳工部对人工智能如何影响劳动力市场和劳动者体验的研判，探索现代化与创新路径，帮助美国劳动者在 AI 时代更好地驾驭未来的工作变革 [1:40] Taylor Stockton: 必须以把劳动者放在核心位置、从根本上改善其生计的方式赢得全球 AI 竞赛 [3:20] Taylor Stockton: 高薪新兴职业如 AI 提示词工程师、合成数据专家、AI 治理分析师等新兴职业快速涌现，多数无需四年制本科学位 [3:20] Taylor Stockton: 在所有教育与劳动力资金渠道中优先普及 AI 素养 [6:39] Taylor Stockton: 自生成式 AI 问世以来，表面上暴露在 AI 影响下的岗位反而增长了 38%，其核心在于生产力红利带动了岗位本身的扩容 [10:39] Taylor Stockton: AI 时代劳动力面临的最大挑战可能并不是岗位流失，而是变革发生的速度 [13:06] Taylor Stockton: 培训后祈祷模式——即培训机构闭门造车，学员结业后只能盲目祈祷能找到好工作，而在剧烈变革的时代，希望绝不是一种策略 [13:06] Taylor Stockton: 2023 届大学毕业生在毕业一年后有 52% 处于失业或不充分就业状态，四年制学位已不再是进入专业职场的铁饭碗 [13:06] Taylor Stockton: 注册学徒人数必须突破并超越 100 万人，重点覆盖先进制造、医疗健康及 AI 基础设施领域 [16:39] Taylor Stockton: 下一代美国劳动者将拥有亲手制造、部署和管理这些机器人与 AI 系统的历史性机遇 [17:57] Taylor Stockton: 让通往美国梦的路径比以往任何时候都更加充满生机

Source quotes

[0:04] Taylor Stockton: not from a late night at casino last night [1:10] Taylor Stockton: the path to AI supremacy must be built by American workers and must place workers directly at the center to improve their livelihoods [13:06] Taylor Stockton: train and pray [13:06] Taylor Stockton: hope is not a strategy [13:06] Taylor Stockton: the college-for-all movement has failed [17:57] Taylor Stockton: pathways to the American dream [17:57] Taylor Stockton: more alive than ever before

Original transcript

[0:04] Thank you so much. And and I'll start [0:05] off with maybe a couple apologies. Uh [0:08] one, I thought this session was at 3 [0:10] p.m. today. Uh I got a call from the the [0:13] lovely AI4 team at 11:29 saying, "Where [0:16] are you?" And I said, "I'm in my hotel [0:18] room." And they said, "Your session's [0:20] starting in 60 seconds." Uh and so [0:22] apologies for anybody who was affected [0:23] by that. We did switch it uh to this [0:26] time. And so glad that that a bunch of [0:28] you still came. Uh, second apology I'll [0:30] give is that I am having a little bit of [0:32] vocal cord issues which I promise are [0:34] not from a late night at casino last [0:36] night and so uh bear with me as uh as I [0:39] get through this. Um, I'll also say the [0:42] time slot that we have now is a little [0:43] bit longer than originally planned and [0:45] so I do have some prepared remarks that [0:47] I'm going to give on this really [0:49] exciting topic. Uh, but we should have a [0:51] good amount of time for questions. Uh, [0:53] which is always great. I will say uh I [0:55] will ask answer questions that are on [0:58] the topic of of the speech at hand. I [1:00] know there's always interest in a [1:02] broader set of questions and I can [1:04] always uh connect you to the right [1:06] communications team for for other [1:08] questions. Uh so with that in mind uh [1:10] like Britney said, my name is Taylor [1:12] Stockton. I'm the chief innovation [1:14] officer at the US Department of Labor [1:16] and really my mandate is to lead the [1:18] agency's view on how AI is impacting the [1:22] labor market, the overall experience of [1:23] workers and what are the new [1:26] possibilities, what are the new ways [1:27] that we can innovate and modernize our [1:29] work to better support workers in in [1:31] navigating the future of work in this in [1:33] this AI reality. And so, uh, excited to [1:36] speak today about AI's impact on the [1:38] American worker. [1:40] AI, sorry, excuse me. AI is no longer an [1:43] emerging technology on the horizon. It [1:45] is an active force that is transforming [1:47] the economy, the labor market, and how [1:50] work is done across every industry. [1:52] Under President Trump's leadership, the [1:54] United States is taking bold action to [1:56] ensure that we are not only the glo [1:58] global leader in developing AI, but also [2:01] on applying AI in a way that radically [2:04] improves productivity, expands economic [2:07] opportunity, and strengthens American [2:09] competitiveness. This administration has [2:11] taken a clear and decisive stance that [2:14] the path to AI leadership runs through [2:17] American innovation and it will be built [2:20] by American workers. The Trump [2:22] administration believes that it's not [2:24] just about whether we win the global AI [2:26] race. It's also about how we win it. And [2:29] the very first principle that the [2:31] administration shared in its