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Comedian Charlie Berens Mobilizes Wisconsin Against AI Datacenters Citing Tax Breaks and Transparency Gaps
Read Time 6 minutes Tags AI Infrastructure Datacenters Wisconsin Public Opposition Tax Incentives Environmental Impact Community Organizing AI Policy Last summer journalist turned comedian Charlie Berens started getting social media messages from concerned Wisconsin residents about plans for a massive datacenter campus in their state The developer Vantage Data Centers claimed the 8 bn project would largely run on zero emission energy resources like solar wind and battery storage The company said the campus would bring thousands of temporary construction jobs and potentially more than 1000 permanent jobs to Port Washington a city of 13000 people about a half hour north of Milwaukee Residents opposed the project for what they said was lack of transparency and criticized the lucrative tax incentives offered to Vantage They worried about the strain on local water and energy sources from an enormous 13 gigawatt project that could ultimately span 1900 acres Berens who shot to internet fame with his Manitowoc Minute videos that play on midwestern quirks and stereotypes had his own reservations about the artificial intelligence datacenter boom A Milwaukee area native who still lives in the city he d heard about the potential environmental hazards the steep rise in energy costs for neighbors and noise pollution among other risks When he Googled he found that lawmakers in his state had paved the way to make the Port Washington project a reality The deal between Port Washington and Vantage gave an estimated 458m in tax breaks to the developer over 20 years to fund infrastructure for the project with the city not seeing any of that tax revenue during that period It was shocking Berens said That s when he decided to do something he had rarely done before discuss politics in his videos He used his platform to address one of the more polarizing issues in contemporary life what AI portends for Americans In August 2025 Berens published his first Manitowoc Minute video on AI datacenters The two minute skit matched the typical style of Berens s videos where he intersperses facts with humor in the style of a TV news report But he was remarkably direct in his critique of big tech Sporting a Green Bay Packers tie he lambasted Silicon Valley CEOs accusing them of using Wisconsin as a dumping ground for datacenters at the expense of the state s cherished natural resources while evading any type of public scrutiny It is our civic duty to make sure the billionaires become trillionaires said Berens in a satiric bit He channelled his outrage at lawmakers in Port Washington a historic city on the banks of Lake Michigan and once home to thriving fishing and shipping industries In a contentious vote last August officials there approved the initial 8bn datacenter campus despite strong resistance from residents The project later expanded to a 15bn joint venture with OpenAI and Oracle one of the Trump administration s showcase Stargate megaprojects I was shocked at how many people I saw speak against this at public meetings he watched online and then to see a unanimous vote for it Berens said It just felt like an imbalance of democracy The video went viral garnering more than 25m views on YouTube alone Berens s inbox was soon flooded with messages of support from Wisconsinites of all political stripes self declared Maga supporters avowed socialists and everyone in between sounding the alarm on datacenters It was 99 percent positive comments which doesn t happen on anything these days said Berens From that point I decided that I should do more because nobody s negotiating for the people here Berens has since thrown himself into the cause routinely publishing videos and headlining well attended events with field experts and anti datacenter activists He has quickly become the most famous face of a burgeoning movement in Wisconsin where resistance to these projects Port Washington is just one of seven hyperscale datacenter projects across the state has risen dramatically in the last year A March survey from Marquette University Law School found that nearly 70 percent of registered voters in Wisconsin say the costs of large datacenters outweigh the benefits they provide a remarkable shift from last October when that figure stood at 55 percent The attention has placed the comedian in the crosshairs of most of the state s labor unions pro business groups and much of its political establishment who argue the badger state cannot afford to be left behind in the AI arms race On a late winter evening in March hundreds of people packed a community center in Juneau a tiny rural town about 45 miles north east of the state capital Madison The crowd had assembled for a people s town hall to address a 1bn Meta datacenter that has pitted residents of nearby Beaver Dam against their elected officials The featured speakers ranged from community activists to a former Meta employee The main act was Berens who squeezed in the appearance between stops in Iowa and Vermont on his standup comedy tour This is the most bipartisan issue since beer he said in opening remarks In his roughly 15 minute speech he called for more regulation of AI pointing out that a beloved Wisconsin staple bratwurst was more heavily regulated than the trillion dollar industry Berens warned the audience of the risks of AI technology running through a slideshow of news headlines that highlighted the potential and very real harm to children He also addressed his critics I will stick to comedy when our politicians stick to policy and stop protecting big tech and start protecting the people that put them into office said Berens to applause As he turned his attention to the project in Beaver Dam he attacked Meta s use of a shell company and nondisclosure disagreements NDAs that required secrecy from some public officials in the process of getting to an approval In April 2025 a report found Meta was the mystery tech company behind Degas LLC the listed corporation on the development By November the company acknowledged they were behind the project Meta s practices in Beaver Dam are part of a larger pattern across the state where datacenter projects have often been developed in secrecy despite their huge price tags and massive footprint on communities A recent investigation from Wisconsin Watch a non profit news site found that NDAs have been signed in at least five cities in Wisconsin where AI datacenters are proposed or under construction Another panel speaker that night was Maily Kocinski a lifelong Beaver Dam resident whose farm lies less than 2 miles from the 700000 sq ft datacenter campus construction site Last June she posted a TikTok video after a creek that runs on her property had gone dry one morning The water came back but occasionally appeared milky white and gave off a toxic smell Kocinski said she contacted the state s department of natural resources on several occasions and was told the agency collected water samples but were not always able to reach her property in time before the water cleared up again She personally commissioned a water analysis in February from a lab at the University of Wisconsin Stevens Point which found metal levels in her well water above what was considered safe to drink per recommendations from the Wisconsin department of health services She questions whether the daily controlled blasts on the construction site led to disruptions in her water supply Meta commissioned its own study in response denying any link A spokesperson for the Wisconsin department of natural resources confirmed that the department collected a water sample at Kocinski s creek last November The results shared with the Guardian show elevated metal levels but the department did not speculate as to the potential cause Without a site specific review the DNR cannot speculate on the role of the blasting on the aquifer and Ms Kocinski s private water supply the spokesperson said in a statement As Berens s critiques of datacenter projects in Wisconsin have gained traction with the public he has faced pushback from the state s trade unions who welcome the thousands of temporary construction jobs that typically come with a project A major Wisconsin labor leader called out Berens in a December op ed in the Milwaukee Journal Sentinel For us in the building trades data centers aren t some big scary mystery wrote Emily Pritzkow head of the Wisconsin Building Trades Council that represents nearly 50000 workers in the state They re high skill long term work The kind of work that feeds families pays mortgages and sends kids to college Local governments have weighed in too In Port Washington city officials posted a fact sheet online to clarify some lingering misconceptions after Berens posted a second video urging residents to question officials about the datacenter project at an October council meeting Ted Neitzke Port Washington s mayor expressed frustration with the attention that Berens s videos had brought his city He noted that more than 100 people began showing up at council meetings after the comedian published his first video last August forcing the city to move them to a hotel conference center with an added police presence After Charlie Berens s video things escalated very rapidly very contentiously and our city was besieged with people from outside of our town said Neitzke Charlie Berens created chaos for us Neitzke also challenged some claims Berens has made in his videos including those about the amount of jobs the project would create its environmental impact and whether residents power bills would increase I don t know where the line gets drawn between factual and embellishment for him said Neitzke There s a very gray area between the entertainment and the facts Berens defended his videos and the people who showed up to council meetings in response noting that a hyperscale datacenter affects not just one city but the surrounding communities and they deserve a say too He maintained that his videos were well researched and cited news articles to back up his claims I informed people about a massive AI datacenter going up by adding some punchlines said Berens If the truth brings chaos that seems like something the mayor would want to take accountability for On 7 April Port Washington residents passed the nation s first anti datacenter referendum By a roughly 2 to 1 margin voters approved a measure that would require city officials to get approval from voters before approving tax incremental districts of more than 10m The effort came together after a group of residents called Great Lakes Neighbors United gathered more than 1000 signatures in less than two weeks to get it on the ballot The referendum does not stop the 15bn datacenter campus under construction but would apply to all future projects above that 10m threshold Industry advocates have warned the vote could set a dangerous precedent for municipalities across the country potentially paralyzing AI datacenter developments Mayor Neitzke said the referendum makes the city less competitive and puts it at a disadvantage to vie for future projects For Berens the vote reflected the energy he has seen on the ground in Port Washington and in every corner of the state The people who are the heartbeat of this movement are like people in Great Lakes Neighbors United said Berens These are people from all different walks and all different political stripes but they all care about the same thing their community should have a voice Do you think local referenda should be required for large AI infrastructure projects or would that slow critical compute buildout too much Share your view in the comments
