A tech startup called Skyfall AI is set to do the most tech startup thing imaginable: toy with an unwitting group of employees’ careers by turning their company into a giant AI experiment.
First, the plan is to buy a small tech business, like an e-commerce or B2B SaaS company, for as much as $1 million. Then the shotcallers at Skyfall will appoint an AI as the CEO, put it in charge of running the entire operation, and dust off their hands while wishing the employees they just subjugated to an AI model all the best.
Skyfall’s human CEO and cofounder Sam Pasupalak frames this as key to realizing the company’s mission to “democratize the power of a CEO to all the small businesses around the world.”
“If you want a truly autonomous business, you have to buy a business and operate the whole thing end to end,” Pasupalak told Forbes in an interview. “Unless you run a business with minimal human intervention, you’ll never know whether an autonomous enterprise is actually possible.”
Unlike many AI firms that’re focused on building AI agents that perform specific workers’ tasks, including sales and coding, Skyfall wants to set itself apart by pursuing a virtual CEO that can help with the big picture decision-making needed to run a company.
The AI model will have plenty of responsibilities, like overseeing pricing, marketing, customer support, finance, and operations, according to Forbes. The goal? Gradually reduce human involvement, all while doubling the company’s revenue.
That’s an ambitious goal. But the SkyFall guys are confident that their AI is ready for the real world after an unusual test: it excelled at the video game RollerCoaster Tycoon, a management sim where you oversee an amusement park. Good enough, right?
“That gave us confidence that we could operate inside a simulated business. The next step is buying a real company,” Pasupalak said.
They’re not prepared to let an AI completely run loose, though, stressing that even when AI hypothetically takes over a lot of a company’s operations, humans should still provide accountability.
“Trust is still important, and you probably don’t want to relinquish actual responsibility to an AI,” chief technology officer Kaheer Suleman told Forbes. “Humans should always remain responsible in some capacity.”
Skyfall is arguably realizing an idea some tech CEOs are already playing with. Meta’s Mark Zuckerberg is reportedly training a “CEO AI agent” to help him do his job, as well as creating a virtual AI clone of the millennial founder so he can be omnipresent through the company. And Block’s Jack Dorsey recently revealed his vision to create a management hierarchy in which everyone in his company reports to him through a central AI — what he styled as the “intelligence layer.”
All those examples illustrate a disturbing trend: CEOs can tout AI’s supposedly wondrous ability to streamline operations all they want, but it’s clear the tech is being used to extend their authority over the rank and file. If they can’t literally be everywhere at once, their all-seeing AI systems can.
Skyfall’s wouldn’t be the first stunt involving AI running a business. Researchers at Andon Labs put a Google Gemini-powered AI model in charge of running an entire coffee shop in Stockholm, which proceeded to blow through most of its budget in a single month. The AI safety firm also let Anthropic’s Claude run a vending machine, allowing it to set prices and choose what to stock. It quickly devolved into chaos after taking employee requests and splurging on bizarre items like tungsten cubes.
Let’s hope for the sake of the employees that Skyfall’s experiment fares a little better. Or maybe not, if it paves the way to a world where AI runs everything.
“We want all businesses to be run autonomously because we believe people should spend their time on things they’re passionate about — not operational tasks,” Pasupalak told Forbes. “We want to build the autonomous era.”
More on AI: AI CEOs Facing Full-on Revolt From Employees
The post Tech Bros Acquiring Entire Company So They Can Appoint an AI as Its CEO appeared first on Futurism.
SenseTime has launched the Galaxy Project, teaming with nearly 20 partners to scale domestic AI chip infrastructure in China.
In a keynote titled ‘Intelligent Transformation and Symbiosis,’ Yang Fan – the company’s co-founder and president of its Large Device Business Group – laid out what SenseTime describes as a closed loop connecting chip-level technology, ecosystem partnerships, and commercial deployment for domestically-produced AI computing power.
Alongside the Galaxy Project, SenseTime signed a space computing agreement with satellite manufacturer Guoxing Aerospace and struck a research partnership with five institutions – including the Shanghai Artificial Intelligence Laboratory – aimed at scientific computing applications.
Yang framed the timing around three converging trends: token demand climbing across enterprise deployments, industrial AI adoption catching up with consumer-facing use cases, and domestic chip commercialisation reaching a point where intelligent computing centres built on Chinese silicon can be stood up at pace.
However, whether that window is as open as SenseTime claims depends heavily on numbers the company has not had independently verified.
Token throughput figures come with a large asteriskSenseTime says its large-scale device platform now processes an average of 2.42 trillion tokens daily, and the company projects that figure will climb 25-fold to 10 trillion tokens per day by the fourth quarter of 2026. That’s a forecast, not a measured result, and enterprise buyers evaluating SenseTime’s infrastructure should treat it as such until quarterly figures start landing.
The cost-effectiveness claims attached to that growth are similarly self-reported. SenseTime says its heterogeneous hybrid inference technology delivers an 85–152 percent increase in Model FLOPs Utilisation on mainstream domestic chips, alongside inference cost-effectiveness the company puts at 1.25x that of Nvidia’s H-series parts.
