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AI assistants are incredibly efficient, but they can be a little predictable. Sometimes it takes an unexpected prompt to solicit a useful response. From disaster-movie logic to unusual cross-pollination, here are five techniques you can use to get better answers from your AI chatbot.
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E-Commerce
Napheesa Collier is more than just a WNBA star who is critical of her league and its leadership. The Minnesota Lynx player is a vice president of the players union, which means she will be sitting across from WNBA Commissioner Cathy Engelbert at the negotiating table ahead of an Oct. 31 deadline to reach a new collective bargaining agreement. If that doesn’t cause enough tension, Collier is also a co-founder of Unrivaled, a three-on-three womens basketball league that plays in the winter and features WNBA stars. That could give her additional leverage to try to press the WNBA as talks unfold. Here’s a look at some of the implications of Collier’s headline-grabbing comments. Player negotiations with the WNBA are already tense. Could they get worse? As an executive on the negotiating team, Collier will have a loud voice in the room when in-person negotiations between the two sides continue. She was at the face-to-face meeting at All-Star weekend in July that included dozens of players. There have been meetings since, but players haven’t really been able to attend because they’ve still been in season. Were working hard to make sure that we are putting ourselves in the best position to negotiate for what we think is fair,” said Collier, who has torn ligaments in her left ankle. We have a lot of meetings internally to make sure were on the same page and were all lockstep for this. Just making sure were super aligned. There also is the trust factor. During her comments at an end-of-season media session this week, Collier revealed conversations that were to remain private that she had had with the commissioner in February. That could undermine trust that is often needed to carry out negotiations. For all the faults that Collier cited in her prepared comments, Engelbert has delivered on many of her promises since coming into the league in 2019. She will have added six expansion teams by 2030 and secured a major new media rights deal for the next decade that will bring in more than $2.2 billion. Engelbert also had the league pay for a full charter flight program this season that the players hope will be added to the new CBA to address concerns about issues ranging from safety to travel time. The commissioner has said all along that the league is hoping for a transformational agreement that includes significantly increased player salaries and benefits. There’s little reason for Collier’s remarks to detract from that goal. How are other players responding to Collier’s comments? Players across the league backed Collier either on social media or at Game 5 of the WNBA semifinals series between Las Vegas and Indiana that the Aces won in overtime. WNBA MVP Aja Wilson said she was appreciative of Collier and the union for standing up for the players. Im grateful to have those type of people to be able to continue to speak up for us, Wilson said after the Aces advanced to the WNBA Finals. Im going to ride with Phee always. Obviously, shes a business girlie and she has her own stuff going on, but moving forward, weve gotta continue to stand on business as we talk about this CBA negotiation. Other players, including Rookie of the Year Paige Bueckers, backed Collier on social media, calling her “Queen Phee” in an Instagram Story while the song Pink Pony Club plays in the background. What do the negotiations mean for free agency? Nearly every player not on a rookie contract will be a free agent this offseason, hoping to cash in on a potential giant leap in the league’s salary structure. Free agency usually has taken place in January, with players meeting with teams and able to sign in February. Players have been able to work out and get treatment for injuries at their former team’s facility in the offseason before becoming free agents. In a worst-case scenario where owners decided to lock out the players or the players decided to go on strike, those courtesies would go away. Could Collier’s Unrivaled league give players more leverage? The 3-on-3 league will start its second season in January and already has expanded to 54 players and added two new teams. The domestic league, made up entirely of WNBA players, now gives players another option to earn money, which would lessen the impact of a lockout or strike. Last season, players in the league had an average salary of more than $220,000, which was close to the maximum base salary in the WNBA. Unrivaled will add Bueckers to an already loaded roster that includes Collier, Breanna Stewart, and Angel Reese. It also has set itself up for the future by offering NIL deals to many of the top college players. Doug Feinberg, AP basketball writer
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E-Commerce
Picture a data center on the edge of a desert plateau. Inside, row after row of servers glow and buzz, moving air through vast cooling towers, consuming more electricity than the surrounding towns combined. This is not science fiction. It is the reality of the vast AI compute clusters, often described as AI supercomputers for their sheer scale, that train todays most advanced models. Strictly speaking, these are not supercomputers in the classical sense. Traditional supercomputers are highly specialized machines designed for scientific simulations such as climate modeling, nuclear physics, or astrophysics, tuned for parallelized code across millions of cores. What drives AI, by contrast, are massive clusters of GPUs or custom accelerators (Nvidia H100s, Google TPUs, etc.) connected through high-bandwidth interconnections, optimized for the matrix multiplications at the heart of deep learning. They are not solving equations for weather forecasts: they are churning through trillions of tokens to predict the next word. Still, the nickname sticks, because their performance, energy demands, and costs are comparable to, or beyond, the worlds fastest scientific machines. And the implications are just as