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On with Kara Swisher

The AI job disaster Congress refuses to fix

Former Commerce Secretary Gina Raimondo says America is heading toward an AI jobs crisis. And she doesn't think Washington is ready.

Kara SwisherhostGina Raimondoguest

Jul 25, 202641mWatch on YouTube ↗

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CHAPTERS

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Chapter summaries

Cold open: Why federal AI action stalls (and the podcast is now video)
Kara Swisher presses Gina Raimondo on why the Biden administration couldn’t “get this done,” prompting a blunt answer about Congress. They also joke about the show’s shift from audio-only to video.
- •Kara asks why the administration couldn’t deliver action on the issue
- •Raimondo points immediately to Congress as the blocker
- •They riff on listeners not seeing reactions now that the podcast has video
- •Tone-setting banter before the main conversation begins
Raimondo’s “9 out of 10” worry: the U.S. isn’t institutionally ready for AI
Kara introduces Raimondo and frames the discussion around AI’s impact on the economy and jobs. Raimondo says she’s highly worried and highly excited, but argues the country lacks up-to-date laws, institutions, and worker supports to manage the transition.
- •Raimondo rates her worry as a nine, citing lack of laws and preparedness
- •She also rates excitement as a nine, describing potential benefits like accelerating research and curing diseases
- •She flags job disruption risks for white-collar workers, women in clerical roles, and entry-level workers
- •She argues unemployment insurance and retraining systems aren’t built for what’s coming
- •She calls for standards and government testing of models before release, with consequences if they fail
The “politics problem”: money, influence, and broken incentives in Washington
Kara asks Raimondo to unpack what she means by a politics and institutions problem. Raimondo focuses on money in politics across industries and calls the system “broken,” pointing to transparency and the effects of Citizens United.
- •Raimondo says influence-buying isn’t just a tech problem but spans regulated industries
- •She describes political spending to buy influence as out of control
- •She argues for more transparency in the political system
- •She calls Citizens United a “terrible decision” and names it as a top fix for American politics
Raise Us: governor-led pilots to prevent AI job losses from becoming a crisis
Raimondo explains her nonprofit, Raise Us, which works with governors of both parties to test workforce transition ideas. She argues training alone won’t be enough and outlines pilots aimed at redeployment, wage supports, and entrepreneurship.
- •Raise Us is described as nonprofit, non-political, and focused on bipartisan governor action
- •Mission: lead in AI without leaving workers behind
- •She proposes incentives for redeployment and disincentives for mass layoffs
- •She describes a Maryland pilot with a “year of service” placing young people into shortage areas
- •She discusses wage insurance, transition support, job sharing, and reforming unemployment insurance to support entrepreneurship
Why the federal government isn’t building an “ark”: Congress doesn’t function
Kara asks why, despite widespread warnings, federal action hasn’t materialized. Raimondo responds that Congress doesn’t get things done, and says her strategy is to develop state-level pilots that can later become bipartisan federal legislation.
- •Kara compares looming AI disruption to a flood no one is preparing for
- •Raimondo says Congress is not functioning and “doesn’t get things done”
- •She shifts focus to governors as the actors who can move faster
- •She lays out a plan to build evidence through pilots that could be scaled nationally later
Data centers as AI flashpoint: community backlash, energy and water safeguards
Kara raises data centers as a new focal point for public anger about AI, citing political pushback like pauses and moratoria. Raimondo argues data centers can be beneficial, but only if companies follow community-centered rules around costs, resources, and benefits.
- •Kara frames data centers as a proxy for broader AI disgust and anxiety
- •Raimondo says data centers aren’t inherently bad and can enable innovation
- •She explains backlash as people feeling costs rise without control or recourse
- •She points to concerns like higher electricity bills and insists bills shouldn’t rise for residents
- •She calls for rules on energy sourcing, water recycling, limiting tax breaks, and worker/community care—because companies must be made to comply
Avoiding a repeat of offshoring pain: Raimondo’s father, concentrated losses, and democracy risk
Kara asks Raimondo to reflect on earlier economic transitions, including her father losing a manufacturing job, and what went wrong. Raimondo argues the offshoring era showed how concentrated job losses can fuel anger and dysfunction—and warns AI could fray democracy if the transition isn’t planned.
- •Raimondo uses her father’s job loss as an analogy for AI-era disruption
- •She argues companies previously prioritized efficiency and profit, ignoring broader social consequences
- •She says losses were concentrated among non-college educated men in a handful of states, fueling political anger
