Bitcoin Magazine

Bitcoin Mining Firm Luxor Raises $16 Million to Scale AI Compute Trading and Hardware

Seattle-based Luxor Technology Corporation announced a $16 million strategic investment round on Tuesday. CMT Digital, the venture capital arm of Chicago-based CMT Group, led the round. Canaan, MicroBT, Halo, Steelhead, and angel investors also participated. 

The round includes both primary and secondary capital. Luxor describes each investor as an operating partner in the compute supply chain it serves, spanning hardware, capital, and power.

The raise follows the September launch of Luxor AI, a business segment that extends the energy, hardware, software, compute trading, and data stack Luxor built for bitcoin mining into artificial intelligence and high-performance computing. 

Luxor said its hardware brokerage desk has traded $1 billion of ASIC hardware for bitcoin mining. Its energy arm services more than 75 megawatts of load across ERCOT and SPP. Its agentic cloud platform had reached 1.8 million workloads year to date as of September.

The firm plans to use the new capital in four areas: scaling its agentic cloud platform, providing working capital for its AI hardware business, expanding its compute derivative trading and market making, and hiring engineering and go-to-market staff.

The hardware push responds to tightening supply chains. The firm’s AI hardware desk sources GPUs and AI servers through a tiered network of partners. As a Value-Added Reseller to major OEMs, the company says it can access abandoned orders, special allocations, and used markets.

Luxor and AI compute

CMT Digital brings nearly three decades of electronic trading and market-structure experience. Sam Hallene, a partner at CMT Digital, said Luxor understands the energy and infrastructure landscape behind compute, as well as the financial tools customers need to manage risk. He said the team has built that skill over eight years in digital assets and is applying it to AI.

Luxor’s over-the-counter desk already offers cash-settled options, forwards, and backstops on compute-rental indexes. CMT’s backing, the company says, positions Luxor to scale that business as AI compute becomes a tradable market.

Canaan CEO Nangeng Zhang said his company is pursuing long-term power resources in North America as part of its compute-plus-energy strategy, and that joining Luxor’s cap table aligns with those goals. He called the company an important partner across bitcoin mining and the energy industry.

Nick Hansen, Luxor’s CEO and co-founder, summed up the thesis: “Compute markets are converging: the same miners, power sites and capital that served digital mining are now powering AI.”

Earlier this year, the company unveiled Commander, a fleet management platform designed to help Bitcoin mining operators monitor operations, manage energy use, and optimize profitability from a single system. 

Integrated with existing infrastructure, the platform offers real-time fleet monitoring, remote machine controls, and tools that help miners adjust to changing hashrate and electricity market conditions.

Public bitcoin miners had signed more than $70 billion in AI and high-performance computing contracts by the end of March, according to CoinShares data. 

This post Bitcoin Mining Firm Luxor Raises $16 Million to Scale AI Compute Trading and Hardware first appeared on Bitcoin Magazine and is written by Micah Zimmerman.

Bitcoin Magazine

Bitcoin Price Hovers Near $80,000 as Traders Await Fed Decision

Bitcoin price traded near $80,000 on Monday, after falling about 2% earlier in the session. The price has dipped below that level several times in recent days and recovered each time.

Selling pressure has been building. Spot Bitcoin ETFs recorded about $635 million in outflows on October 22. A brief drop below $80,000 last week also triggered roughly $800 million in liquidations of long positions, or bets that prices would rise.

The next major test to look out for is the Federal Reserve’s two-day meeting, which ends October 28. The Fed raised its target range to 3.75% to 4% in September, and its minutes suggested another increase could come by year-end. 

Most recent market odds favor the Fed holding rates steady, though those odds have shifted. Traders are watching Fed Chair Kevin Warsh’s remarks for signs of a December hike.

Higher interest rates typically pull money away from risky assets, including crypto. Broadly speaking, a hold could keep bitcoin price between roughly $80,000 and $88,000. A rate hike could push it toward the lower end of that range.

Bitcoin price backdrop

Geopolitical tensions are also weighing on markets. Oil rose above $100 a barrel in early September as strikes between the U.S. and Iran escalated. Higher energy prices feed into inflation, which affects the Fed’s decisions. 

In tech, Anthropic is reportedly preparing an initial public offering that could value the AI company near $965 billion. The timing has shifted toward the weeks before the November midterm elections, but no date has been confirmed. 

U.S. stocks surged a bit Monday as investors geared up for a busy week of Big Tech earnings and the Federal Reserve’s interest-rate decision. The Nasdaq rose 0.8%, the S&P 500 gained 0.6%, and the Dow climbed 0.3%. 

Alphabet and Microsoft report Wednesday, followed by Amazon Thursday, while markets largely expect the Fed to hold rates steady. 

AI-related stocks rallied following Anthropic’s IPO filing. Tesla jumped 6%, while Meta plans to raise up to $95 billion for AI investments. 

As of writing, bitcoin price trades at $80,215, down nearly $4,000 or 4.75%, on the month. Over the past month, prices have ranged from a high of $86,806.95 to a low of $79,397.76. Bitcoin price peaked above $86,000 in early October, then slid toward $80,000 in the second week of the month. 