Compared with domestic homogeneous inference setups, SenseTime claims a 2.5x increase in token output at equivalent cost, a jump it says pushes optimised hybrid inference clusters past what the industry previously regarded as the minimum profitability threshold for domestic computing power.
None of these figures come with third-party benchmarking, and the gap between a vendor’s optimised test cluster and a customer’s production environment – with its uneven data pipelines and delayed firmware updates – tends to be where such numbers soften.
Adaptability claims and the multi-chip problemDomestic AI chips have historically struggled with a fragmented software stack: models trained for one architecture often require rework to run on another. SenseTime says it has built a full-stack adaptation layer spanning models, frameworks, operators, toolchains, and hardware to address that, with the aim of letting customers migrate workloads across domestic chip vendors without extensive rewrites.
The company points to two applied examples. In an AI4S long-sequence protein prediction workload, SenseTime says fused operator optimisation cut overall prediction time by a factor of three. In AIGC video generation, it claims a 93 percent multi-card parallel acceleration ratio for domestic chips running DiT models, alongside what it describes as zero-cost migration for mainstream AI development tools.
These are the kinds of figures that read well in a sandbox test and matter far more once they’re stress-tested against real customer pipelines running mixed hardware generations.
Energy metrics get a new benchmark nameSenseTime introduced a metric it calls Tokens Per Watt, positioned as a replacement yardstick for measuring AI data centre efficiency, alongside a Computing-Power Collaboration Agent that handles resource scheduling, electricity price prediction, and energy storage optimisation across what the company describes as an eight-level data system with five decision chains.
Combining compute, electricity pricing, and automated scheduling, SenseTime claims an 80 percent increase in token output per unit of electricity cost, average power prices 10 percent below comparable regional data centres, and 96 percent accuracy in computing load prediction.
These are claims worth watching over the next several quarters rather than accepting at face value. Electricity price arbitrage and load forecasting accuracy tend to perform differently once a system runs through a full seasonal cycle with genuine demand volatility, rather than the conditions under which a vendor typically runs its pilot.
Impressive partner roster spans chipmakers to component suppliersThe Galaxy Project’s stated ecosystem includes domestic chip vendors Cambricon, Muxi, Hygon, Huawei Ascend, Moore Threads, Sunrise, and Biren Technology, component partner Xizhi Technology, and infrastructure firms including Silicon Motion, Qujing Technology, Zhongke Jiahe, Qingcheng Jizhi, Sophon Information, and Jiliu Technology.
SenseTime says the plan covers construction of one “token factory,” five computing clusters at what it calls “10,000-calorie” scale, joint work across ten technology directions, and support for 200 AI startups.
“Domestic production is not simply about replacing individual chips, but rather a collaborative effort across the entire chain of China’s innovation capabilities, from chips and components to infrastructure and application scenarios,” Yang said.
Space, optical, and quantum computing bets look further outBeyond near-term infrastructure, SenseTime outlined work on optical computing for data centre efficiency, quantum computing applications in AI optimisation, and a space computing partnership with Guoxing Aerospace to build what the two companies call the SenseTime Space Computing Constellation.
SenseTime’s plan calls for a first satellite launch in 2026, building toward thousands of computing satellites and computing capacity in the tens of thousands of petabytes by 2030.
Yang argued the value extends past raw capability, framing space-based computing as a way to extend the reach of Chinese AI services into weak-network environments such as maritime operations and disaster response, and by extension to support China’s AI exports internationally.
That 2030 target sits five years out, and satellite computing deployments of this scale have no precedent to measure the timeline against.
Physical infrastructure spans Shanghai to RiyadhOn the ground, SenseTime says its Shanghai facility runs the country’s first data centre rated at what it calls “5A” intelligent computing level, handling over 20 trillion tokens daily across more than 20 industries. A Yancheng site has launched with an initial 3,000 petaflops of capacity focused on energy, manufacturing, and low-altitude economy applications.
In Hong Kong, SenseTime is building what it describes as the territory’s largest domestic intelligent computing centre, targeting 40,000 petaflops by 2030. The company also plans what it calls China’s first overseas domestic computing cluster in Saudi Arabia, positioned as a full-stack domestic computing base for the Middle East.
On the research side, SenseTime’s tie-up with the Shanghai AI Laboratory, Beijing Zhongguancun Academy, Shenzhen Hetao Academy, the Shanghai Algorithm Innovation Research Institute, and Shanghai Jiao Tong University’s AI school aims to build a shared platform spanning compute, tooling, and model capability for life sciences, materials science, and manufacturing research. Yang called AI for Science “a key lever for paradigm innovation in basic research,” tying the initiative to China’s broader “Artificial Intelligence+” policy push.
SenseTime’s forecast of 10 trillion tokens per day by Q4 2026 is the figure to track against whatever the company reports when that quarter actually closes.
See also:Kimi K3 open-weight model: China’s biggest AI is a bet on memory, not compute
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