Is it not enough to simply watch the beautiful game, unadorned?

These days you might use an AI chatbot to keep abreast of what’s happening in the World Cup. And that AI chatbot, in a sign of the times, might try to shove prediction market odds in your face as another way of ostensibly keeping you up to speed. Because are you really getting the full picture if you don’t know where a bunch of gamblers fall on the outcome?

This is exactly what OpenAI is doing. The Sam Altman-led company quietly struck a deal with Kalshi, which lets you bet on outcomes far beyond sports, to show its prediction market data in ChatGPT, the New York Times reported.

It’s perhaps the inevitable melding of two of the most divisive innovations to come out of the tech industry in recent years.

Searching France and Spain on ChatGPT ahead of their quarterfinal clash on Tuesday returned a graphic that showed that Les Bleus had a 60 percent chance of winning, according to the reporting. (We hope no one acted on that information.) Asking about the England and Argentina game on Wednesday showed that the Three Lions had a 54 percent chance of coming out on top.

Neither side promoted the deal, and the graphic is tellingly light on branding. There are no logos and no outbound links. The only sign of the collab is a small notice in the bottom left corner stating, “Source: Kalshi.”

This is the first partnership of its kind for OpenAI. The company recently updated its help page to stress that users “cannot place bets through ChatGPT,” with the Kalshi data being limited to “queries related to the 2026 World Cup,” according to the NYT.

Zooming out, it’s another sign of prediction markets laundering their image by glomming themselves onto other, more credible brands. In January, Kalshi partnered with CNN to provide its real-time prediction data on the news network’s broadcasts. Its rival Polymarket entered into a similar partnership with Dow Jones, the publisher of The Wall Street Journal, that same month. Both have also partnered with Google to show their data in search results.

Unlike traditional betting platforms, prediction markets don’t frame what they provide as gambling: they provide markets, not bets. They allow you to wager on all kinds of real-life events, ranging from the geopolitical outcomes to the completely trivial. Polymarket lets you bet on the destruction caused by wildfires and the use of nuclear weapons, for example, while also giving you the chance to make a buck on whether Cristiano Ronaldo will cry at the World Cup. (He did.)

Their fuzzy legal framework, plus their Wild West approach to gambling, has led to numerous controversies. Suspiciously timed bets on massive events like the US’s capture of ousted Venezuelan president Nicolás Maduro have raised concerns of rampant insider trading and put pressure on lawmakers to crackdown on the platforms. Arrests have been made in some cases, but these have been rare.

Put simply, for OpenAI, it may be somewhat risky to associate itself with all this baggage, which might be why the Kalshi collaboration is very limited — at least for now.

More on: Grim New Prediction Market Lets Gamblers Bet on Raging Wildfires

The post OpenAI Strikes Bold Deal With Kalshi to Mix Together the Most Hated Technologies in Existence: AI and Prediction Markets appeared first on Futurism.

Whenever big data center developers roll into town, their pitch ends up sounding something like this: hand over your land and your resources, throw in a decade of government subsidies, and we’ll give your ailing little burg some jobs, tax revenue, and a shiny new facility to boost the local economy.

Sounds like a no-brainer, right? Not so fast, cowboy.

In theory, more tax revenue means money for schools, roads, and first responders. In practice, states and counties are waving those taxes in order to entice tech companies to set up shop — while existing campuses put major strain on local communities. One analysis found that Georgia, Virginia, and Texas each lose over $1 billion a year to state data center incentives, while at least fourteen states don’t even disclose data center tax breaks in the first place.

The down-stream impacts don’t really materialize either. Researchers at Georgia Tech found that in rural areas, data centers typically employ fewer than 100 permanent workers and are likely to import specialized services from outside the community.

While this can temporarily boost unemployment numbers, any broader, long-term impacts are not guaranteed and are highly dependent on local conditions. As the Georgia Tech researchers explain, “the sweeping job and wage growth often promised during local recruitment efforts is unlikely to arrive on its own.”

But the jobs creation claim is where the pitch really falls apart. In Iowa, the Cedar Rapids Economic Development Center recently disclosed that two ongoing data center projects — one for Google carrying a price tag of at least $576 million and another for Blackstone firm QTS starting at $750 million — are contractually required to create a combined minimum of just 61 permanent jobs.

In other words, for $1.3 billion in combined capital coming into the city, Cedar Rapids is only guaranteed 61 permanent positions, a spend of $21.3 million per job. That’s even worse than the $136 million Ark Data Center going up in Northeastern Ohio, which is projected to create a whopping ten full-time jobs when all is said and done.

And while Cedar Rapids brags that these developments will bring “thousands of construction and trade jobs,” that may not be to the city’s benefit. Not only are data center construction jobs temporary — and therefore highly dependent on the whims of notoriously unreliable data center developers — they’re also putting quite the squeeze on the construction market.

As a recent analysis by the firm Turner & Townsend found, the rush to build so many data centers has created a “two-speed construction market,” where top-dollar AI projects drive up costs for other developments like housing, while sucking up the skilled labor needed in other sectors.

That report found that 87 percent of global construction markets are experiencing shortages of mechanical, electrical, and plumbing (MEP) specialists, while the cost and lead-time of acquiring MEP components rises. That’s great news for MEP contractors, but the trade-off is higher costs for everything else we might want to build.

That’s before we even mention how disastrous data centers are for the environment — making it all the more insulting that taxpayers are forced to foot the bill for the tech industry’s vastly exaggerated promises.

More on data centers: The Pollution Being Churned Out by AI Data Centers Is So Severe That It’s Almost Incomprehensible

The post The Economics of Data Centers Creating Jobs Are So Bad That They Sound Like a Joke appeared first on Futurism.

With each passing day without an AI labor revolution, the tech industry’s pricing scheme for AI is becoming more and more disconnected from reality — so much so that even its biggest clients are starting to revolt.

Speaking during an interview on CNBC‘s “Squawk on the Street” segment earlier this week, CEO of cybersecurity giant Palo Alto Networks Nikesh Arora implored the tech industry to lower the cost of AI.

During the segment, the chief executive argued that the cost to use large language models (LLMs) has to drop by 20 percent by 2027 — and 90 percent by 2028 — for the tech to be useful to enterprises.

“We need to see the pricing for AI come down,” Arora said.

Zooming out, Arora’s pleas for cheaper AI are instructive. For your average CEO, the ostensible value of any AI tool is in its ability to automate human workers and either slash payroll costs or keep them low — hence the trillions of dollars capitalists have poured into the industry.

There’s just one problem: AI, or more specifically, LLMs, can’t seem to make automation happen in an effective way, and some argue they never will. Instead, AI has largely become a tool to discipline labor, to squeeze more work out of every worker and keep margins high.

Unfortunately for the CEOs of the world, the rising cost of AI threatens to disrupt even this less-than-optimal arrangement.

Approaching the issue from the opposite end — though without the drama of a CEO experiencing buyer’s remorse — is tech critic Ed Zitron. In a separate interview with the same CNBC panel, Zitron argued that the AI industry is a “$10 to $30 billion [total addressable market] industry pretending to be a $1 trillion one.”

The critic’s diagnosis is essentially the same: the tech industry’s pricing assumes a level of AI demand and value creation that doesn’t exist. The difference between the two is that Arora needs that to change in order to justify his outrageous AI bill, forcing a confession in line with one of the tech industry’s fiercest critics.

More on AI: Companies That Embraced AI Are Now Rotting Away in a Very Specific Way

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