A brief rally around October 21 pushed it back near $84,000, but those gains faded within days. Trading volume over the past 24 hours reached about $18.12 billion. 

Bitcoin price remains far below its all-time high of $126,277.05.

This post Bitcoin Price Hovers Near $80,000 as Traders Await Fed Decision first appeared on Bitcoin Magazine and is written by Micah Zimmerman.

Andreessen Horowitz is expanding its crypto team’s investment brief. Chris Dixon’s October 22 announcement renames a16z Crypto as a16z Frontier, with the new fund able to pursue a wider range of emerging technologies while continuing crypto investment.

TL;DR

• A broader mandate changes the set of businesses considered for funding.
• Using a blockchain does not determine a company’s customers or revenue model.
• Commercial demand remains the test for products combining AI and crypto.

The business model becomes more useful than the sector label

The announcement is a change in investment scope. It does not establish how much of future spending will reach any particular technology.

For a founder, the practical question is which problem a prospective investor understands. A payments company using stablecoins may need distribution, banking access and regulatory expertise. A data marketplace may need rights management and reliable buyers. Calling both crypto companies does little to identify the skills that will make either successful.

That difference is already visible in products available today. Circle’s institutional funding documentation deals with reserved balances, settlement and withdrawal routes. These are treasury operations, even though USDC is involved throughout the process.

Similarly, USD.AI distinguishes a governance instrument from the instrument receiving lending income. A project can use tokens at several layers while its underlying commercial task remains financing computing equipment.

The distinction helps explain the variety in BitGo’s move into consumer applications. Infrastructure expertise may support a new product, but it does not eliminate the separate work of attracting users and keeping them engaged.

Combining AI and crypto still needs a paying customer

A broader investment mandate can accommodate companies whose activity does not fit an existing sector neatly. That is useful when a product relies on several technologies at once. It also makes comparison more demanding: a hardware business, a financial intermediary and a software service cannot be evaluated with the same measures.

For a hypothetical AI service purchasing data automatically, blockchain payments might solve a transaction problem. They would not settle whether the purchased data improves the service, whether its use is licensed or whether customers will pay enough to cover the cost.

Those remaining questions determine the business. Faster settlement can improve working capital without creating demand. Automated spending can remove a manual task while also making spending limits and authorization more important.

Payment-fraud screening is one example of the controls that have to accompany easier transfers. And actual payment usage offers more evidence of utility than attaching a new label to a balance sheet.

The rebranding creates room for more kinds of company to enter the investment conversation. Its eventual significance for crypto will depend on the businesses funded, the products they deliver and whether users need the blockchain functions those products contain.

Written by the News Desk and edited by Samuel Rae.

Grayscale’s October 23 research note looks at a more specific crypto-and-AI proposition than a general bet on smarter software: who supplies training data, who finances hardware and who receives payment when computing capacity is used.

TL;DR

• The report highlights DATA, USD.AI and Pearl as examples of different infrastructure roles.
• Hardware financing introduces credit questions alongside blockchain mechanics.
• A useful service and a valuable token remain separate propositions.

Three business models sit behind one broad AI label

In the note, research head Zach Pandl describes DATA’s work on contributed training data, USD.AI’s GPU-backed lending and Pearl’s attempt to combine AI computing with blockchain rewards. These are Grayscale’s selected examples and expectations, not independently proven outcomes for the entire sector.

Each model has a different customer. A data network needs someone willing to license its datasets. A financing protocol needs borrowers who can service debt. A computing network needs useful workloads and a way to measure delivery. Combining them under an AI-token heading can hide more than it reveals.

USD.AI’s documentation makes the credit element explicit. It describes loans secured by GPUs and associated cash flows, with recourse generally limited to the collateral and specified exceptions. Its yield-bearing instrument draws on loan payments and reserve-related income.

That creates familiar underwriting questions in a new distribution format. What happens if the equipment loses value faster than expected? How concentrated are the customers paying to use it? Who can recover and sell collateral if a borrower fails? A blockchain record can improve visibility into a claim without answering all three.

The operating-versus-financing distinction also runs through Bitdeer’s mining and AI disclosures. Machines, funded capacity and realized customer revenue are related measures, but they are not interchangeable.

The token must connect to the underlying activity

Even successful infrastructure does not automatically deliver the same benefit to every associated token. A governance token, a claim on lending income and a token used to pay for a service can have very different rights.

USD.AI’s depositor guide, for example, separates the non-yield-bearing USDai instrument, the income-accruing sUSDai instrument and CHIP governance. Naming the protocol alone does not identify which exposure a holder receives.

The same discipline applies to data. Paying contributors can encourage useful supply, but sustainable demand depends on whether buyers find that data lawful, relevant and difficult to obtain elsewhere. Token incentives can finance initial activity without proving that customers will keep paying once incentives shrink.

Tokenized-asset growth similarly needs to be read through the rights attached to the token. And AI-facing consumer applications still need users who return because a product is useful.

Grayscale’s report provides a set of business models to examine. Adoption, unit economics and enforceable claims will provide the stronger evidence that those models are becoming durable businesses.