AI action [2:33] plan last month is that we must win it [2:35] in a way that puts workers at the center [2:38] and fundamentally improves their [2:40] livelihood. [2:41] At the Department of Labor, that [2:43] principle defines our mission. We are [2:45] charting a new path for our agency at a [2:48] time when the nature of labor itself is [2:51] shifting more rapidly than in any point [2:53] in modern history. As AI transforms work [2:56] across every sector of the economy, the [2:58] Department of Labor's role cannot be [3:00] reactive. We are taking bold, proactive [3:03] steps to ensure that AI drives [3:05] widespread prosperity and that the [3:08] American workforce system is more agile [3:10] than ever before. So today, I want to [3:12] share four core ideas guiding how the [3:15] Department of Labor is navigating the re [3:18] rise of AI in the workforce. First, we [3:20] believe that AI represents a powerful [3:22] new frontier of opportunity for American [3:25] workers. Too often in these [3:26] conversations about AI and the [3:29] workforce, they start with a focus on [3:31] risk and disruption. But at the [3:33] Department of Labor, we start with [3:35] possibility. AI is already expanding [3:38] opportunity in a few different ways. By [3:40] creating new jobs, reshaping how [3:43] existing jobs are performed, and [3:45] transforming the workplace itself. When [3:48] it comes to new jobs, we're seeing [3:49] entire new categories of work emerge [3:52] that barely existed just a few years [3:53] ago. Roles like AI prompt engineer, [3:56] synthetic data specialist, and AI [3:59] governance analyst are growing rapidly [4:01] across many sectors. Not only are these [4:04] jobs highpaying, but many of them also [4:06] do not require a four-year degree. They [4:09] present a major opportunity to grow the [4:11] middle class and unlock new pathways to [4:14] upper mobility for American workers. But [4:16] AI isn't only creating new jobs. It's [4:19] also fundamentally reshaping how [4:21] millions of existing jobs are performed, [4:24] often in ways that make workers more [4:26] productive and their work more [4:28] meaningful. Across industries, AI is [4:31] amplifying human capability. It allows [4:34] individuals in small teams to accomplish [4:37] what once required entire departments, [4:40] which unlocks new possibilities for [4:41] entrepreneurship and small business [4:43] ownership. According to McKenzie [4:46] estimates, generative AI could add up to [4:48] $4.4 trillion in global productivity [4:51] each year. That higher productivity can [4:54] translate into higher wages for workers, [4:57] greater competitiveness for small [4:59] businesses, and innovation inside [5:01] companies of all sizes. [5:04] Equally important, AI is making many [5:06] jobs more human. By automating [5:09] repetitive and time-consuming tasks, it [5:11] frees workers to focus on aspects of [5:13] their work that require trust building, [5:16] empathy, and interpersonal interactions. [5:19] In healthcare, for example, AI can [5:21] generate clinical notes, analyze patient [5:24] histories, and suggest treatment [5:25] options, allowing doctors to spend more [5:28] time on what they can do best of [5:31] directly interacting with their [5:33] patients. Finally, AI is transforming [5:36] the workplace itself by improving health [5:38] and safety conditions, especially in [5:40] high-risk industries like mining, [5:42] construction, and manufacturing. We're [5:45] already seeing ways that AI powered [5:46] sensors can identify hazards in real [5:49] time and prevent accidents and deaths [5:52] before they occur. These innovations, [5:55] from new career paths to enhance [5:57] productivity to safer workplaces, show [6:00] the extraordinary upside of AI for [6:02] workers. [6:04] But to fully realize these benefits, we [6:07] recognize that workers must have the [6:09] foundational skills to engage with AI. [6:13] That's why the president signed an [6:14] executive order in April to bring AI [6:16] literacy to all Americans. The [6:18] Department of Labor is advancing this [6:20] vision by prioritizing AI literacy [6:22] across all education and workforce [6:25] funding streams [6:27] so that all