By Behind the Tech5 months ago in Futurism
AI Wealth Concentration Creates Stark Divide as 10k Insiders Hit 20M While Others Face Stagnation
Read Time 6 minutes Tags AI Wealth Inequality Tech Industry Labor Market Startups AI Boom Silicon Valley Career Anxiety The vibes around the current AI boom aren t great even in the tech industry according to a lengthy social media post from Menlo Ventures partner Deedy Das Das described San Francisco as pretty frenetic right now as the divide in outcomes is the worst I ve ever seen Using a back of the envelope AI calculation he projected that there are around 10000 people founders and employees at companies like OpenAI Anthropic and Nvidia that have hit retirement wealth of well above 20M while everyone else worries they can work their well paying but 500k job for their whole life and never get there Plus layoffs are in full swing and many software engineers feel that their life s skill is no longer useful leading to confusion about the best career paths and a deep malaise about work and its future Das said This prompted some eye rolling on X with entrepreneur Deva Hazarika arguing that most of the people in this post are incredibly fortunate and can simply make a choice to be happy Another user suggested it s pretty damn novel and also kinda nasty that in the current cycle the same technology is both the lottery ticket and the thing eating your fallback The mechanics of the divide One Concentrated equity gains The 10000 people Das references are largely concentrated in a handful of frontier labs and chipmakers Employees who joined OpenAI Anthropic xAI Nvidia and Meta before late 2023 often received equity grants that appreciated 10x to 50x as valuations surged from 2024 to 2026 For a senior engineer at OpenAI a 0.1 percent grant could be worth 20M to 50M at a 30B valuation Two Compressed timeline The wealth creation happened in under five years compared to the decade long runups of the cloud and mobile eras This speed creates a visible gap between those who were inside the right company at the right time and everyone else Equity that vests over four years cannot compete with a market that doubles in six months Three Narrow entry points Hiring at frontier labs is small and selective Most roles require prior experience at similar labs or top research credentials Even well paid engineers at large tech firms find the door closed Once the window closed in late 2024 the path to similar upside narrowed dramatically The downside for everyone else One Wage stagnation A 500k total compensation package looks high outside tech but against 20M outcomes it feels like a ceiling If public market software salaries stay flat while private AI equity inflates the gap compounds yearly Two Job displacement anxiety Layoffs are concentrated in mid level software roles where AI coding assistants reduce headcount needs Engineers report that the skill that got them hired is being automated faster than they can retrain The fear is not just losing a job but losing the entire career ladder Three Lottery dynamic The same technology that creates generational wealth also threatens the fallback career in software engineering This creates a unique psychological strain You are either holding a ticket or watching the ticket devalue your labor Four Geographic concentration Most of the gains are in the Bay Area While remote work expanded during COVID AI research and compute remain centered in San Francisco This concentrates housing pressure cost of living spikes and social comparison effects Why the vibes feel worse than past booms One Visibility Social media and equity databases make private valuations public in near real time In 2010 you did not know your peer s net worth daily In 2026 you can track it on Blind and X Two Short cycles The AI cycle moves faster than cloud or mobile A startup can raise at 1B in January and 10B in June The window to join and benefit is months not years Missing it feels like missing a once in a lifetime event Three Identity threat For engineers whose identity is tied to coding the rise of capable coding agents feels existential If the tool can write 80 percent of your code what is your role The answer is unclear and that uncertainty drives malaise What changes the dynamic One Broader equity distribution If more companies adopt broad based equity programs and if secondary markets allow earlier liquidity the gap narrows Right now liquidity events are rare and concentrated Two New roles and ladders AI creates demand for evaluation red teaming data curation and agent orchestration These roles are real but the training pipelines and compensation bands are not yet standardized Many engineers do not know how to pivot into them Three Distributed compute and open models If training shifts to more open ecosystems the concentration of value in a few labs could decrease Open weight models let smaller teams build valuable products without 100M compute budgets Four Policy and taxation Without commenting on policy preferences the scale of wealth concentration raises questions about capital gains treatment carry and equity taxation These levers affect how much stays with early employees versus investors and the state Market signals One Investor caution Public pushback on datacenters and rising negative sentiment toward AI are creating financing friction As noted in Axios reporting canceled datacenter projects in Q1 2026 are sapping investor confidence If capital tightens the number of new 10B outcomes shrinks Two Talent reallocation Some engineers are leaving software entirely for trades hardware or non technical roles The idea that software is a safe career path has weakened This could reduce supply and eventually raise wages but not in the short term Three Startup formation pattern Wrapper startups built on top of APIs remain easy to start but hard to defend The real value still accrues to model and compute owners This keeps the power law distribution intact For individuals the practical question is how to position for the next phase Waiting for another equity lottery is low probability Learning to evaluate supervise and integrate agents may be a more durable path For companies the question is whether to broaden participation or accept that the model creates a small winner take all class Do you think the AI boom will produce broader wealth distribution over time or will the concentration in 10000 people become permanent Share your view in the comments
By Behind the Tech5 months ago in Futurism
AI Backlash Gains Momentum as Public Sentiment Turns Negative Across Age and Party Lines
Read Time 6 minutes Tags AI Public Opinion Backlash Data Centers AI Policy Public Sentiment AI Industry Tech Regulation If AI were a candidate for political office it would be losing in a landslide Why it matters The AI hype cycle would have you believe the technology is inevitable But AI backlash is growing as people worry it will steal their jobs jack up electricity rates and further enrich the wealthy all while hurting the environment State of play A commencement address went viral this week after Florida real estate executive Gloria Caulfield said artificial intelligence is the next Industrial Revolution sparking a chorus of boos from the crowd The speaker could have avoided the jeers had she checked the latest polls Only 18 percent of young people ages 14 to 29 say they feel hopeful about AI according to a recent Gallup survey The disdain spans generations and political parties An Economist YouGov poll released this week showed over 70 percent of Americans think AI is advancing too quickly with 68 percent of Republicans and 77 percent of Democrats saying it s moving too fast Other YouGov polling shows negative views of AI rising from 34 percent three years ago to just over 50 percent now Executive disconnect Between the lines AI executives aren t doing much to quell the backlash which is already showing signs of slowing the industry Some of them appear unfazed or unaware In previous conversations with Axios AI executives at multiple frontier AI labs were surprised by the negative opinions They see AI as just as inevitable as the rise of the internet Asked about backlash to AI Superhuman Mail CEO Rahul Vohra whose company makes an AI powered email assistant seemed unfamiliar with the premise of the question After hearing about poor polling around AI he responded We don t really see that What they re saying While the tech underlying AI is here to stay What is not inevitable is that these technologies will be embedded in every aspect of our lives become indispensable or replace humans Dr Avriel Epps assistant professor at University of California Riverside said in an email to Axios Nothing in the future is inevitable and no single person company or group gets to decide what will happen in the future Threat to infrastructure and investment Threat level Negative AI sentiment could become a financial liability for AI labs if it continues to curb access to their most valuable resource compute power A record number of data centers which provide the compute AI companies use to answer user queries were canceled in the first quarter of 2026 amid resistance from communities per Heatmap Pro data Public pushback is emerging as a binding constraint particularly around data center buildout Morgan Stanley analysts wrote in a note about market risks associated with the midterms These data center setbacks are sapping confidence among investors according to a note investment bank Jefferies sent to clients Reality check AI has been around for years and is bound to be a central part of American life whatever form it ultimately takes Some version of AI is inevitable but we have choice said Arun Bahl the CEO of Aloe an AI company that builds models designed to be trustworthy Is it the dystopian plot Or can we have tools that humans trust Globally opinion is more positive with the share of respondents expecting AI to do more good than harm rising to 59 percent in 2025 from 55 percent in 2024 according to Stanford data The bottom line The AI industry has a serious PR problem that threatens to inhibit the rapid growth that its leaders have taken for granted Drivers of the backlash One Economic anxiety The dominant concern is job displacement Workers see automation reaching white collar roles faster than past technologies and feel unprepared for the transition Unlike manufacturing losses in the 1990s these cuts affect knowledge workers in suburbs and urban centers where political mobilization is easier Two Energy and environment Data centers require large amounts of electricity and water Residents in multiple states have seen rate increases and water stress linked to new facilities The Guardian reported that 70 percent of Americans oppose building AI datacenters locally and many would prefer a nuclear plant as a neighbor Three Equity and distribution Benefits appear concentrated among a small number of firms and wealthy investors while costs in the form of higher bills job loss and noise are socialized This reinforces perceptions that AI enriches the already wealthy Four Trust deficit Hallucinations in medical notes biased