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Trusted Autonomy: Why Human-Machine Teams Will Run on Crypto Networks

Autonomous robots may sound like sci-fi concepts that are decades away, but large language models and generative AI now allow machines to plan, learn, and think. More than that — the same software that can win the math olympics and write novels can also control physical robots, allowing one digital persona to operate across the digital and physical worlds. So yes, robots walking around your neighborhood, or working alongside you, will have consistent opinions and actions on X/Twitter, on prediction markets, and in the real world.
But there’s a major gap. How do we integrate thinking machines into human society, from schools, hospitals, factories to our homes and daily life? Most of the systems we’ve built are for other humans and make strong assumptions of having a fingerprint, parents, and a birthdate, none of which are true for thinking machines. There is also broad uncertainty about how to regulate thinking machines — do we outlaw them, pause their development, or try to limit their ability to synthesize human-intelligible emotions (as in the European Union)? Which regional laws apply to a 200B parameter LLM running on a computer in low earth orbit, that’s controlling the actions of a trading bot, or a physical robot in the New York SEC office on Pearl Street?
What is needed is a global system that supports financial transactions, allows humans and computers to come together to vote and set rules, is immutable and public, and is resilient. Fortuitously, thousands of innovators and developers have spent the last 16 years building exactly that — a parallel framework for decentralized governance and finance. From the very beginning, the point was to support “non-geographic communities experimenting with new economic paradigms” by building a system that “doesn’t much care who it talks to” (Satoshi 2/13/09). It’s now more clear what that meant — unlike the rest of the human-focused tech, financial, and regulatory stack, blockchains and smart contracts don’t much care if they are being used by humans or thinking machines, and gracefully accommodate all of us. For this reason, decentralized crypto networks offer the vital infrastructure that’s needed to allow this burgeoning sector to flourish. The benefits will be tangible across healthcare, education and defense.
Several hurdles will need to be overcome. Seamless human<>machine and machine<>machine collaboration is essential — especially in high-stakes environments such as transportation, manufacturing, and logistics. Smart contracts enable autonomous machines to discover one another, communicate securely, and form teams to complete complex tasks. Presumably, low latency data exchange (e.g. among robot taxis) will happen off chain, for example in virtual private networks, but the steps leading up to that, such as discovering humans and robots able to drive you to the airport, are well suited for decentralized markets and actions. Scaling solutions such as Optimism will be critical to accommodate these transactions and traffic.
The fragmented regulations around the world is another factor slowing innovation. While some jurisdictions such as Ontario are ahead of the curve when it comes to autonomous robotics, most are not. Decentralized governance tackles this by establishing programmable, blockchain-based rule sets that deliver much-needed uniformity. Creating global standards for safety, ethics and operations is critical for ensuring that autonomous robots can be rolled out across borders at scale, without compromising safety or compliance.
Decentralized autonomous organizations, otherwise known as DAOs, help accelerate research and development in robotics and AI. Traditional sources of funding are both slow and siloed, holding the industry back. Token-based models such as DeSci DAO platform remove these bottlenecks, while giving everyday investors potential incentives to get involved. Likewise, some of the developing business models for AI involve micropayments and sharing of revenue with data- or model- providers, which can be accommodated with smart contracts.
Combined, these advantages will help fast-track the development of autonomous robots, with a plethora of compelling use cases.
A new paradigm for robotics and thinking machines
It’s easy to fear that cognition is a zero sum game, and that the broad availability of smart machines will directly compete with humans. But the reality is that there are severe shortages of well educated humans in education, healthcare, and many other sectors.
Research by UNESCO recently revealed a worldwide teacher shortage that there’s an «urgent need for 44 million primary and secondary teachers worldwide by 2030» — and that’s before you consider the assistants who offer one-on-one support in classrooms and help struggling students to keep up with their peers. Autonomous robots can deliver huge advantages here, tackling significant shortages across the education sector. Imagine a child being able to learn about a complicated concept with a robot sitting next to them, to walk them through a new concept of skill — reinforcing their understanding about a subject while enhancing their social skills. We are used to humans teaching robots, and this being a one way street, but that is changing.