workers have access to the [6:29] skills that they need to thrive in an [6:31] AIdriven economy. But that still leaves [6:35] us one of the questions we get most [6:36] often. Isn't AI just going to replace [6:39] us? Our second core idea today is that [6:43] the fear of mass job displacement is [6:46] deeply overstated and does not match the [6:49] reality that we see playing out in the [6:51] labor market. History gives us a clear [6:54] reason to be skeptical of these doomsday [6:57] predictions. Each major wave of [6:59] technological change from the [7:01] mechanization of agriculture to [7:03] industrial automation to the arrival of [7:05] the internet despite fears to the [7:08] contrary has ended up creating more jobs [7:11] than it has displaced. Yes, AI is [7:15] different. But we're already seeing [7:18] similar dynamics with AI. According to [7:21] PWC, since the introduction of [7:23] generative AI, jobs that on paper are [7:26] more exposed to AI have actually grown [7:30] by 38%. Driven in part by AI's [7:33] productivity benefits in those very same [7:35] roles. We're seeing that it is far more [7:38] likely for AI to transform jobs than [7:41] replace them. And there are several [7:43] reasons why. First, many people assume [7:46] that if AI makes workers more [7:48] productive, companies will simply want [7:51] fewer workers to produce the same amount [7:53] of output. But in practice, what we're [7:56] actually seeing is the opposite. [7:58] Companies are using AI to help the same [8:01] amount of workers have increased output [8:05] with the same amount of workers being [8:07] able to create, for example, more [8:09] content, the same amount of workers [8:11] being able to launch new products faster [8:13] or the same amount of workers being able [8:15] to serve their customers better to [8:18] better differentiate from their [8:19] competitors. [8:21] Second, AI is rarely able to run an [8:24] entire workflow from start to finish. No [8:27] matter how strong AI might be at [8:29] individual tasks or even a set of tasks, [8:32] what we are seeing is that humans are [8:34] almost always needed at two key points. [8:37] At the beginning to set goals and [8:39] context for the AI and at the end to [8:43] evaluate the AI's output and make [8:45] decisions based off of it. For many [8:47] occupations, continuing to have a human [8:50] in the loop is critical, especially in [8:53] any domains where trust and safety [8:55] matter most. Third, people often assume [8:58] that the labor market impact is [9:00] determined by technical capability [9:02] alone. Just be because AI can do [9:06] something technically doesn't mean it [9:09] will in the labor market. The real [9:11] barriers to adoption are often not [9:14] technical. The real obstacles are driven [9:17] by customer preferences, by regulations, [9:20] and by organizational adoption. [9:23] Customers, for example, often want human [9:25] interaction, especially in areas like [9:27] healthcare or education where [9:29] relationship buildings matters. [9:32] Regulations, for example, in some states [9:35] may end up requiring human oversight in [9:37] many professions, even when AI can [9:40] technically do the job alone. And on [9:43] organizational adoption, companies [9:45] looking to adopt AI may need and will [9:48] very likely need years to reorganize all [9:51] of their systems and processes before [9:53] they truly can replace a meaningful [9:55] number of workers. To be clear, none of [9:58] this is to say that there will not be [10:00] any jobs displaced. There will be. But [10:03] the factors we've laid out, productivity [10:06] dynamics, human loop design, and [10:08] societal bottlenecks, point to a very [10:10] different picture than some of the [10:12] sensationalist predictions about mass [10:15] job loss, which we do believe when we [10:17] look at the data are overstated. [10:20] Our goal is not to prevent every [10:22] instance of labor market change or [10:25] displacement. It is to make sure that we [10:28] have the right systems in place to help [10:30] navigate those changes and help workers [10:33] impacted find new pathways to even [10:36] better opportunities in the economy. [10:39] Our third