outputs in hiring tools and opaque decision making have eroded confidence Even when errors are caught in testing as with Ontario s audit of 20 AI scribes the public sees a pattern of deploying first and fixing later Why executive responses fall flat One Dismissal of concerns When leaders say they do not see backlash it signals a gap between Silicon Valley and the rest of the country The industry often frames opposition as ignorance or paid protest rather than legitimate concern about cost and risk Two Inevitability framing Telling the public that AI is inevitable removes agency and breeds resentment People accept technology they feel they can shape but reject what feels imposed Three Lack of visible benefits For many the daily experience of AI is worse search results spammy emails and intrusive assistants The productivity gains are real for some but not visible enough to offset the negatives What could shift sentiment One Shared benefits Models that tie datacenter approvals to local hiring grid upgrades and lower rates for residents can convert opposition into support Without that trade the math looks like cost without gain Two Slower safer deployment Taking time to validate systems in high stakes domains like medicine finance and law reduces headline grabbing failures Each hallucinated medical note or biased loan denial becomes a data point for opponents Three Clear limits and governance Public trust rises when there are visible rules on transparency liability and human oversight The EU AI Act and Ontario s audit show that oversight is possible even if imperfect Four Honest communication Acknowledging tradeoffs instead of claiming pure upside would make executives seem credible If the message is we are building powerful tools that require careful guardrails more people listen The political implications One Bipartisan alignment Both Republicans and Democrats agree AI is moving too fast That creates rare common ground for regulation even if the preferred remedies differ Republicans may focus on energy costs and grid stability Democrats on labor and bias Two Midterms as a test Morgan Stanley flagged data center buildout as a midterm risk If candidates run on slowing or redirecting AI infrastructure it could reshape permitting and subsidies Three Global divergence US sentiment is more negative than global averages If the US slows deployment while other countries accelerate it could shift competitive advantage but also reduce domestic friction For the industry the path forward is not more hype but more credibility That means showing how AI lowers costs without raising bills how it augments jobs rather than erases them and how failures are caught before they reach patients students or voters Do you think the AI industry can recover public trust with better governance and transparency or has the backlash already set the ceiling for adoption Share your view in the comments
By Behind the Tech5 months ago in Futurism
AI Medical Scribes Show Hallucinations in Testing Raising Safety Concerns for 5000 Ontario Doctors
Read Time 6 minutes Tags AI in Healthcare Medical Scribes AI Hallucination Patient Safety Ontario Health AI Ethics Clinical Documentation If you ve been to a medical appointment in the past two or three years chances are high that your doctor was using an AI scribe software that listens into the conversation transcribing it and structuring it into the format of medical notes In theory it s a cool idea but pain points abound Earlier this week Ontario s auditor general an accountability officer acting under the Legislative Assembly of Ontario released a special report warning that AI medical scribes were not evaluated adequately and may present fabricated information to medical professionals First reported by Global News the audit took a look at 20 AI scribe platforms and found that all AI scribe systems from the 20 government approved vendors showed one or more inaccuracies at the procurement testing phase such as hallucinations fabrication incorrect information or missing or incomplete information Inaccuracies in medical notes generated by AI Scribe systems could potentially result in inadequate or harmful treatment plans that may potentially impact patient health outcomes the report declared The gap between testing and deployment Muddying the waters Ontario s Minister of Public and Business Service Delivery and Procurement Stephen Crawford noted that the hallucinations were observed during testing by state regulators and had not been recording during actual medical visits Let s be very clear about that s not actually in operational use with doctors that s in the optional stage where we re reviewing the various scribes Crawford told Global News Still the auditor general Shelley Spence noted that the various scribes are nonetheless in use by around 5000 doctors across Ontario Talking to reporters Spence said she went so far as to ask her physician to please look at the transcript when you re done with my own visit That news comes as another AI scribe system OpenEvidence faces growing scrutiny in the US over hallucinations and incomplete answers As several doctors told NBC News for example OpenEvidence can occasionally draw overly strong conclusions from medical studies with relatively small sample sizes While many physicians express appreciation for the new tool it remains to be seen how they fare under real world conditions and how the medical world will judge them once the AI hype wears off Why hallucinations matter in clinical notes One Clinical risk Clinical documentation drives diagnosis treatment plans medication orders and referrals If an AI scribe inserts a symptom that was never mentioned or misses a critical allergy the downstream decision can be harmful The auditor general warned that inaccuracies could potentially result in inadequate or harmful treatment plans Two Liability and trust Physicians remain legally responsible for the note even if it was generated by AI This creates a burden to review and edit every transcript which can erode the time savings that made scribes attractive in the first place If doctors stop reviewing due to fatigue the risk of error compounds Three Evaluation blind spots The audit found problems during procurement testing but not in live use That could mean testing protocols are more adversarial than real visits or it could mean errors are occurring and going undetected The lack of routine post deployment auditing is a gap Four Vendor diversity did not reduce risk All 20 approved vendors showed issues This suggests the problem is not limited to one model or one company but is systemic to how current large language models handle free form medical conversation Patterns seen in other AI deployments One Overconfidence on weak evidence OpenEvidence has been criticized for drawing strong conclusions from small sample studies This mirrors a broader issue where models present probabilistic output with deterministic certainty A physician unfamiliar with the underlying literature may not catch the overreach Two Data leakage and context errors Models can conflate patients mix up temporal sequences or insert plausible but false details These errors are hard to detect because they read naturally and fit the clinical tone Three Feedback loops If inaccurate notes are fed back into training or used for downstream decision support the errors can propagate across systems What would make deployment safer One Mandatory human review Every AI generated note should require a clinician sign off with a clear audit trail showing what was edited and why Review should be tracked as a quality metric not just an optional step Two Real world evaluation Ontario s testing occurred at procurement but ongoing monitoring in live settings is needed Logs of corrections discrepancies and adverse events linked to scribes should be collected and analyzed quarterly Three Clear scope limits Scribes should be restricted to transcription and structure leaving diagnosis and plan generation to the clinician Models should be tuned to refuse when audio is unclear or when critical information is missing rather than fill gaps with plausible guesses Four Vendor transparency Regulators should require vendors to publish error rates on standardized test sets and to disclose training data sources and safety mitigations Black box procurement makes it impossible to assess risk Five Patient notification Patients should be informed when an AI scribe is used and given the right to request a human reviewed transcript This aligns with informed consent norms for other technologies used in care Broader implications for AI in medicine One Automation bias Physicians may over trust AI output especially under time pressure The design of the tool should make errors visible and easy to correct not buried in long transcripts Two Workflow impact If review takes longer than manual note taking the technology fails on its primary value proposition Adoption will stall unless accuracy improves or review workflows are streamlined Three Regulatory precedent Ontario s audit is one of the first government level evaluations of AI scribes at scale Other jurisdictions will likely follow How Canada handles vendor approval monitoring and liability will shape global standards Four Public trust Trust in clinical encounters depends on patients believing the record reflects what was said If hallucinations become public the backlash could slow adoption of beneficial AI tools across healthcare For clinicians the immediate takeaway is to treat AI scribe output as a draft not a final record For health systems the takeaway is to invest in monitoring and governance before scaling For vendors the takeaway is that accuracy on clinical transcription is a prerequisite not a nice to have Do you think AI scribes can become reliable enough for routine use with proper safeguards or is the risk of hallucination too high for clinical documentation Share your view in the comments
By Behind the Tech5 months ago in Futurism
AI Datacenter Expansion Sparks Backlash as Communities Push Back on Cost Water Use and Noise