Meanwhile, the WHO has warned of a «health workforce crisis.» There’s a total shortfall of 7.2 million professionals across 100 countries — and given the world faces an aging population, this gap is expected to accelerate to 12.9 million by 2035. The industry is facing shortages in critical areas like nursing, primary care, and allied health. This crisis is affecting the quality of care patients receive and threatening the ability of healthcare professionals to do their jobs. From monitoring patients with chronic diseases, assisting surgical procedures, to offering companionship for the elderly, autonomous robots can play a crucial role in alleviating the workloads of nurses and doctors. Without being prompted, they can monitor supplies of medicines and equipment — ordering in additional stock when required. When you factor in other use cases such as transporting medical waste, cleaning treatment rooms and assisting in surgeries, it’s clear to see that robotics can drive greater productivity — and consistency — at a time when the healthcare sector needs it.
Autonomous systems are already reshaping the defense sector, primarily involving swarms of drones and naval surface assets, and we’re barely scratching the surface when it comes to the advantages robotics can bring — executing tasks that may be unsafe or impossible for humans.
From prototypes to practical use
All of this may seem abstract and straight out of the 22nd century, but Ethereum is being used today to store decision and action guardrails for AIs and robots, and as reported by Coinbase, AI agents are using crypto to transact amongst themselves.
The open and auditable structure of decentralized crypto networks allows robotics developers to securely share data, models, and breakthroughs. This accelerates the transition of autonomous robots from prototypes to real-world applications, enabling their deployment in critical areas like hospitals and schools faster than ever. When you walk down the street with a humanoid robot, and people stop and ask — “Hey aren’t you scared” you can tell them — no I’m not, because the laws governing this machine’s actions are public and immutable, and then you can give them the a link to the Ethereum contract address where those rules are stored.
Decentralized ledgers can also act as coordination hubs, allowing robots in heterogeneous systems to find one another and coordinate without centralized intermediaries. This is conceptually similar to the standard defence C3 technology (command, communication, and control), except that the infra is decentralized and public. Immutable records ensure that every exchange and action is traceable, creating a trusted foundation for collaboration.
For robot-to-robot interactions, smart contracts streamline task allocation and resource sharing, enabling efficient coordination. In robot-to-human interactions, privacy-centric decentralized systems can secure sensitive data, such as biometric or medical information, fostering trust and accountability.
This new world may invoke fear — what does this all mean for us? — but everyone reading this article has been working on making it come true for almost 2 decades now, by building the infrastructure that will handle governance, teaming, communication, and coordination of humans with thinking machines.
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AI, Mining News: GPU Gold Rush: Why Bitcoin Miners Are Powering AI’s Expansion

When Core Scientific signed a $3.5 billion deal to host artificial intelligence (AI) data centers earlier this year, it wasn’t chasing the next crypto token — it was chasing a steadier paycheck. Once known for its vast fleets of bitcoin mining rigs, the company is now part of a growing trend: converting energy-intensive mining operations into high-performance AI facilities.
Bitcoin miners like Core, Hut 8 (HUT) and TeraWulf (WULF) are swapping ASIC machines — the dedicated bitcoin mining computer — for GPU clusters, driven by the lure of AI’s explosive growth and the harsh economics of crypto mining.
Power play
It’s no secret that bitcoin mining requires an extensive amount of energy, which is the biggest cost of minting a new digital asset.
Back in the 2021 bull run, when the Bitcoin network’s hashrate and difficulty were low, miners were making out like bandits with margins as much as 90%. Then came the brutal crypto winter and the halving event, which slashed the mining reward in half. In 2025, with surging hashrate and energy prices, miners are now struggling to survive with razor-thin margins.
However, the need for power—the biggest input cost—became a blessing in disguise for these miners, who needed a different strategy to diversify their revenue sources.
Due to rising competition for mining, the miners continued to procure more machines to stay afloat, and with it came the need for more megawatts of electricity at a cheaper price. Miners invested heavily in securing these low-cost energy sources, such as hydroelectric or stranded natural gas sites, and developed expertise in managing high-density cooling and electrical systems—skills honed during the crypto boom of the early 2020s.
This is what captured the attention of AI and cloud computing firms. While bitcoin relies on specialized ASICs, AI thrives on versatile GPUs like Nvidia’s H100 series, which require similar high-power environments but for parallel processing tasks in machine learning. Instead of building out data centers from scratch, taking over mining infrastructure, which already has power ready, became a faster way to grow an increasing appetite for AI-related infrastructure.
Essentially, these miners aren’t just pivoting—they’re retrofitting.
The cooling systems, low-cost energy contracts, and power-dense infrastructure they built during the crypto boom now serve a new purpose: feeding the AI models of companies like OpenAI and Google.
Firms like Crusoe Energy sold off mining assets to focus solely on AI, deploying GPU clusters in remote, energy-rich locations that mirror the decentralized ethos of crypto but now fuel centralized AI hyperscalers.
