Datacenex Recruitment

AI data centres can warm surrounding areas by up to 9.1°C says @newscientist

@Shruti_0810 LLM

This is insane 😳 Most people are just using AI tools Very few actually understand how they work So I collected Stanford’s complete LLM curriculum and turned it into a step-by-step learning path Worth over $500 Giving it away free for the first 4,500 people Transformers → Training → Alignment → Agents → Evaluation Study this once and you’ll stop guessing with prompts and start thinking like a real AI engineer How to get it: Follow must (so i can dm you) Rt and comment 'LLM'

The AI gold rush and its risks forbes.com/sites/investor…

Data centers emerge as growing wedge issue in midterm races thehill.com/homenews/campa…

🚨 BREAKING: Anthropic quietly dropped a 32-page playbook on building Claude Skills. Skills let you teach Claude your exact workflow once. It executes it every time after that. Across Claude.ai, Claude Code, and the API. No more re-explaining. No more inconsistent output. This is how AI goes from chatbot to custom operating system. PDF: resources.anthropic.com/hubfs/The-Comp…

Striking image from the new Anthropic labor market impact report.

We are not in a bubble. The amount of money going into AI infrastructure right now is huge, but it’s being financed in a fundamentally different way from a classic market bubble. Instead of being built on easy credit and hype, it’s being funded mostly by real cash flow from large, profitable companies. The biggest spenders are the major cloud platforms such as “the hyperscalers". These companies already generate massive amounts of operating cash every year. They are paying for rising AI capex directly from their own balance sheets, not from borrowing on a risky scale. According to Goldman Sachs, hyperscalers now make up a very large share of total S&P 500 capital spending. By 2026, total S&P 500 cash spending is expected to reach roughly $4.4 trillion, with most of that growth coming from investment in equipment, infrastructure, and R&D. In other words, corporate spending is shifting toward long-term, productive investments rather than short-term financial engineering like buybacks. When Jensen Huang talks about $3–4 trillion of AI infrastructure by 2030, that’s not a random, hyped-up number. Major banks like JPMorgan Chase are running detailed models to figure out how that investment could realistically be financed. Their analysis shows that while it’s a big number, it can be covered through a mix of internal cash flow growth, rising private capital investment, and moderate use of debt. What makes this build-out credible is that AI investment is already producing measurable productivity gains. Independent studies, including work by McKinsey & Company, estimate that AI could create trillions of dollars in value annually across industries. Companies deploying AI are reporting clear time savings per worker, better output per dollar spent, and faster production cycles. That means a portion of today’s capex is directly buying future earnings potential. Another important difference from past bubbles is who’s putting up the money. A lot of the financing is coming from private markets and infrastructure investors. This capital is mostly being directed at real, income-producing assets. Things like data centers, power generation, fiber networks, and chips. These are tangible investments with collateral and long-term contracts. This matters because in a true bubble, prices float far above any underlying income or asset value. Here, the investments are anchored to physical infrastructure and real returns. During the Dot-com bubble of the late 1990s and early 2000s, huge sums of money poured into internet companies that had no revenue, no profits, and no real plan to ever make money. A lot of that funding came from speculative capital and debt. When the hype broke, there was nothing solid underneath to support valuations and the market collapsed. Today looks very different. The macroeconomic and policy backdrop supports productive investment. Tax incentives make equipment and R&D cheaper. Large corporations have strong cash positions and can shift money from buybacks to capex without relying on risky borrowing. That kind of investment raises future output which is the opposite of the 1990s, when many companies were burning cash on unproven ad-driven models that never scaled profitably. This doesn’t mean there’s no risk. There are concentrated exposures among a few very large companies, plus challenges around power supply, grid constraints, and high valuations in some smaller or early-stage AI names. These can create sharp corrections in specific parts of the market. But the key ingredients of a system-wide bubble, excessive speculative credit, leverage spread across many sectors, and massive investment in assets with no economic value are largely missing from the AI capex core. What’s happening instead looks more like the early phase of a structural shift in how companies spend their money. Moving from financial engineering (buybacks) toward building real assets that raise productivity and earnings.

@readswithravi Fear and Loathing

What’s one book that’s worth rereading every year?

#Bitcoin fixes this.

@NikkeiAsia Considering China own roughly 80% of the precious mineral mines worldwide, this is a trade war against China. Who will simply control supply to raise prices. The consumer pays the tariffs ultimately

Trump orders tariff probe on all U.S. critical mineral imports s.nikkei.com/42Ixidz

@junglefamous_ Is this who I think it is?? lol

BEAVERSTRETCHER account is 14-0 all time. The BS is choosey. Jungle takes losses, w/grace. However, BEAVERSTRETCHER IS RUTHLESS & his tradition in excellence is everything. BEAVERSTRETCHER is 3X Long $Trump w/bids placed strategically to $1. Margin padded to “-“ or $0. Suket 🦫

Pick a title and build a business

Energy limitations in the US are a bigger bottleneck for AI advancement than the availability of GPUs. Decentralized distributed training is the answer. This fact is not priced in. benzinga.com/markets/crypto…

Big Breaking News! Reliance Jio and Polygon Labs Join hands together to explore Web3 technologies. This is a significant development, considering Jio has 450 million users and is the largest telecom player in the world. Congratulations, team @0xPolygon. This is huge!

Ex-Google CEO Eric Schmidt tells UK PM Keir Starmer that building more AI data centers and investing in the energy infrastructure to power them will paradoxically solve energy issues much quicker

President-elect Donald Trump announced a $20 billion investment for data centers in the United States by the Emirati company DAMAC Properties, which is led by billionaire Hussain Sajwani.

Charted: The Survival Rate of U.S. Businesses (2013-2023) 💼 🔍Discover more visual insights daily on the @VoronoiApp. voronoiapp.com/business/This-…