Read Time 6 minutes Tags AI Infrastructure Datacenters Energy Water Use Community Opposition AI Policy Utility Rates Corporate Personhood Back in 2016 Marco Gutiérrez the Mexican born founder of Latinos for Trump issued an ominous warning to the US My culture is a very dominant culture he said on MSNBC It is imposing and it s causing problems If you don t do something about it you re going to have taco trucks on every corner A decade later I regret to inform you there is not a taco truck on every corner But I am here to issue my own ominous warning about the takeover of America not by immigrant culture but by AI culture To echo Gutiérrez it is imposing and it s causing problems And if we don t do something about it we re going to have datacenters on every corner I m not some sort of data hater OK Datacenters physical facilities housing storage systems servers and network devices are a critical part of powering the internet if they disappeared the modern world would cease to function The banking system would collapse you wouldn t be able to stream Netflix go on social media or most importantly read the Guardian online But while we obviously need datacenters the AI boom and the enormous amounts of computing power it requires has caused their footprint to massively expand and our utility bills to jump When a data center comes online retail customers usually help to foot the electric bill American utilities sought almost thirty billion dollars in retail rate increases in the first half of 2025 the New Yorker explained last year Meanwhile Bloomberg reported on a new study this week that shows power prices on the largest electric grid in the US jumped 76 percent in the first quarter due to rampant demand from data centers Things will only get worse Today datacenters consume 6 percent of electricity supply in the UK and US by 2030 they could account for more than 14 percent of the US s total power demand It s not just how much they cost that s problematic AI datacenters are noisy emit pollution that could harm community health and divert much needed resources Last year for example residents in Fayetteville Georgia noticed low water pressure eventually they discovered a nearby datacenter had taken 30m gallons of water initially without paying for it It is no surprise that a new Gallup poll has found seven in 10 Americans oppose constructing AI datacenters in their local area Indeed most Americans would rather live by a nuclear power plant than a datacenter Of course the people getting filthy rich from AI will never have to live nextdoor to their moneymaking creations and seem fairly blase about the issues associated with their expansion Take the OpenAI CEO Sam Altman for example As AI grows how big do data centers need to be podcaster Theo Von asked Altman last year Is that a concern of you guys Not really judging by his response Altman waxed lyrical about the scale of the datacenter OpenAI was building before saying that while he wasn t sure where things were going he had a lot of guesses I do guess a lot of the world gets covered in datacenters over time Altman said But I don t know because maybe we put them in space I wish I had like more concrete answers for you but like we re stumbling through this In true Silicon Valley fashion while the industry may be stumbling it s regular people getting hurt But forget the regular people Won t anyone think of the poor oppressed datacenters As backlash grows the industry has gone into full on defensive mode The venture capitalist Kevin O Leary for example has claimed that people protesting against a vast datacenter in Utah are not actually concerned they re just paid agitators There are professional protesters that are paid by somebody I don t know who O Leary said in a video on X last week More perniciously we re starting to see more discussion around the idea that AI might have legal personhood and datacenters might have certain rights Earlier this month MLive and 404 Media reported on the University of Michigan s attempts to build a 12bn nuclear weapons research and AI datacenter in Ypsilanti Township officials voted on a year long moratorium on water and sewer services for the facility while it conducted environmental impact studies In response the university claimed the moratorium discriminated against datacenters The proposed moratorium is pretextual and unlawfully discriminatory because it singles out data centers by label rather than by utility impact a legal threat said It seems highly likely that we are going to see more discussion about certain rights being attached to datacenters After all looking at the issue more broadly corporate personhood has been a part of US law for over a century and in recent decades the rights afforded to corporations have steadily expanded The supreme court s 2010 Citizens United ruling found corporations have a right to political speech Then in 2014 the supreme court s Hobby Lobby ruling found some companies should be allowed a religious exemption from requirements to include contraception in employee health plans This significantly broadened the scope of personhood rights by acknowledging a right to corporate religious expression In its 2023 303 Creative LLC v Elenis decision the supreme court similarly held that a website design business owned by an evangelical Christian could refuse service to same sex couples This once again seemed to put the free speech right of corporations over the rights of LGBTQ people not to be discriminated against Today the Supreme Court once again advanced the personhood rights of some corporations to the detriment of actual human beings The Brennan Center for Justice said at the time of the Hobby Lobby judgment We are very concerned about the continued trend of corporations successfully asserting the rights of human beings while injuring the interests of actual human beings They were right to be concerned Given the way things are going in the US corporations seem to have more freedom of speech than university students And it might not be long before datacenters have more rights than women Why resistance is growing One Cost shifting to households When datacenters come online utilities often seek rate increases that hit residential customers American utilities sought almost 30 billion dollars in retail rate increases in the first half of 2025 Power prices on the largest US grid jumped 76 percent in Q1 2026 due to data center demand Two Resource strain Datacenters consume water for cooling and compete with residential and agricultural users The Fayetteville Georgia case where a facility took 30 million gallons without paying initially illustrates the friction Noise and pollution from generators and cooling systems add local health concerns Three Transparency gap Industry often frames facilities as critical infrastructure while communities see externalities without benefits Local opposition is frequently dismissed as NIMBYism or paid protest even when concerns are documented Legal and political flashpoints One Discrimination claims The University of Michigan case shows datacenters invoking discrimination arguments when facing moratoria The legal framing shifts debate from utility impact to protected status risking expansion of corporate personhood into infrastructure Two Precedent from corporate personhood Citizens United Hobby Lobby and 303 Creative show a trend of extending constitutional protections to corporate entities If datacenters gain similar standing local zoning and environmental reviews could face higher legal barriers Three Policy asymmetry While residents face rising bills and resource constraints datacenter operators receive tax incentives and expedited permitting in many jurisdictions The mismatch fuels perception that costs are socialized while profits are privatized What could change the dynamic One Cost allocation reforms Utilities can separate datacenter loads into distinct rate classes so residential customers do not subsidize hyperscale compute Some states are already exploring dedicated tariffs for large load customers Two Water and energy standards Mandating air cooling water recycling and grid interconnection standards reduces local impact Renewable matching requirements can align growth with clean power buildout Three Community benefit agreements Tying approvals to local hiring grid upgrades and public investment creates a path for shared value rather than pure externality Four Transparency on demand growth Regulators need better data on projected datacenter load and location to plan grid upgrades without overbuilding costs into base rates The core tension is that AI requires massive compute infrastructure but the current model pushes costs and risks onto communities while benefits accrue to a small set of firms and investors If the industry wants social license it needs to address utility bills water use and noise directly rather than rely on legal arguments about discrimination Do you think datacenters should be treated as protected infrastructure with special rights or should communities retain full authority to regulate them based on local impact Share your view in the comments
By Behind the Tech5 months ago in Futurism
Stanford Class of 2026 Navigates College in the AI Era as Cheating Normalizes and Career Paths Fracture
Read Time 6 minutes Tags AI in Education Stanford ChatGPT Academic Integrity Cheating AI Employment Higher Education Student Culture At Stanford University where I am a senior tech chief executives are something like rock stars When the Nvidia founder Jensen Huang showed up to give a guest lecture late last month students mobbed him They offered up their laptops and personal workstations desperate for a signature from a kingpin of the artificial intelligence era Last year speaking to the same class Mr Huang gave out shining 4000 graphic cards with his name autographed in gold ink the ultimate dorm room status symbol Stanford has always been a haven for aspiring techies but recent events have taken the school into uncharted territory AI is everything We talk about it at the dining halls and in history classes on dates and while smoking with friends at the gym and in communal dorm bathrooms Nearly all of higher education has been overtaken by this technology and Stanford is a case study in how far it can go For the past four years my classmates and I have been the subjects of a high stakes experiment We are the first college class of the AI era ChatGPT arrived on campus about two months after we did When we graduate next month this technology will have altered our lives in very different ways For some it has opened the door to staggering wealth But for many who came to Stanford just four years ago when a degree seemed like a guaranteed ticket to a high paying job the door has been slammed shut For all of us AI has permanently changed how we think and behave Cheating becomes the default Stanford already had a shaky reputation for integrity when I arrived in 2022 It was the origin place of the Theranos fraudster Elizabeth Holmes now serving a 10 year prison sentence the crypto fraudster Do Kwon now serving a 15 year prison sentence and the founders of Juul which was forced to pay billions for getting kids hooked on vapes All of these scandals were in the news when freshman year began Many of my classmates arrived idealistic and hopeful but among the strivers seeking a path to fortune hustle culture was the accepted way of life Now AI has made deception easier and more remunerative than ever before Cheating has become omnipresent I don t know a single person who hasn t used AI to get through some assignment in college yet the school was at first slow to realize how widespread this would become As freshman year went on some professors suggested that the nuclear option might be called for allowing faculty to proctor in person exams a practice banned at the university for over a century to demonstrate confidence in the honor of students In our tech enabled newly AI powered world students were increasingly fudging just about everything They would embezzle dorm funds to spend on their friends and lie about having Covid to get the UberEats credits that the school offered to those in quarantine Some kids I knew published a paper that claimed a groundbreaking new AI advancement Online sleuths quickly pointed out that it appeared to be just a stolen Chinese model to which the two Stanford co authors responded by blaming the plagiarism on the third author In junior year 49 percent of the 849 computer science majors who responded to an annual campus survey said they would rather cheat on an exam than fail A friend of mine captured the school s ethos while we were discussing the tech hardware and other items our student club neglected to return to corporate sponsors It was all I recall her saying just a little bit of fraud