core idea is that the most [10:42] critical capability that the Department [10:43] of Labor needs to build for our [10:45] workforce in the age of AI is agility. [10:49] And that's because we believe the [10:50] greatest workforce challenge posed by AI [10:53] will probably not be job loss. It will [10:56] be the speed of change itself. As AI [10:59] accelerates shifts in talent and skill [11:01] needs, the workforce system must be able [11:04] to adapt in real time or risk falling [11:07] behind. In the AI action plan that [11:10] President Trump announced last month, [11:12] the workforce focused sections [11:14] highlighted how the Department of Labor [11:16] is already making progress in building [11:19] this muscle of agility through actions [11:22] that strengthen our capacity to respond [11:24] to rapid change. [11:27] One example is the Department of Labor [11:29] Labor is launching an AI workforce [11:32] research hub. This effort will help us [11:34] build the agility muscle of our [11:37] workforce system by one tracking how AI [11:41] is changing jobs in real time. Two, [11:44] translate what we are learning in the [11:45] data into policy for education and [11:48] workforce. And three, quickly testing [11:50] and refining new innovation pilots as [11:54] the market evolves. A second example [11:56] from the AI action plan is the [11:58] Department of Labor's work to build [12:00] stronger pipelines of talent for [12:02] critical AI infrastructure jobs like [12:05] electricians, linesmen, and data center [12:08] technicians. We're creating a clear [12:10] process to identify skill needs, partner [12:13] with industry on training, and fund what [12:15] is actually working to address talent [12:17] shortages. While the specific focus of [12:20] that plan is for AI infrastructure jobs, [12:23] it does create a blueprint for how we [12:25] can quickly respond to future talent [12:27] shortages across different areas of the [12:29] economy. [12:31] The reality is we cannot be certain [12:34] exactly how the capabilities of AI will [12:36] evolve in the coming years and how they [12:38] will impact the workforce. But what we [12:40] can be certain of is that our workforce [12:42] system cannot stay stagnant. We need a [12:45] system that can diagnose the changes [12:47] that are happening, translate insight [12:49] into action, and help workers and [12:52] businesses keep pace with the evolving [12:54] economy. The Department of Labor's work [12:56] on the AI action plan is a bold step [12:58] towards building the critical muscle of [13:01] agility that we'll need to navigate this [13:03] wave of transformation. [13:06] My fourth and final core idea today is [13:08] that we need a paradigm shift in [13:10] workforce development to prepare for the [13:13] demands of an AIdriven economy. For too [13:16] long, there's been a persistent [13:18] disconnect between the training that [13:20] workers receive and the skills that [13:22] employers are trying to hire for. In [13:25] some states, data suggests that only 34% [13:28] of participants in workforce training [13:30] programs end up working in the [13:32] occupations that we paid to train them [13:35] for. And so, as AI reshapes the skills [13:38] that are required across every industry, [13:41] that mismatch puts workers and [13:43] businesses even further at risk of [13:46] falling behind. We need a workforce [13:48] system that's faster, that's more [13:50] responsive, and that's more directly [13:52] aligned with labor market demand. [13:55] That means moving beyond the historical [13:57] approach that we've seen for workforce [13:59] training, which we like to call the [14:01] train and prey model where training [14:04] programs operate in deep isolation from [14:06] hiring needs and workers are left to [14:09] just hope that their training will lead [14:11] to a good job. In an age of rapid [14:13] change, hope is not a strategy. The [14:16] Department of Labor is setting a new [14:18] standard for workforce development [14:20] programs that begin with clear signals [14:22] from employers, focus on in demand [14:24] skills, and deliver real uh economic [14:28] opportunity. Just yesterday, the [14:30] Department of Labor publicly released [14:32] our flagship workforce development [14:34] report alongside the Department of [14:36] Commerce and the Department of Education [14:38] called America's Talent Strategy: [14:40] Equipping American Workers for the [14:42] Golden Age. [14:44] At the core of that future workforce [14:46] vision is the belief that [14:47] apprenticeships are one of the most [14:50] powerful tools to build real talent [14:52] pipelines in the critical industries of [14:54] the future. A bachelor's degree no [14:57] longer guarantees access to professional [15:00] employment. 