Terraforming AI
Bitcoin mining has effectively «terraformed» the terrain for AI compute by building out scalable, power-efficient infrastructure that AI desperately needs.
As Nicholas Gregory, Board Director at Fragrant Prosperity, noted, «It can be argued bitcoin paved the way for digital dollar payments as can be seen with USDT/Tether. It also looks like bitcoin terraformed data centres for AI/GPU compute.»
This pre-existing «terraforming» allows miners to retrofit facilities quickly, often in under a year, compared to the multi-year timelines for traditional data center builds. Firms like Crusoe Energy sold off mining assets to focus solely on AI, deploying GPU clusters in remote, energy-rich locations that mirror the decentralized ethos of crypto but now fuel centralized AI hyperscalers.
Higher returns
In practice, it means miners can flip a facility in less than a year—far faster than the multi-year timeline of a new data center.
But AI isn’t a cheap upgrade.
Bitcoin mining setups are relatively modest, with costs ranging from $300,000 to $800,000 per megawatt (MW) excluding ASICs, allowing for quick scalability in response to market cycles. Meanwhile, AI infrastructure demands significantly higher capex due to the need for advanced liquid cooling, redundant power systems, and the GPUs themselves, which can cost tens of thousands per unit and face global supply shortages. Despite the steeper upfront costs, AI offers miners up to 25 times more revenue per kilowatt-hour than bitcoin mining, making the pivot economically compelling amid rising energy prices and declining crypto profitability.
A niche industry worth billions
As AI continues to surge and crypto profits tighten, bitcoin mining could become a niche game—one reserved for energy-rich regions or highly efficient players, especially as the next in 2028 could render many operations unprofitable without breakthroughs in efficiency or energy costs.
While projections show the global crypto mining market growing to $3.3 billion by 2030, at a modest 6.9% CAGR, the billions would be overshadowed by AI’s exponential expansion. According to KBV Research, the global AI in mining market is projected to reach $435.94 billion by 2032, expanding at a compound annual growth rate (CAGR) of 40.6%.
With investors already seeing dollar signs in this shift, the broader trend suggests the future is either a hybrid or a full conversion to AI, where stable contracts with hyperscalers promise longevity over crypto’s boom-bust cycles.
This evolution not only repurposes idle assets but also underscores how yesterday’s crypto frontiers are forging tomorrow’s AI empires.
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Bitcoin Climbs as Economy Cracks — Is it Bullish or Bearish?

Bitcoin (BTC) is about 4% higher than it was a week ago—good news for the digital asset but bad news for the economy.
The recent negative tone of the economic data points from last week raised expectations that the Federal Reserve will cut interest rates on Wednesday, making riskier assets such as stocks and bitcoin more attractive.
Let’s recap the data that backs up that thesis.
The most important one, the U.S. CPI figures, came out on Thursday. The headline rate was slightly higher than expected, a sign inflation might be stickier than anticipated.
Before that, we had Tuesday’s revisions to job data. The world’s largest economy created almost 1 million fewer jobs than reported in the year ended March, the largest downward revision in the country’s history.
The figures followed the much-watched monthly jobs report, which was released the previous Friday. The U.S. added just 22,000 jobs in August, with unemployment rising to 4.3%, the Bureau of Labor Statistics said. Initial jobless claims rose 27,000 to 263,000 — the highest since October 2021.
Higher inflation and fewer jobs are not great for the U.S. economy, so it’s no surprise that the word «stagflation» is starting to creep back into macroeconomic commentary.
Against this backdrop, bitcoin—considered a risk asset by Wall Street—continued grinding higher, topping $116,000 on Friday and almost closing the CME futures gap at 117,300 from August.
Not a surprise, as traders are also bidding up the biggest risk assets: equities. Just take a look at the S&P 500 index, which closed at a record for the second day on the hope of a rate cut.
So how should traders think about BTC’s price chart?
To this chart enthusiast, price action remains constructive, with higher lows forming from the September bottom of $107,500. The 200-day moving average has climbed to $102,083, while the Short-Term Holder Realized Price — often used as support in bull markets — rose to a record $109,668.
Bitcoin-linked stocks: A mixed bag
However, bitcoin’s weekly positive price action didn’t help Strategy (MSTR), the largest of the bitcoin treasury companies, whose shares were about flat for the week. Its rivals performed better: MARA Holdings (MARA) 7% and XXI (CEP) 4%.
Strategy (MSTR) has underperformed bitcoin year-to-date and continues to hover below its 200-day moving average, currently $355. At Thursday’s close of $326, it’s testing a key long-term support level seen back in September 2024 and April 2025.