profound. A recent study of 500 AI compute systems worldwide found that their performance is doubling every nine months, while both cost and power requirements double every year. At this pace, the frontier of artificial intelligence is not simply about better algorithms or smarter architectures. It is about who can afford, power, and cool these gigantic machines, and who cannot. The exponential moat When performance doubles every nine months but cost doubles every 12, you create an exponential moat: each leap forward pushes the next frontier further out of reach for all but a handful of players. This is not the familiar story of open-source vs. closed-source models: it is more fundamental. If you cannot access the compute substrate (the hardware, electricity, cooling, and fabs required to train the next generation ) you are not even in the race. Universities cannot keep up. Small startups cannot keep up. Even many governments cannot keep up. The study shows a stark concentration of capability: the most powerful AI clusters are concentrated in a few corporations, effectively privatizing access to the cutting edge of machine intelligence. Once compute becomes the bottleneck, the invisible hand of the market does not produce diversity. It produces monopoly. Centralization vs. democratization The rhetoric around AI often emphasizes democratization: tools made available to everyone, small actors empowered, creativity unleashed. But in practice, the power to shape AIs trajectory is shifting toward the owners of massive compute farms. They decide which models are feasible, which experiments get run, which approaches receive billions of tokens of training. This is not just a matter of money. It is about infrastructure as governance. When only three or four firms control the largest AI clusters, they effectively control the boundaries of the possible. If your idea requires training a trillion-parameter model from scratch, and you are not inside one of those firms, your idea remains just that: an idea. Geopolitics of compute Governments are beginning to notice. At the 2025 Paris AI Action Summit, nations pledged billions to upgrade national AI infrastructure. France, Germany, and the U.K. are each moving to expand sovereign compute capacity. The United States has launched large-scale initiatives to accelerate domestic chip production, and China, as always, is playing its own game, pouring resources into massive wind and solar buildouts to guarantee not only chips, but the cheap electricity to feed them. Europe, as usual, is caught in the middle. Its regulatory frameworks may be more advanced, but its ability to deploy AI at scale depends on whether it can secure energy and compute on competitive terms. Without that, AI sovereignty is rhetoric, not reality. And yet, there is a darker irony here. Even as governments race to assert sovereignty, the real winners of the AI arms race may be corporations, not nations. Control over compute is concentrating so quickly in the private sector that we are edging closer to a scenario long depicted in science fiction: corporations wielding more power than states, not only in markets but in shaping the very trajectory of human knowledge. The balance of authority between governments and companies is shifting, and this time, it is not fiction. Environmental reckoning There is also a physical cost. Training one frontier model can require as much electricity as a small city uses in a year. Cooling towers demand enormous volumes of water, and while much of it is returned to the cycle, siting matters: in water-scarce regions, the strain cn be significant. The carbon footprint is similarly uneven. A model trained on grids dominated by coal or gas produces orders of magnitude more emissions than one trained on grids powered by renewables. In this sense, the AI sustainability debate is really an energy debate. Models are not green or dirty by themselves. They are as green or as dirty as the electrons that feed them. What efficiency cannot buy Efficiency alone will not solve this problem. Each generation of chips gets faster, each architecture more optimized, but the aggregate demand continues to rise faster than the gains. Every watt saved at the micro level is consumed by the macro expansion of ambition. If anything, efficiency makes the arms race worse, because it lowers the cost per experiment and encourages even more experiments. The result is a treadmill: more compute, more power, more cost, more centralization. What to demand If we want to avoid a future in which AIs destiny is set by the boardrooms of three companies and the ministries of two superpowers, we need to treat compute as a public concern. That means demanding: Transparency about who owns and operates the largest clusters. Auditability of usage: what models are being trained, for what purposes. Shared infrastructure, funded publicly or through consortia, so that researchers and smaller firms can experiment without asking permission from trillion-dollar corporations. Energy accountability, requiring operators to disclose not just aggregate consumption but sources, emissions, and water footprints in real time. The debate should not stop at which model is safest or which dataset is fair. It should extend to who controls the machines that make the models possible in the first place. The machines behind the machines The next control point in AI isnt software: its hardware. The massive compute clusters that train the models are now the real arbiters of progress. They decide whats possible, whats practical, and who gets to play. If history teaches anything, it is that when power centralizes at this scale, accountability rarely follows. Without deliberate interventions, we risk an AI ecosystem where innovation is bottlenecked, oversight is optional, and the costs, from financial or environmental to human, are hidden until it is too late. The arms race of AI supercomputers is already underway. The only question is whether society chooses to watch passively as the future of intelligence is privatized, or whether we recognize that the machines behind the machines deserve just as much scrutiny as the algorithms they enable.
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E-Commerce
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