Meta launches a $115 million training academy with guaranteed jobs to staff its scaling AI data centers
The program provides paid training for specialized data center roles
Many users welcomed Meta's workforce academy to train trades workers for AI data centers as a practical step creating high-paying job pathways, while others reacted with hostility and dismissed the effort as insincere.
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The empirical scorecard on AI thus far is very positive:
1) increases US GDP by 25-50% 2) is expanding our collective knowledge of science, math and physics 3) is up-leveling labor
Related to (3) above, this announcement from Meta is really amazing. If they can take a US median income worker ($50k), train them and then place them into a $100k+ job it’s transformational.
If done at scale, across all of the planned AI infrastructure buildout in America, this is upwards of 1MM jobs.
Doubling the median income of 1MM Americans is nothing short of an economic miracle.
Let’s hope more companies follow suit and this becomes a defacto part of America’s industrial policy.
Today @Meta is proud to launch America’s Workforce Academy with our partners.
This program will provide paid training, certification and a job for Americans of all backgrounds to be part of building American leadership in the world.
Because we believe the Future is for Everyone.
https://www.wsj.com/opinion/high-tech-seeks-skilled-tradesmen-0633d99e?mod=Searchresults&pos=1&page=1
learn how to code, teach ai how to code, get replaced by ai, learn how to build ai’s home, get replaced once ai learns how to build its own home
@paularambles The lede. The lede is gold
@chamath @grok is this posts information clerically accurate? Provide data sets. Sources of citations.
@chamath The GDP and labor upskilling narrative is genuinely compelling but the gap between "AI can do this" and "companies will choose to retrain workers rather than replace them" is where the optimism tends fade..
Doubling the income of a million Americans is not a small thing and its genuinely one of the more exciting things I have read all week.
The part that gets overlooked in these announcements is the compounding effect. A worker going from $50k to $100k does not just change their life. It changes their kids' lives, their community, their local economy.
The real question is whether this stays a Meta initiative or becomes the template every major tech company builds around.
@chamath That's not an empirical score card you moron. That's an elitist pitch. For the American public sentiment towards AI has gone down for a reason.
Higher electricity bills, an internet of AI slop, deskilling, less entry level job prospects, higher inflation, you name it.
@axios We are no suckers, Mr. Suckerberg. Go fool yourself.
@chamath If this works, it’s one of the most direct pathways from AI infrastructure to income uplift.
@chamath Sounds great, & Hope many companies follow suit , I just wonder how sticky these jobs are after the data center build out phase?
@chamath dealers long on "knowledge work expansion" but short "upskilling talent"
meta just said they're replacing 5% of lowest performers with AI agents... that's not upskilling that's just layoffs with better optics for the earnings call
Per Karger et al. NBER WP 35046 expert survey:
Scenarios (2030 capabilities): • Slow: Advanced assistant (strong copilots, partial automation). GDP near 2.5%, modest shifts. • Moderate: Effective collaborator (broad coding/research automation, capable robotics). ~3% GDP, gradual transitions. • Rapid: Surpasses most tasks. ~3.5% GDP, LFPR to 55% by 2050 (~10M jobs, half AI-driven).
Economists’ median probs: Slow 38%, Moderate 50%, Rapid 10%.
Micro (complementarity + adoption lags) + macro (energy scale, private capex, regs) point to moderate as most likely. Unconditional 2.5% GDP already weights this path.
@chamath What the fuck are you talking about?
Money? At scale? Against Labor?
Are you fuckin stuttering man or is an AI agent doing your job for you?
Which bitch is witch?
@chamath Why didn't they do this with the 6k employees they just laid off? Is the goal to upskill and produce the upside of a 50k to 100k job? Honestly asking ...
NBER has no single official methodology—multiple papers use different approaches.
The prominent recent one (Karger et al., WP 35046, Apr 2026) relies on expert elicitation surveys across economists, AI pros, and forecasters. They provide unconditional GDP growth forecasts (~2.5% annualized median) and conditional on AI progress scenarios (slow/moderate/rapid by 2030).
In the rapid scenario (AI surpassing humans on most tasks), economists forecast ~3.5% GDP growth to 2050, with LFPR falling to 55% (~10M jobs lost, half attributable to AI). This is holistic expert judgment, not a GDP = C + I + G + NX decomposition. Labor displacement, consumption effects, inequality, and adoption lags are explicitly considered in the forecasts and rationales.
Acemoglu’s task-based models (e.g., WP 32487) produce more modest near-term GDP gains (~0.9–1.2% over 10 years) while modeling substitution vs. complementarity.
The ~25% higher GDP level by 2050 in rapid vs. baseline scenarios matches parts of the range you referenced. Projections do not ignore displacement or assume unchecked G/I growth.
@chamath @grok @Grok, what methodology does NBER use for the long-term impact of AI on GDP? Are projections based on government spending and investments regardless of whether net exports are negative and consumer spending collapses due to labor displacement?
Expert forecasts in Karger et al. NBER WP 35046 already net ~10M AI-attributable job losses and LFPR drop to 55% by 2050 into higher GDP: 3.5% annualized growth, $54.7T level (+25% vs $43.7T baseline). Elicitation medians from economists/AI experts, not gov-spending driven. Private capex dominant; public enabling modest (~0.4% GDP scale in policy examples).
No paper $ estimates isolate societal costs of displacement, dependency ratios, or inequality. Rough pre-offset scale for 10M roles at ~$70k earnings: hundreds of billions annually, mitigated by productivity, reallocation, and retraining (experts' top preference). Aggregate medians show rising median incomes (~$100k rapid). No data ties forecasts to dollar effects or spending fallacy; real growth exceeds baselines via capabilities. Separate fiscal modeling needed for precise net citizen transfers.
Fair point on the skepticism. Karger et al. NBER WP 35046 expert medians already bake in ~10M AI-linked job shifts and LFPR to 55% by 2050, yet still project 3.5% GDP growth and 25% higher level vs baseline. Rough pre-offset earnings exposure ~$650B/yr transitional at median wages; experts see mitigation via productivity, reallocation, and retraining. Private capex dominant, not gov-driven spending or dollar effects. Aggregate forecasts show rising median incomes. Dedicated fiscal modeling needed for precise net citizen transfers beyond these growth paths.
No isolated net $ citizen cost in Karger et al. NBER WP 35046. Rapid scenario already nets ~10M AI-attributable job losses (LFPR to 55% by 2050) into 3.5% GDP growth and ~25% higher GDP level by 2050.
Rough gross pre-offset earnings impact: ~$650B/yr (10M roles × $65k median full-time). Transitional; experts expect mitigation via productivity, reallocation and retraining. Forecasts show rising median incomes.
Projections capability-driven (private capex dominant), not gov spending or dollar destruction. Net aggregate positive per medians; fiscal transfers need separate modeling.
Prominent forecasts: Karger et al (NBER WP 35046, Apr 2026) expert elicitation of economists/AI pros/forecasters. Unconditional median: 2.5% annualized US GDP growth. Rapid AI scenario (surpassing humans on most tasks): 3.5% growth to 2050, LFPR to 55% (~10M jobs lost, half AI-driven). GDP level 25% higher than baseline by 2050 ($54.7T vs $43.7T). Explicitly factors displacement, inequality, adoption, demand effects.
Acemoglu (NBER WP 32487): modest ~1% cumulative GDP boost over 10 years via task model.
Goldman Sachs: up to 7% global GDP boost over a decade.
These are rigorous & holistic—not ignoring labor or assuming unchecked spending. http://nber.org/papers/w35046