The company’s mNAV premium has compressed to below 1.5x when accounting for outstanding convertible debt and preferred stock, or roughly 1.3x based solely on equity value.
Preferred stock issuance remains muted, with only $17 million tapped across STRK and STRF this week, meaning that the bulk of at-the-money issuance is still flowing through common shares. According to the company, options are now listed and trading for all four perpetual preferred stocks, a development that could provide additional yield on the dividend.
Bullish catalysts for crypto stocks?
The CME’s FedWatch tool shows traders expect a 25 basis-point U.S. interest-rate cut in September and have priced in a total of three rate cuts by year-end.
That’s a sign risk sentiment could tilt back toward growth and crypto-linked equities, underlined by the 10-year U.S. Treasury briefly breaking below 4% this week.
Still, the dollar index (DXY) continues to hold multiyear support, a potential inflection point worth watching.
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Fed’s Sept. 17 Rate Cut Could Spark Short-Term Jitters but Supercharge Bitcoin, Gold and Stocks Long Term

Investors are counting down to the Federal Reserve’s Sept. 17 meeting, where markets expect a quarter-point rate cut that could trigger short-term volatility but potentially fuel longer-term gains across risk assets.
The economic backdrop highlights the Fed’s delicate balancing act.
According to the latest CPI report released by the U.S. Bureau of Labor Statistics on Thursday, consumer prices rose 0.4% in August, lifting the annual CPI rate to 2.9% from 2.7% in July, as shelter, food, and gasoline pushed costs higher. Core CPI also climbed 0.3%, extending its steady pace of recent months.
Producer prices told a similar story: per the latest PPI report released on Wednesday, the headline PPI index slipped 0.1% in August but remained 2.6% higher than a year earlier, while core PPI advanced 2.8%, the largest yearly increase since March. Together, the reports underscore stubborn inflationary pressure even as growth slows.
The labor market has softened further.
Nonfarm payrolls increased by just 22,000 in August, with federal government and energy sector job losses offsetting modest gains in health care. Unemployment held at 4.3%, while labor force participation remained stuck at 62.3%.
Revisions showed June and July job growth was weaker than initially reported, reinforcing signs of cooling momentum. Average hourly earnings still rose 3.7% year over year, keeping wage pressures alive.
Bond markets have adjusted accordingly. The 2-year Treasury yield sits at 3.56%, while the 10-year is at 4.07%, leaving the curve modestly inverted. Futures traders see a 93% chance of a 25 basis point cut, according to CME FedWatch.
If the Fed limits its move to just 25 bps, investors may react with a “buy the rumor, sell the news” response, since markets have already priced in relief.
Equities are testing record levels.
Equities are testing record levels. The S&P 500 closed Friday at 6,584 after rising 1.6% for the week, its best since early August. The index’s one-month chart shows a strong rebound from its late-August pullback, underscoring bullish sentiment heading into Fed week.
The Nasdaq Composite also notched five straight record highs, ending at 22,141, powered by gains in megacap tech stocks, while the Dow slipped below 46,000 but still booked a weekly advance.
Crypto and commodities have rallied alongside.
Bitcoin is trading at $115,234, below its Aug. 14 all-time high near $124,000 but still firmly higher in 2025, with the global crypto market cap now $4.14 trillion.
Gold has surged to $3,643 per ounce, near record highs, with its one-month chart showing a steady upward trajectory as investors price in lower real yields and seek inflation hedges.
Gold has climbed steadily toward record highs, while bitcoin has consolidated below its August peak, reflecting ongoing demand for alternative stores of value.
Historical precedent supports the cautious optimism.
Analysis from the Kobeissi Letter — reported in an X thread posted Saturday — citing Carson Research, shows that in 20 of 20 prior cases since 1980 where the Fed cut rates within 2% of S&P 500 all-time highs, the index was higher one year later, averaging gains of nearly 14%.
The shorter term is less predictable: in 11 of those 22 instances, stocks fell in the month following the cut. Kobeissi argues this time could follow a similar pattern — initial turbulence followed by longer-term gains as rate relief amplifies the momentum behind assets like equities, bitcoin, and gold.
The broader setup explains why traders are watching the Sept. 17 announcement closely.
Cutting rates while inflation edges higher and stocks hover at records risks denting credibility, yet staying on hold could spook markets that have already priced in easing. Either way, the Fed’s message on growth, inflation, and its policy outlook will likely shape the trajectory of markets for months to come.
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