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🏭xAI's Pollution Problem
PLUS: Alibaba Outpaces Competitors | MIT Maps AI Displacement
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🗞️In this edition
xAI plans 88-Acre solar farm amid pollution controversy
Sponsored: i10x - All-in-one AI workspace
Alibaba commits to higher AI capex amid growth
MIT's Iceberg index maps AI labor displacement risk
Workflow Wednesday #47: AI-Powered Planning
In other AI news –
WhatsApp kicks ChatGPT and Copilot off the platform
Bezos project Prometheus snaps up agentic computing startup
Regulators in Italy widen scrutiny of Meta AI tools
4 must-try AI tools
Hey there,
xAI's proposing an 88-acre solar farm covering 10% of power needs while running 400+ megawatts of unpermitted gas turbines that increased nitrogen dioxide 79% in a predominantly Black Memphis neighborhood. Alibaba can't add servers fast enough to meet customer orders despite $52B infrastructure commitment as China's 515 million AI users create demand matching US hyperscaler growth rates. And MIT released a study showing AI can replace 11.7% of US jobs representing $1.2 trillion in wages, with most exposure hidden in back-office functions, not tech layoffs making headlines.
We're committed to keeping this the sharpest AI newsletter in your inbox. No fluff, no hype. Just the moves that'll matter when you look back six months from now.
Let's get into it.
What's happening:
AI startup xAI, founded by Elon Musk, told Memphis city and county planners last week it plans to build a solar farm next to its Colossus data center, one of the world's largest facilities for training AI models.
The project would occupy 88 acres. Given the proposed size, the solar farm would likely produce around 30 megawatts of electricity, only about 10% of the data center's estimated power use.
Musk's company has come under fire for operating over 400 megawatts of natural gas turbines without permits, according to the Southern Environmental Law Center. The legal organization, working with NAACP, says xAI has operated at least 35 turbines capable of emitting more than 2,000 tons of NOX pollution annually, nitrogen oxide emissions that contribute to smog and respiratory problems.
Turbines have sparked fierce opposition from residents in nearby Boxtown, predominantly Black community where University of Tennessee researchers found peak nitrogen dioxide concentration levels increased by 79% in areas immediately surrounding the data center after xAI began operations. Community activists have reported increased asthma attacks and respiratory issues since the facility opened.
The AI company said it intends to use turbines until it can secure additional power, but local officials gave xAI permit to operate 15 turbines through January 2027.
Why this is important:
A 30-megawatt solar farm providing only 10% of data center power while operating 400+ megawatts of unpermitted gas turbines shows solar is a PR move, not a solution.
Operating 35 turbines without permits while emitting 2,000+ tons of NOX pollution annually is flagrant regulatory violation xAI's getting away with.
$439M USDA award including $414M interest-free loan while EPA cancels other clean energy grants shows preferential treatment for Musk's companies under Trump administration.
Our personal take on it at OpenTools:
This is greenwashing while poisoning a community.
Proposing a 30-megawatt solar farm that covers 10% of power needs while running 400+ megawatts of unpermitted gas turbines is performative environmentalism. The math doesn't work.
79% increase in nitrogen dioxide causing asthma attacks and respiratory issues is documented health harm. This isn't theoretical. People are getting sick.
Local officials giving permit to operate 15 turbines through January 2027 despite 35 operating without permits shows regulatory capture. xAI's operating 20+ turbines with zero permission and officials respond by permitting 15 retroactively.
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What's happening:
Alibaba Cloud reported 34% year-over-year revenue growth to $5.6 billion in Q2, marking its fourth consecutive quarter of double-digit growth. CEO Eddie Wu said the company can't add servers fast enough to meet customer orders and won't rule out scaling up capital expenditure beyond its $52 billion commitment.
"The pace at which we can add new servers is insufficient to keep up with the growth in customer orders," Wu said during Tuesday's earnings call.
Alibaba's cloud growth aligned with US peers: Microsoft Azure posted 40% growth, Google Cloud 33.5%. Jefferies analyst Thomas Chong said "high level of growth is expected in the December quarter."
The company deployed about $16.8 billion in AI and cloud infrastructure capex over the past 12 months. Wu said Alibaba would prioritize in-house model training and Model Studio demand over leasing out GPUs.
China saw generative AI adoption reach 515 million users in the first half of this year, most preferring domestic models. Alibaba's Qwen chatbot app is its push into the consumer AI market.
Alibaba intends to outspend domestic peers Tencent and Baidu on AI infrastructure, aiming to spearhead China's global AI leadership efforts.
Why this is important:
Alibaba saying server capacity can't keep up with orders echoes Google's admission that compute constraints are limiting product launches. This is an infrastructure bottleneck across the industry, not isolated problem.
The $52 billion commitment being China's largest-ever computing project by a single private business shows the scale of Alibaba's AI ambitions. That's comparable to US hyperscaler spending levels.
515 million generative AI users in China preferring domestic models creates a massive addressable market for Qwen. That's a larger user base than the US population.
Our personal take on it at OpenTools:
$52B commitment is massive but "wouldn't rule out scaling up" suggests even that isn't enough. Alibaba's chasing moving target where demand keeps accelerating faster than supply ramps.
34% cloud growth matching Microsoft and Google validates China's AI infrastructure market is scaling at a pace comparable to the US. The geographic split in AI development (US and China dominating) is reflected in infrastructure spending.