Written by the News Desk and edited by Samuel Rae.

Bitcoin Magazine

Bitcoin Core Ends Long Embargoes for Bugs AI Can Rediscover

Bitcoin Core has changed its security disclosure policy to speed up fixes for vulnerabilities that large language models can find with little effort, ending the months-long embargoes those bugs once received.

Maintainers merged the change to the project’s security policy on Thursday. Chaincode Labs developer Antoine Poinsot, who wrote the proposal, announced it in a message to the Bitcoin Development Mailing List the same day.

“We recently experienced an increase in the number of reports for security vulnerabilities that can be easily (re-)discovered,” Poinsot wrote. “This is largely due to LLMs drastically reducing the bar to find certain classes of issues or identify covert fixes.”

How the policy worked before

Bitcoin Core, the software that runs on most nodes in the Bitcoin network, has graded each reported vulnerability by severity and timed its disclosure to match. Low-severity flaws were made public two weeks after the next major version shipped. 

Medium- and high-severity issues stayed under embargo until two weeks after the last affected release reached end of life, a span that could stretch to a year.

The extended timelines gave node operators, many of whom upgrade at a slow pace, time to move to patched software before details became public. In practice, the approach meant fixes went unreleased for older maintained versions and arrived with the next major release.

Poinsot argued that this tradeoff fails for bugs that are easy to rediscover. “The embargo period achieves little but it delays the availability of a fixed release,” the policy reads.

What changes

During triage, Bitcoin Core can flag a vulnerability as “easily discoverable,” a category the policy defines as issues that can be found “with minimal effort, such as through a simple LLM prompt.” The label is open to flaws found by other means as well.

For those issues, the project will backport a fix to every maintained major version, cut point releases as soon as possible and publish a security advisory with some details at the same time as the fixed software. 

Poinsot acknowledged that such fixes will have less testing time before they reach users.

Vulnerabilities outside that category keep the existing severity-based schedule. Critical bugs, those that could enable coin theft at the protocol level, inflate the supply or cause a permanent chain split, remain outside the standard policy and will be handled case by case.

Developer Antoine Riard pushed for an option to ship a fix without an advisory and withhold details on a case-by-case basis. Poinsot declined, writing that alerting users without details “could create more risk unnecessarily” by prompting a rushed migration among users who would otherwise be unaffected.

A summer of AI-driven reports

The change follows months of heightened pressure on Bitcoin’s open-source projects. In late July, a volunteer group of 16 developers known as the Bitcoin Red Team ran AI models across 390 bitcoin repositories in about a day and logged 4,962 findings, including 85 rated critical.

Within days, a BTCPay Server flaw the group had reported in private was exploited on live merchant servers, draining Lightning nodes.

In August, Core Lightning developers told operators to upgrade or disconnect after AI-generated reports to the project proved accurate.

The Red Team effort followed a Coldcard hardware wallet exploit in which attackers drained 594 BTC in 25 minutes on July 30. Losses across four sweeps approached $114 million.

This post Bitcoin Core Ends Long Embargoes for Bugs AI Can Rediscover first appeared on Bitcoin Magazine and is written by Micah Zimmerman.

TL;DR: BitGo has launched a research and product unit focused initially on AI and crypto. Its first projects include Gokubu, a consumer financial app that BitGo says is already live.

BitGo is moving further into the application business with a new unit intended to build products on top of its digital-asset infrastructure.

The company announced BitGo Labs on October 21, describing an initial focus on the intersection of artificial intelligence and crypto. The unit will develop applications and publish research, prototypes and reference implementations.

Its launch includes Gokubu, which BitGo describes as a consumer app combining an AI-assisted card, a personalized financial assistant and automated rewards features. BitGo says the product is live and accepts registrations.

From Financial Infrastructure To A Consumer Product

The move puts a different kind of execution challenge alongside custody and other infrastructure services. An application must persuade users that its convenience is worth adopting, then deliver that convenience consistently.

For an AI-assisted financial product, usefulness also depends on the boundary between suggestions and actions. Showing someone a possible saving is different from making a transaction on their behalf. Permissions, clear account information and understandable costs are part of the product experience, not details that can be left to the technology underneath.

BitGo’s announcement makes ambitious claims about the role crypto infrastructure could play in an economy of autonomous software agents. Those claims describe its strategy. They do not establish that the emerging applications have already generated material revenue or reached a particular scale.

Similarly, the release’s description of Gokubu does not provide a complete account of fees, jurisdictional eligibility or the terms governing every advertised benefit. Readers should treat it as a launch announcement rather than a review of the service.

The meaningful development is that BitGo has assigned a dedicated team to turn its infrastructure into applications and named an initial consumer product. Whether that approach creates a lasting business will depend on the behavior of those products in use, including how clearly they handle the financial decisions customers entrust to them.

Related Reading

• Grayscale Adds BitGo To Hyperliquid Staking ETF Custody Setup
• BitGo Implements 15% Workforce Reduction In Shift To AI Infrastructure
• Visa And Mesh Want Crypto Wallet Balances To Work At 175 Million Merchants

This article was written by the News Desk and edited by Samuel Rae.