- •She connects the CHIPS Act to a broader lesson: lack of foresight can create national-security and economic crises
- •She identifies AI-driven job disruption and unrest as a major systemic risk, and says no company benefits from mass instability
Are tech companies serious—or just warning? Coalition-building and shared responsibility
Kara asks whether tech companies are genuinely concerned or simply sounding alarms without solutions. Raimondo describes assembling a coalition of companies (including competitors) and argues responsibility extends beyond AI labs to every company implementing AI.
- •Raimondo says she has brought together a coalition of companies, including tech firms
- •She says warnings alone aren’t solutions and presses for concrete action
- •She refuses to place all blame on AI companies, noting adoption decisions are made across the economy
- •She argues there must be a way to lead in AI without creating sky-high unemployment
- •She says she’s giving companies the benefit of the doubt while pushing for outcomes
Workers training their replacements: why retraining alone won’t solve it
Kara plays an audience/expert question about low-wage workers wearing cameras to generate data used to train robots that may later replace them. Raimondo responds that retraining is only one piece and that the economy can’t absorb sudden, massive displacement without broader transition supports.
- •The question highlights workers paid today to create data that may automate their jobs tomorrow
- •Raimondo says retraining is not enough, though it matters
- •She reiterates the need for transition support and incentives/disincentives around layoffs
- •She argues the U.S. economy will create new jobs and businesses over time, but timing matters
- •She warns against “pulling the rug out” from tens of millions of people quickly
What jobs are “safe,” and what the workforce system gets wrong
Kara asks what kinds of jobs will exist and which are most at risk, as Raimondo discusses uncertainty and the need to focus on entry-level pathways. Raimondo points to demand in trades and clinical roles, critiques the “route everyone to college” approach, and urges fixing pipelines where shortages exist.
- •Raimondo says it’s early and hard to predict who loses and who gains jobs
- •She argues headline unemployment figures miss underemployment and insecurity
- •She emphasizes protecting entry-level pathways and pressuring companies not to cut analyst programs
- •She identifies trades/technician work and clinical roles as in-demand areas
- •She critiques the U.S. workforce system for defaulting to four-year college and calls for more effective pathways tied to real jobs
If she were president: AI safety first, then overhaul incentives and worker supports
Kara asks what Raimondo would do first as president on AI. Raimondo prioritizes safety—standards, pre-deployment testing, regulation with penalties—and then turns to economic reforms including unemployment insurance modernization and changing incentives that currently reward layoffs over employing people.
- •Raimondo says the first priority would be AI safety
- •She calls for standards, pre-deployment testing, and regulations with penalties
- •She raises risks like cybersecurity and bioterrorism, including misuse by non-state actors
- •She says the U.S. should work with allies and even China to reduce catastrophic misuse
- •She argues incentives reward firing people and using AI agents, and floats taxing AI usage by companies as part of a new incentive structure
Higher education in the AI era: fewer colleges, more apprenticeships, and job-ready AI skills
Kara asks how universities should respond as AI changes work and learning. Raimondo argues many colleges have poor outcomes and that the U.S. lacks enough alternatives like apprenticeships; she also says employers want graduates who can use AI to do real work, not just “cheat.”
- •Raimondo argues the country has too many colleges and many don’t produce good outcomes
- •She notes many students don’t finish college, leaving debt and limited benefit
- •She says high school graduates face too few options besides college, military, unions/trades, or low-wage service work
- •She asks why apprenticeship-style pathways aren’t more common in the U.S.
- •She says employers want people who can use AI effectively for the job while still having critical thinking and writing skills
Advice for students: courage, resilience, learn the tools, and consider entrepreneurship
Kara closes by asking what Raimondo would tell students preparing for AI-driven change. Raimondo urges courage, practical AI literacy, and resilience, and says if she were graduating today she’d try to start a business—possibly tackling problems like lowering healthcare delivery costs.
- •Raimondo tells students to face the future with courage and not be paralyzed by fear
- •She encourages learning to use AI tools for real work because they aren’t going away
- •She argues lab demos don’t instantly translate into total job displacement and gives examples of overconfident predictions
- •She urges students to track where job openings actually are and build judgment, calmness under pressure, and adaptability
- •She says she would start a business today using AI to build cheaply, and mentions interest in solving healthcare cost/access challenges

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