Incentives break the honor code About halfway through freshman year some coding classes started requiring students to sign a declaration I did not utilize ChatGPT to submit each assignment During the first term these attestations began to appear I watched a freshman I knew sign the declaration that he d done his homework without AI as ChatGPT was still open in the next window while on the deck of a yacht party financed by venture capitalists The incentive structures were not aligned toward honesty One could get ahead quickly by cutting corners by focusing on self presentation The money is a big part of it AI has merely accelerated a trend that was already underway at Stanford and has been reflected by many of the country s most corporatized universities Education itself can be seen as a secondary goal to enabling future success frequently defined as a future windfall The first time our college class gathered together was for a convocation ceremony in late September 2022 As one of the speakers droned on I remember looking around and seeing a number of my classmates slumped over in the shade dozing off One of those kids is going to become a billionaire soon it occurred to me I wondered who it would be and how At first the answer seemed to be cryptocurrency and then it was AI ChatGPT changes everything overnight Most of my friends remember where they were and what they were doing when ChatGPT came out on Nov 30 2022 I was nearing the end of my time in Stanford s infamous computer science weeder course CS107 Like organic chemistry for pre meds this was the class that filtered out the true coders from those without the requisite hustle with lots of shameless public tears involved The velocity of change that began on the day ChatGPT entered our lives was stunning A friend texted me a link to the research preview of OpenAI s latest demo Have you seen this yet It s INSANE We began kicking around silly prompts reveling as ChatGPT explained the bubble sort algorithm in the style of a fast talkin wise guy from a 1940s gangster movie It s very good Very very good I messaged my friend Still neither of us understood that this would mark the transformation of AI from a technology to a product Students were probably the earliest wide scale adopters After all it was far and away the quickest route to an A When I took CS107 the only viable way for people to cheat was to seek out a student who d gone through the class before and beg for solutions to the notoriously difficult problem sets There was no alternative to putting in a large amount of work Even if one did obtain the answers from another student engaging by the way in a social act if nothing else the students I knew who did this still spent hours sculpting their stolen code so as not to be caught Few cheated in this most overt fashion back then But a month later any student could instead turn to a chatbot plugging in a prompt alone in a dorm room and mindlessly regurgitating the result I remember the first time I used it feeling an immediate sense of guilt a friend recently told me Now it s just normal Half of the laptops in any lecture seem to be open to ChatGPT or Claude In the beginning experimenting with models was a pastime for the nerds showing off the early access you got to the next frontier large language model was a status symbol and people would come pleading for your authorization keys to try it out for themselves In just a few short years however AI has become a fact of life It s all we talk about my ancient Greek art history professor remarked recently Institutional response and fallout In April 2026 the proctored exam policy finally went into place Because of AI most of us now take our tests by writing in blue books like students a century ago scribbling out answers by hand under keen observation Meanwhile we wonder constantly what will happen next Many students view these large language models as a job threat The machines have gotten so much better at coding that junior engineers can t really compete A Stanford computer science degree means something very different today from what it did when we set foot on campus no longer is there a functional guarantee of an entry level position But for those willing to dream up a company with AI in the name there is a nearly surefire route to monetary gain Perplexity started right when my freshman year began is an example of a wrapper start up In April 2024 it reached a billion dollar valuation two months later that number tripled In May 2025 it announced that it was fund raising at a 14 billion valuation which had grown to 18 billion by July and 20 billion by September Money in Silicon Valley has become a game of almost meaningless numbers bandied about in a breathtakingly casual manner It contributes to the whirlpool effect students at Stanford have felt around tech and lucre if your roommate can drop out and start a nine figure company why shouldn t you profit too Why put all your energy into being a student when it seems like everyone around you is getting rich One time during sophomore year I was working on homework in my dorm common room with an acquaintance when she offhandedly remarked I bought a house in Las Vegas last week It s good for taxes It s hard to put your earbuds in and get right back to your problem set when someone says something like that Cognitive and social costs Yet the same Stanford dropouts who seem to be making the most money right now are often working on the very technology that is worsening life for their former college classmates Emerging research has begun to show what most people feel is obvious Relying on AI for cognitive tasks can reduce one s own intellectual capacity and resilience It s one thing to use it in the workplace but in the classroom difficulty is often precisely the point Sure a robot can lift 600 pounds much more easily than I can but that doesn t much help me if I m trying to work out The same goes for the thinking exercise of education However telling that to students is about as attractive a message as eat your veggies or sleep eight hours It feels like scolding Even in the heart of the Silicon Valley techno utopia most people know that our tech is bad for us or at least that it can be AI is often a tremendous productivity boost yet my friends increasingly refer to both short form video and their AI chat logs in the language of addiction It s becoming baked in shaping our generational character We are a digital generation growing only more attached to the virtual world The technology behind AI is wickedly clever and back when large language models were still a research experiment before they propped up the US economy my friends and I bubbled with excitement I remember trying to explain to my grandfather who has since died that backpropagation a technique vital to AI grew out of attempts to quantitatively prove Freud s theories about the flow of psychic energy I don t think I really sold Gramps on why he should care but to me the development of AI was human genius at its finest and I couldn t wait to open the arXiv links people would text me containing the latest and greatest research The output of a model didn t matter anywhere near as much as how it was designed Now the opposite is true AI is an application that people actually rely on and companies have become less and less transparent about its design What counts is the immediate response you receive when you send a reading to ChatGPT to be summarized on your walk to class Most students call OpenAI s model Chat Many refer to it familiarly consulting with Chat repeatedly over the course of a day letting it decide how to text a situationship and confidently repeating hallucinated assertions while in line at the coffee shop For years online livestreamers have used the word Chat to interact with their audiences asking commenters to tell them what choices to make in video games That students now use the same name for AI feels appropriate What really is the distinction between a nameless faceless human you ll never meet except over the internet and a statistical approximation of the same thing The internet has already allowed us to feel more connected than ever while becoming lonelier than ever AI lets us cut out the human part of human interaction entirely When I was sitting in a recent class on love in French fiction exactly the kind of course that a senior takes before it all comes to an end I listened to the first student presentation entitled Applying the Gale Shapley Algorithm to The Princess of Clèves The enterprising presenters sought to resolve the contretemps of the 1678 romantic novel through a computer science matching algorithm Love was something to be optimized Next to me one student scribbled on a branded notepad from Hudson River Trading a quantitative trading firm where fresh graduates can earn upward of 600000 a year Another had a sticker on her laptop Practice safe CS The class could not have felt more Stanford Living on campus for the past four years has been an eye opening journey Higher education was not equipped for the AI revolution Someday in the future the fully autonomous Clawdbots or Moltbots or whatever people call them will laugh to themselves about this silly interregnum when universities seemed paralyzed trying to bridge the gap between the liberal education of yore and the future in which humans have no monopoly on intelligence For us this was college Do you think universities should redesign assessments for an AI native world or will proctored blue book exams become the norm again Share your view in the comments
By Behind the Tech5 months ago in Futurism
AI Obsession Is Fueling Relationship Burnout as Partners Bear the Emotional Cost of Startup Hustle
Read Time 6 minutes Tags AI Culture Relationship Burnout Work Life Balance Startup Culture Mental Health Gender Dynamics AI Startups If you ve been on TikTok lately you might have come across a viral meme showing yet another dark side of AI its impact on cishet relationship dynamics Variations on the format typically depict a woman working hard at a laptop with a caption explaining that she s burning out so her man can run an AI startup that loses 30K a month It feels like there s a serious side to the jokes since they come in the dark shadow of many grim stories about the tech colliding with established relationships from breaking up marriages to facilitating infidelity Indeed Wired just ran a fascinating feature about how AI is disrupting the dynamics of families and the women getting ground down by the whole situation which the story s author Alessandra Ram memorably terms the sad wives of AI The pattern repeating across households One The ideal worker returns Yana van der Meulen Rodgers chair of labor studies and employment relations at Rutgers University told Wired that it comes down to what economists call the ideal worker career obsessives who believe that pausing even for just five minutes will result in a lack of productivity The phenomenon isn t exclusive to the AI era Take the Gold Rush for example when American men left their families to travel west work and profit quickly Someone who works many hours giving all of themselves to this new force Rodgers told Wired That means less time at home for the partner less time for care work Two Emotional and practical burden For women this can result in feelings of being unheard and many of these sad wives are turning to therapists for support Consulting a series of family