515 million users adopting generative AI in six months in China is a stunning adoption rate. For context, ChatGPT took longer to reach 100 million users globally. China's scale creates different market dynamics.
Qwen competing with DeepSeek, ByteDance's Doubao, and Tencent's Yuanbao in the domestic market while Alibaba invests $52B+ creates the question: can the Chinese market support three major AI platforms profitably? Or does consolidation follow an infrastructure race?
Fourth consecutive quarter of double-digit growth is a positive trend but doesn't address whether AI cloud services are profitable or subsidized to gain share. Revenue growth without margin disclosure leaves key questions unanswered.
This positions Alibaba as China's AI infrastructure leader but also reveals same capacity constraints plaguing Google and Microsoft. Nobody can build fast enough to meet demand. That's either a sustainable growth story or temporary demand spike before correction. The next 12 months will clarify which.
What's happening:
MIT released a study showing AI can already replace 11.7% of the US labor market, representing $1.2 trillion in wages across finance, healthcare, and professional services.
The research used the Iceberg Index, a labor simulation tool created by MIT and Oak Ridge National Laboratory that models 151 million US workers as individual agents tagged with skills, tasks, occupation, and location across 32,000 skills in 923 occupations.
The visible "tip of the iceberg" (tech layoffs and IT role shifts) represents just 2.2% of wage exposure or $211 billion. The hidden exposure includes routine functions in HR, logistics, finance, and office administration totaling $1.2 trillion.
Tennessee, North Carolina, and Utah have validated the model using state labor data. Tennessee cited the Iceberg Index in its AI Workforce Action Plan released this month.
North Carolina Sen. DeAndrea Salvador said the tool provides "county-specific data to essentially say, within a certain census block, here are the skills currently happening now and the likelihood of them being automated or augmented."
The Index shows exposed occupations spread across all 50 states, including inland and rural regions often excluded from AI conversations.
Why this is important:
$1.2 trillion in exposed wages being 5.5× larger than visible tech displacement ($211B) shows most AI labor impact is hidden in routine back-office functions, not headline-grabbing tech layoffs.
County-level granularity lets policymakers identify which specific communities face displacement, not just broad industry categories. That enables targeted reskilling investments.
Three states already using the tool for policy planning shows this is an operational framework, not academic exercise. Tennessee's official AI Workforce Action Plan citing Iceberg validates practical utility.
Our personal take on it at OpenTools:
11.7% exposed doesn't mean 11.7% unemployed.
"Can replace" and "will replace" are different. The study maps technical feasibility, not economic viability or adoption timeline. Just because AI can do a job doesn't mean companies will automate it immediately.
The 2.2% visible displacement being tech/IT jobs while 11.7% total includes back-office is revealing. The media focuses on software engineers worried about Copilot. Real displacement risk is administrative assistants, bookkeepers, and logistics coordinators who don't make headlines.
County-level detail is genuinely useful for policymakers. Knowing Nashville faces different exposure than rural Tennessee lets states target reskilling programs geographically. That's a practical policy tool.
But simulation accuracy depends on data quality. Mapping 32,000 skills across 923 occupations for 151 million workers requires massive assumptions about skill transferability, AI capabilities, and adoption rates. Small errors compound at scale.
Tennessee, North Carolina, and Utah validating with state data is a good sign but also reveals selection bias. These are states proactively engaging with AI policy. States ignoring the issue aren't validating anything.
Positioning this as "sandbox for states to test scenarios" is correct framing. It's a scenario planning tool, not a prediction engine. States should use it to prepare, not panic.
The real question is policy response. If 11.7% of jobs are exposed, what interventions actually work? Reskilling programs have a mixed track record. Simulation can identify exposure but can't guarantee training programs produce employment.
This is a useful tool for policymakers but shouldn't be mistaken for certainty about AI's labor impact. Exposure isn't destiny.
This Week in Workflow Wednesday #47: AI-Powered Planning
This week, we’re showing you how to turn messy goals into clear, data-backed strategy — without disappearing into a five-hour planning session or a 40-slide deck.
Workflow #1: Build a 90-Day Growth Plan in 10 Minutes (Perplexity)
Step 1: Drop a single query into Perplexity — “Give me the current market landscape for [your industry] with competitors, trends, risks, and opportunities.” Watch it pull live data you’d normally spend half a day hunting down.
Step 2: Paste that output back in and tell Perplexity to rank your next 90-day priorities using the ICE or RICE frame……We break down this workflow (and two more ways to use AI to plan smarter and execute faster) in this week’s Workflow Wednesday.
ChatGPT and Copilot are being booted out of WhatsApp – The Meta messaging app’s new terms prohibit rival AI chatbots.
Jeff Bezos’ New AI Venture Quietly Acquired an Agentic Computing Startup – Project Prometheus has raised over $6 billion in funding and hired over 100 employees, a handful of whom joined through its acquisition of General Agents, according to records and sources.
Italy antitrust watchdog may curb Meta as WhatsApp AI probe widens – The case underscores growing regulatory scrutiny of Big Tech’s push into generative AI, as platforms with massive user bases such as WhatsApp become key gateways for new services.
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Watermelon - An all-in-one automated customer service tool powered by GPT-4
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