52% of the class of 2023 was [15:04] unemployed or underemployed one year [15:06] after graduation. The college for all mo [15:10] movement has failed. Apprenticeships [15:13] offer a faster debt-free alternative to [15:16] traditional higher education and combine [15:19] paid on the job experience with training [15:22] that directly maps to employer needs. [15:25] That's why the president has given the [15:26] Department of Labor a bold mandate to [15:29] reach and surpass 1 million registered [15:32] apprenticeships with a focus on critical [15:34] areas like advanced manufacturing, [15:37] healthcare, and AI infrastructure. To [15:39] deliver on this mandate, the Department [15:41] of Labor is taking bold steps to [15:43] modernize our apprenticeship system. [15:45] We're exploring ways of cutting red tape [15:47] that slows down results, increasing the [15:50] value proposition for employers, and [15:52] making the apprenticeship model more [15:54] flexible to be able to work for new [15:56] occupations and industries. But [15:58] strengthening our talent pipeline is not [16:00] enough. We must also demand [16:02] accountability across the entire [16:04] workforce system. For too long, billions [16:08] of dollars of taxpayer money has been [16:10] spent by the government on workforce [16:12] development programs with no reliable [16:14] ways to measure success. This [16:17] administration is bringing transparency [16:20] and accountability to the workforce [16:22] system. We are tracking the outcomes [16:24] that matter, publishing data so workers [16:26] can make informed decisions, and [16:28] increasing the use of pay for [16:30] performance models so that we are [16:32] actually funding programs that deliver [16:34] results for American workers and [16:36] American businesses. [16:39] In closing, AI is transforming the [16:41] fabric of our economy from how [16:43] businesses operate to the skills that [16:45] workers need. The scale and speed of [16:48] this transformation surpassed any past [16:51] wave of technological change and demand [16:54] an equally bold response. At the [16:56] Department of Labor, we recognize that [16:58] even though kids today are surrounded by [17:01] headlines warning that robots will take [17:03] their jobs, the reality is that the next [17:05] generation of American workers will have [17:07] the opportunity to build and manage [17:10] these robots and AI systems. We believe [17:12] we are at a unique moment in time to [17:15] shift the narrative around AI and the [17:17] workforce from fear to optimism. If we [17:21] accept the narrative of job loss and [17:22] decline, we will miss the chance to [17:25] build systems that will grow our middle [17:27] class and prepare American workers for [17:29] the high-paying jobs of the future. The [17:32] Department of Labor is committed to [17:33] leading that shift by expanding the new [17:36] opportunities that AI creates and [17:38] equipping workers with the AI literacy [17:40] skills needed to pursue them. By [17:42] building the agility needed to keep up [17:44] with the pace of change through new [17:46] models of research and innovation, and [17:49] by transforming our workforce [17:50] development system through proven [17:52] approaches like apprenticeships to [17:54] better connect workers and businesses. [17:57] Through these actions, we will deliver [17:58] on the president's vision for American [18:00] leadership in AI and ensure that all [18:03] Americans can realize the opportunities [18:05] of the AI economy and make pathways to [18:07] the American dream more alive than ever [18:10] before. Thank you. [18:12] [Applause]