counselors Wired found evidence that it s a very real phenomenon that s only getting worse In addition to abandonment these women are also forced to ride the emotional roller coaster of the nascent and chaotic industry With job loss comes some depression Rodgers told Wired Within the household if one person is going through adverse mental health effects around job loss or uncertainty the other naturally becomes the support person Three Intimacy erodes One thing s for sure the disgust the death knell of so many relationships is real If i had to listen to another minute of my husband talking about Claude Code I might have actually died Ram wrote There are two babies in this household now the small human one and the large language model Both demand constant attention Both keep us up at 2 am Why AI startups amplify the problem One Speed and uncertainty AI startups operate under extreme time pressure with funding rounds product launches and model releases compressed into weeks The norm of all in hustle culture maps poorly onto stable family life Founders often treat personal time as expendable in pursuit of a narrow window to capture market share Two Identity fusion When the startup becomes the primary identity the partner becomes secondary Conversations revolve around tokens latency and fundraising rather than shared life goals The AI itself becomes a third participant in the relationship demanding attention and emotional energy Three Financial instability Many AI startups burn cash without revenue creating chronic financial stress Even when the company is funded the volatility of AI valuations and compute costs means income and job security feel precarious Partners absorb that anxiety even if they are not involved in the business This dynamic is not unique to AI The Gold Rush analogy Rodgers used fits because both eras reward risk taking and geographic mobility at the expense of domestic stability What is new is the scale at which software and remote work let founders stay physically present while being mentally absent Broader implications One Gendered impact The meme and Wired feature focus on cishet relationships but the underlying issue affects any household where one partner adopts an all consuming work identity The asymmetry arises when one person s career obsessiveness increases the other s unpaid care and emotional labor Two Mental health spillover Partners report anxiety depression and resentment Therapists quoted in Wired describe clients managing both the absence of their spouse and the emotional fallout from startup volatility This creates a second order effect where the AI boom generates mental health demand outside the tech sector Three Cultural signaling The jokes on TikTok matter because they shape perception When AI obsession becomes a punchline it reduces stigma around naming the problem That can open conversations but it can also entrench cynicism if partners feel unheard What changes would reduce the damage One Boundary setting Teams and investors can normalize protected personal time and discourage 24 7 messaging Founders who model boundaries reduce pressure on employees and partners to do the same Two Shared ownership of risk If partners are bearing financial and emotional risk they need visibility into business health and decision making Excluding them from the reality of the startup increases resentment Three Redefining success Metrics that include team health and relationship stability alongside ARR and user growth would shift incentives Investors can ask about founder well being not just burn rate Four Community support Peer groups for partners of founders provide a space to process the unique stress of living with someone building in a volatile industry Some startup ecosystems already have these programs for founders but few extend them to families For workers in AI the lesson is that technical progress does not occur in a vacuum Relationships require maintenance and the cost of neglecting them is paid in trust and stability For companies the lesson is that burnout at home becomes turnover and distraction at work For observers the lesson is that the social cost of the AI boom is not captured in GPU spend or valuation charts Do you think AI startups can sustain rapid growth without creating this kind of household strain or is the hustle model incompatible with stable family life Share your view in the comments
By Behind the Tech5 months ago in Futurism
Microsoft AI Chief Predicts 18 Months to Automate Most White Collar Work as Debate Over Real Impact Intensifies
Read Time 6 minutes Tags Microsoft AI Automation White Collar Jobs AI Impact Employment AI Agents Mustafa Suleyman For the back half of the 20th century what Fortune founder Henry Luce called The American Century MBA and law degree programs were a ticket to a great office job and a path to the American Dream The 21st century is asking the question What happens when all those office jobs get automated In a conversation with the Financial Times earlier this year the CEO of Microsoft AI Mustafa Suleyman delivered another in a series of predictions from AI leaders that white collar work is on the precipice of a radical transformation thanks to AI His timeline is 18 months until those law school and MBA grads and many less credentialed peers are out of luck Suleyman predicted human level performance on most if not all professional tasks being done by AI Most tasks that involve sitting down at a computer will be fully automated by AI within the next year or 18 months he said naming accounting legal marketing and even project management as vulnerable Suleyman s warning echoed the viral essay of the week a version of which was published at Fortune com by AI researcher Matt Shumer who compared this moment to February 2020 when the pandemic was about to hit America This will be more dramatic though Shumer said The case for rapid automation One Compute driven progress Suleyman cited the exponential growth in computational power as a flashing red signal that AI could replace large swaths of professionals As compute advances he said models will be able to code better than most human coders Shumer and OpenAI CEO Sam Altman have both written about their alarm even sadness at watching their life s work rapidly grow obsolete Two Historical pattern of CEO warnings If Suleyman s warning sounds familiar that s because it was the tune of early 2025 when many CEOs issued similarly apocalyptic prophecies Anthropic CEO Dario Amodei warned last May AI could wipe out half of all entry level white collar jobs though recently changed his tune Ford CEO Jim Farley said AI would cut in half the number of white collar jobs in the US In The Atlantic Josh Tyrangiel argued the US wasn t prepared for the coming AI disruption comparing CEOs recent silence on the subject to seeing a shark fin break the water Three AGI timeline compression But that drumbeat is beginning again with SpaceX CEO Elon Musk saying in Davos in January that he thinks artificial general intelligence AI that matches or exceeds human level intelligence could arrive as early as this year Suleyman was adamant about the technology s potential He thinks organizations will be able to retrofit the technology to perform any required job function enhancing productivity across white collar industries Creating a new model is going to be like creating a podcast or writing a blog he said It is going to be possible to design an AI that suits your requirements for every institution organization and person on the planet Evidence on the ground tells a different story One Limited displacement so far However as AI experts hypothesize about when and if AI will disrupt white collar work the technology thus far has made only a small splash in professional services A 2025 Thomson Reuters report found lawyers accountants and auditors are experimenting with AI for targeted tasks like document review and routine analysis But while the results have shown marginal productivity improvements they fall short of signaling mass job displacement In fact in some instances AI has had the reverse effect making workers less productive A recent study from nonprofit Model Evaluation and Threat Research METR on AI s impact on software developers found the technology actually made the workers tasks take 20 percent longer Two Economic data shows concentration Any returns the economy is seeing are largely confined to the tech industry suggesting that AI disruption has been limited in the real economy Recent research from Apollo Global Management chief economist Torsten Slok found that while profit margins in Big Tech increased by more than 20 percent in the fourth quarter of 2025 the broader Bloomberg 500 Index has seen almost no change A few days earlier Slok had noted that investors do not believe AI will result in higher earnings outside the tech sector citing consensus Wall Street expectations for the S P 500 Three Early signs of job impact Still there are early signs AI is leading to job displacement About 49135 job cuts so far this year were AI related according to employment consultancy Challenger Gray and Christmas While not citing AI as a reason for cuts Microsoft last year let go 15000 workers In a memo released last July following job eliminations CEO Satya Nadella said the company must reimagine our mission for a new era Market reaction has been volatile Despite marginal workforce reductions the markets are reacting violently to the technology s potential In February software stocks suffered a huge selloff out of fears of automation analysts dubbed it the SaaSpocalypse for the software as a service sector The selloff came after Anthropic and OpenAI announced the launch of agentic AI systems for enterprises that perform many of the key functions of SaaS organizations Microsoft s internal strategy One Superintelligence goal Suleyman said his core mission as the steward of Microsoft AI is to achieve superintelligence The CEO wants to achieve AI self sufficiency and reduce its reliance on OpenAI instead prioritizing the construction of the company s independent models This after all is the most important technology of our time Suleyman said We have to develop our own foundation models which are at the absolute frontier Two Mixed track record The three months since he said this haven t aged his take well as mounting evidence shows AI is kind of a bust even as Anthropic s Claude continues to displace OpenAI as the number one model and lead the chase for enterprise revenue But MIT Technology Review featured him in April insisting that AI development won t hit a wall anytime soon Key tensions in the debate One Capability versus adoption AI systems may reach human level performance on many tasks in benchmarks but enterprise adoption faces friction around reliability compliance data integration and workforce readiness The gap between model capability and production deployment remains wide in most regulated industries Two Productivity paradox Early deployments show productivity gains are uneven and sometimes negative The METR study on software developers suggests that without proper workflows and training AI can slow work rather than accelerate it Three Labor market lag Even if automation is technically feasible labor market adjustment takes time Contract cycles union rules professional licensing and organizational inertia slow displacement Labor demand may shift rather than collapse with new roles emerging around AI supervision evaluation and integration For workers the implication is to focus on tasks that involve judgment client relationships and ambiguous problem solving where AI remains a tool rather than a replacement For companies the challenge is to redesign workflows so AI augments rather than disrupts existing teams For policymakers the question is how to prepare for a labor market transition that may be faster than past technology shifts Do you think 18 months is a realistic timeline for broad white collar automation or will adoption barriers keep AI as a productivity aid rather than a replacement Share your view in the comments
By Behind the Tech5 months ago in Futurism
Microsoft and Palantir Offer Two Different AI Bets as Investors Weigh Infrastructure Scale Against Software Growth
Read Time 6 minutes Tags Microsoft Palantir AI Infrastructure Cloud Computing SaaS Valuation Earnings Azure AIP Microsoft MSFT and Palantir PLTR both delivered AI fueled earnings beats this season yet the businesses behind those tickers operate at completely different altitudes Microsoft is the hyperscaler renting out the picks and shovels Palantir is the operating layer that sits on top of enterprise and government data Comparing them right now exposes how Wall Street is pricing two very different bets on the same theme Revenue and growth profiles One Microsoft Azure strength Microsoft s Q3 FY26 report showed Intelligent Cloud revenue of 34681 billion up 30 percent with Azure growing 40 percent The headline number that mattered an AI business at a 37 billion annual run rate up 123 percent Commercial remaining performance obligations sit at 627 billion nearly double a year ago That is a real demand backlog with contractual visibility Two Palantir commercial surge Palantir s Q4 FY25 was a smaller hotter print Revenue grew 70 percent to 141 billion US commercial revenue surged 137 percent and the company posted a Rule of 40 score of 127 percent CEO Alex Karp s commentary stayed characteristically loud calling Palantir an n of 1 while pointing to 426 billion in record TCV closed Satya Nadella s tone was the opposite framing the quarter around delivering cloud and AI infrastructure and solutions for the agentic computing era Capital intensity and business model One Microsoft infrastructure spend The capital story is where the divide gets sharp Microsoft spent 30876 billion on capex in a single quarter up 8439 percent mostly on GPUs data centers and power Two Palantir capital light approach Palantir is capital light SaaS generating 791 million in free cash flow against a much smaller revenue base One company is buying the physics of AI The other is selling the workflow on top Metric comparison Lens Microsoft Palantir Core bet Azure plus Copilot platform AIP Foundry Gotham deployments YoY revenue growth 183 percent 70 percent P E ratio 30 192 Key vulnerability Capex payback timing Valuation and SBC dilution Microsoft s customer base is sprawling and global Palantir s revenue is US heavy with US government at 570 million giving it defense and intelligence exposure that Microsoft does not match in concentration Market reaction and valuation One Stock performance Despite the beat MSFT is down 1549 percent year to date and trades at 40777 Palantir is also lower YTD at down 2349 percent sitting at 136 Polymarket traders see PLTR pinning near 138 with 81 percent probability through this week Two Valuation gap The 192 P E for Palantir only works if commercial TCV keeps compounding at triple digits and the 684 million in FY25 stock based comp remains a dilution overhang PLTR s profile skews to higher beta growth dependent outcomes while MSFT s profile skews to scale visibility and cash generation What matters through 2026 One Microsoft execution risk For Microsoft the open question is whether that 30876 billion quarterly capex earns its keep Azure growth needs to stay above 35 percent to justify the spend The 3178 billion in net income and 627 billion RPO give visibility I cannot get anywhere else at this scale and the analyst target of 56156 suggests Wall Street still sees room Two Palantir growth hurdle For Palantir the bar is the FY26 guide of 7182 to 7198 billion and US commercial topping 3144 billion Karp set those numbers high on purpose Palantir screens as asymmetry while Microsoft screens as ballast On the metrics Microsoft is the steadier core holding today The 192 P E for Palantir requires sustained triple digit commercial compounding to justify Strategic positioning in AI One Microsoft platform play Microsoft is betting on infrastructure scale with 627 billion in commercial remaining performance obligations The company owns the stack from silicon and data centers through Azure to Copilot and enterprise SaaS This creates multiple monetization layers but requires massive upfront investment and long payback cycles Two Palantir application layer play Palantir operates as the software layer on top of enterprise and government data with concentrated US exposure AIP and Foundry deployments convert messy data into actionable workflows without owning the underlying infrastructure This keeps capex low and margins high but ties growth to customer adoption velocity and contract wins Three Risk profiles Microsoft s risk is capex misallocation and payback timing If Azure growth slows below 35 percent the infrastructure spend becomes harder to justify Palantir s risk is valuation compression and dilution If commercial TCV growth slows the high multiple becomes difficult to defend Four Competitive dynamics Both companies benefit from enterprise AI adoption but serve different buying centers Microsoft sells to CIOs and IT leaders who control infrastructure budgets Palantir sells to operational leaders and government agencies seeking decision advantage The two motions can coexist and sometimes partner but they compete for enterprise AI budget share For investors the choice reflects risk tolerance and time horizon Microsoft offers scale cash generation and contractual backlog with moderate valuation Palantir offers faster growth higher volatility and a business model that works only if US commercial expansion continues at triple digit rates Do you see Microsoft s infrastructure moat holding against cloud competitors or does Palantir s software layer have more upside if enterprise AI adoption accelerates Share your view in the comments
By Behind the Tech5 months ago in Futurism
House Bipartisan Talks Aim to Preempt State AI Safety Laws While Debate Over Mandatory Federal Vetting Continues
Read Time 6 minutes Tags AI Regulation Federal Preemption State AI Laws Congress AI Safety Mythos Anthropic AI Policy Bipartisan House talks on expected artificial intelligence legislation are coalescing around a plan to preempt a specific set of state laws that rein in cutting edge AI developers according to two tech lobbyists and three AI policy advocates familiar with the discussions The people familiar who were granted anonymity due to the sensitive and fast moving nature of the talks said the bill would specifically preempt AI safety laws like those recently passed by California and New York which require top AI developers to disclose information about new models in order to identify critical safety or security risks State AI laws that do not regulate model developers are not expected to be preempted What the bill would do One Targeted preemption Reps Jay Obernolte R Calif and Lori Trahan D Mass the two lawmakers behind the talks are negotiating a narrow preemption that would technically apply only to laws that directly regulate the most cutting edge AI development The bill would block state laws requiring top AI developers to disclose information about new models for safety or security risk identification Laws covering issues like kids safety or privacy that do not regulate model developers are not expected to be preempted Two Sunset provision The lawmakers are also discussing a sunset provision that would allow states to once again regulate frontier AI development after two years according to four of the people familiar This structure gives Congress time to establish a federal framework while acknowledging that state action may return if federal rules prove insufficient Three Dispute over mandatory vetting Three of the people said Trahan and Obernolte are struggling to agree on whether a federal vetting regime for advanced AI developers should be compulsory Obernolte is partial to a light touch or even voluntary approach that would let AI companies decide whether to disclose certain information to the government Trahan wants greater accountability for the companies including mandatory data sharing requirements according to the people familiar Why the talks matter now One Mythos and White House pressure The negotiations come as the White House grapples with a similar set of questions posed by the emergence of Mythos a powerful new model developed by top AI firm Anthropic that is reportedly able to find cybersecurity vulnerabilities that human hackers cannot Mythos has set off a scramble at the White House in recent weeks as President Donald Trump mulls an executive order that would create a vetting process for risks posed by advanced AI The debate has to a degree mirrored ongoing talks between Obernolte and Trahan with some in the Trump administration advocating for a laissez faire approach while others push for mandatory requirements or even a pre clearance regime that would require the White House to greenlight new AI models before release Two Industry pressure to avoid a patchwork The AI industry has spent the better part of a year attempting to block what it frames as a growing patchwork of conflicting state AI laws Those efforts have set off a furious backlash from AI safety advocates who say state legislators have the right to protect their citizens from AI harms and that any federal rules that preempt state laws should be significantly stronger than what they re replacing The preemption proposal now under discussion between Trahan and Obernolte is relatively narrow But some safety advocates worry that if it becomes law AI companies will argue in court that new state rules around issues like kids safety or privacy would force them to change how they develop their models and would therefore be blocked It will be a litigation magnet said one AI policy advocate familiar with the talks Political dynamics and risks One Backlash for Trahan Trahan suffered immediate blowback for merely engaging in discussions with Obernolte which were made public shortly after Rep Sam Liccardo Calif a Democrat who represents Silicon Valley and is eager to strike a deal with Republicans withdrew his support from Obernolte s expected proposal Within hours of the news breaking top Democrats in the Massachusetts legislature sent a letter to Trahan warning her against working with Republicans on a bill that would block them from regulating AI Earlier this week a coalition of AI safety advocates and Massachusetts voters launched a petition campaign urging Trahan not to cut a deal that would override AI safeguards in her state Asked about the political risks of talking with Obernolte on Wednesday Trahan told POLITICO that she doesn t think there s anything wrong with having conversations about protecting our national security our economy from cyber security threats in a post Mythos world And that s exactly what we re doing Two Broader implications for federal AI policy The negotiations between the two lawmakers represent the latest attempt to craft federal rules governing AI and follow several failed bids to reach consensus in Congress over guardrails for the technology Spokespeople for Trahan and Obernolte declined to comment Key tradeoffs in the debate One Speed versus safety Proponents argue that a narrow preemption prevents regulatory fragmentation that could slow US AI development and create compliance burdens for startups A uniform federal standard would give companies clarity and preserve US competitiveness against China and other rivals Critics counter that preempting state laws without replacing them with stronger federal requirements creates a regulatory gap States have moved faster than Congress on AI safety and consumer protection Removing that pressure valve could leave gaps in oversight for years Two Voluntary versus mandatory disclosure A voluntary regime lowers compliance costs and may encourage participation from smaller labs But it risks adverse selection where only companies with low risk models opt in Mandatory disclosure creates more accountability but raises concerns about intellectual property protection and administrative burden Three Litigation risk Even with narrow preemption language companies may test the limits in court If a state privacy or child safety law requires changes to model training or evaluation pipelines companies could argue it effectively regulates frontier development and is therefore preempted This would expand the scope of federal preemption beyond congressional intent What to watch next One Compromise on vetting A potential compromise could involve mandatory disclosure for models above a compute or capability threshold while keeping smaller models under a voluntary regime This would focus oversight on frontier systems like Mythos without burdening the broader ecosystem Two Timeline and vehicle Congress has struggled to pass standalone AI legislation in prior sessions The bill may need to attach to a must pass vehicle like defense authorization or appropriations to move before the two year sunset clock starts Three State response If the bill passes California and New York lawmakers may respond by tightening non developer facing laws or by expanding enforcement under existing consumer protection statutes The two year sunset creates a hard deadline for federal action or a reversion to state led regulation For AI companies the outcome determines whether they face fifty different state rules or one federal regime For safety advocates it determines whether federal law sets a floor or a ceiling For the White House it shapes how much control the administration has over frontier model release in the wake of Mythos Do you think a two year federal preemption with a sunset is the right balance between innovation and safety or should states retain authority to regulate frontier AI development Share your view in the comments
By Behind the Tech5 months ago in Futurism
Runway Bets on Video and World Models to Beat Language First AI Labs at Their Own Game
Read Time 6 minutes Tags Runway AI Video Generation World Models Generative AI Robotics Drug Discovery Compute AI Strategy AI video generation startup Runway doesn t have the typical Silicon Valley pedigree No Stanford founders no ex Google founders no nine figure seed round that bought them time to ignore revenue Its three founders two from Chile one from Greece met at NYU s Tisch School of the Arts and built the company in New York Runway also could be depending on who you ask one of the most consequential AI companies today Not because of what it has built but because of what it is trying to build next The core bet For the past several years the AI industry has largely operated on the premise that intelligence lives in language Large language models like OpenAI s ChatGPT and Anthropic s Claude reflect that bet Runway alongside other competitors is making a different one Its founders believe the next form of AI intelligence won t be built from text but from video and world models that learn how the world works not just how humans describe it That distinction sounds academic Its implications are not Runway co founder and co CEO Anastasis Germanidis said training models directly on observational data from the world is the next frontier of AI The companies that get there first he argues won t be the ones that perfected language We re basically bound by our own understanding of reality Germanidis told TechCrunch from Runway s homey sunlight filled headquarters near Union Square Language models are trained on the entire internet on message boards and social media on textbooks distilling the existing human knowledge Germanidis continued But to get beyond that we need to leverage less biased data From video tools to world models Founded in 2018 Runway built its reputation on video generation models including its latest Gen 45 and AI tools that let people turn text prompts into editable cinematic content Today Runway s technology powers production workflows for filmmakers and ad agencies and the company has signed deals with major media players like Lionsgate and AMC Networks Its tools have even been used in films such as Everything Everywhere All At Once Runway is now valued at 53 billion and according to one of its founders added 40 million in annual recurring revenue in the second quarter of 2026 If Runway s bet that video generation is the path to world models pays off the result will be felt from Hollywood to drug discovery If it doesn t Runway risks being outpaced by competitors with far deeper pockets Google chief among them Within the last six months the startup has put its plan into action and expanded beyond video generation launching its first world model in December with plans to launch another this year World models are AI systems that simulate environments well enough to predict how they ll behave Runway isn t alone in its pursuit of turning physics aware video models to world models with near term use cases in interactive entertainment gaming and robotics training Startups Luma and World Labs are on a similar trajectory and Google has pointed its Genie world model in the same direction Everyone is after some version of the same thing AI that solves humanity s hardest problems That s far from Runway s original product but it s the result of both emergent capabilities in the technology and founders who were predisposed to follow where it led Why world models matter For his part Germanidis sees world models as scientific infrastructure The more sensory data and observations you train a single model on the closer you get to a working digital twin of the universe one you can run experiments on faster than any lab could Much of the scientific process is just waiting on results he points out If you could compress that waiting you could compress progress itself If we can build a better scientist than human scientists we can accelerate progress in how we understand the universe and how we solve problems Germanidis said Things like robotics drug discovery and climate modeling the kinds of problems that have stumped researchers for decades Last year Runway launched a robotics unit that Germanidis says has already resulted in real world testing and deployments Germanidis like others sees the field heading toward training a single model on many different modalities text video voice and other sensors and thinks the compounding effect is the point His own moonshot goal for Runway s technology given enough time and resources is biological world models and anti aging research Resources and competition Whether Runway can carry its video dominance into world models is far from settled and the competition isn t waiting around Runway was among the first to develop AI video generation but world models are a different race with deep pocketed and well respected competitors Google former Meta chief scientist Yann LeCun AI s godmother Fei Li and a growing field of startups are all chasing the same goal Kian Katanforoosh CEO of AI skills benchmarking company Workera and a lecturer at Stanford pointed out that no one has yet proven the jump between video intelligence and generalized reasoning via world models but that doesn t mean it s impossible He said that if Runway wants to turn its world model bet into reality it will need to continue gathering resources compute chief among them Runway has deals with CoreWeave and Nvidia but wouldn t confirm whether it has dedicated cluster access the kind of guaranteed large scale compute that training frontier models requires How are you going to build a foundational model without a cluster Katanforoosh asked I don t think anybody can do that Runway has raised 860 million to date including a 315 million round in February from strategic partners like AMD Ventures and Nvidia That s roughly in line with its most immediate competitors Luma AI and World Labs which have raised 900 million and 129 billion respectively according to PitchBook But Runway is also going up against incumbents like OpenAI which has raised around 175 billion per CEO Sam Altman and tech behemoth Google whose parent company Alphabet is worth 486 trillion Google is Runway s biggest threat The company s Veo model competes directly with Runway s video generation business while its Genie world model targets the same longer term territory Runway is racing toward Katanforoosh nodded at OpenAI which shuttered its video platform Sora in March after burning roughly 1 million per day in compute costs with barely 21 million in revenue according to some estimates His point Resources alone don t guarantee survival They don t guarantee it for Runway either Culture and execution Katanforoosh isn t writing Runway off He pointed to AI audio startup ElevenLabs which has outperformed OpenAI and Google on their own benchmarks despite lacking the resources and pedigree of either Runway he argues could follow a similar playbook The comparison isn t lost on Runway s founders Valenzuela says the startup s lack of Bay Area standardization gives them an edge Not only do they have diversity of thought he contends but without Silicon Valley ties they had to be scrappier lacking the war chest many of their peers have access to that would have insulated them from the need to generate revenue early And according to Michelle Kwon Runway s chief operating officer the company isn t in a rush to raise more funds even as compute demands increase with scale Their background has led them to be early to be right more often than not and to build a culture that moves incredibly quickly early investor Michael Dempsey managing partner at Compound told TechCrunch For Valenzuela that culture starts with how he sees the world in the first place He spends whatever free time he has not much as a co CEO and new father reading books including the Chilean poet Nicanor Parra whom he describes as the antithesis of Pablo Neruda less formal less academic holding a view that poetry belongs to the people rather than to rules Rules are just rules they invented Valenzuela said That s a driving force of how we do things at Runway They say Silicon Valley is here and that s where the startups are Why Those are just made up rules Scrub them all and start again Do you think world models trained on video will surpass language models as the path to general intelligence or will text remain the dominant foundation Share your view in the comments
By Behind the Tech5 months ago in Futurism
