CORECORE Price

Core Scientific reported a $41.9 million payment to terminate a contract with Block and its subsidiary Proto for the supply of Bitcoin mining chips. This move finalizes its exit from plans to grow its hash rate, shifting its business model entirely towards AI colocation. The agreement, announced in July 2024, was for 3nm chips providing roughly 15 EH/s of hash rate. Following the cancellation, Core Scientific stated it will no longer invest in new mining hardware to maintain or expand its cryptocurrency mining capacity. Instead, it will generate cash flow from its existing mining fleet while repurposing its data centers, potentially selling or decommissioning ASIC miners. In Q2 2026, revenue from AI colocation surged to $136.7 million from $10.6 million a year earlier, representing 83% of total revenue. In contrast, its own Bitcoin mining revenue fell 66% to $21.5 million. Quarterly Bitcoin production dropped 53% year-over-year. AI colocation capacity reached 437 MW by mid-July 2026, with CoreWeave accounting for all current hosting revenue and approximately 77% of Core Scientific's total revenue in H1 2026. The company also announced a partnership with AMD for up to 2.5 GW of potential data center capacity, with initial 15-year agreements for about 530 MW estimated to bring in over $14 billion in contract revenue. Total Q2 revenue grew to $164.2 million, while capital expenditures jumped to $797.5 million. As of June 30, 2026, the company reported long-term debt of $4.3 billion and free liquidity of $1.82 billion. The shift aligns with a broader industry trend of major Bitcoin miners accelerating a pivot to AI amid pressure on Bitcoin mining profitability.

Analysts from Bernstein revealed details of a deal between Core Scientific and AMD with a potential total value of over $14 billion. According to the report, initial contracts for 530 MW of capacity could generate this revenue over 15 years, with AMD acting as a credit guarantor for part of the bitcoin miner's infrastructure. The partnership, announced on July 28, has the potential to allocate up to 2.5 GW of data center capacity for AI. Bernstein broke down the 530 MW into 377 MW of direct triple-net lease for AMD and 152 MW for an unnamed cloud provider backed by AMD's credit. This structure is seen as lowering financing costs and counterparty risk. AMD also received warrants to buy 30 million Core Scientific shares at $23.47 each, which vest upon reaching the 2.5 GW target. Average annual revenue from the deal is estimated at around $0.9 billion, or about $1.8 million per megawatt, which is 5-25% below recent AI hosting deals by other miners. However, the 377 MW triple-net lease for AMD carries a margin close to 100%. Core Scientific expects capital expenditures for the deal to be $11-12 million per MW, totaling about $6 billion. Bernstein views this partnership as a new phase in the transformation of former bitcoin miners into AI infrastructure operators, with AI chipmakers like AMD now acting as direct anchor tenants. Recent similar deals include Hut 8 allocating 704 MW to a tenant believed to be Nvidia, and AMD reserving 200 MW with Riot Platforms. Core Scientific also paid Block $41.9 million to terminate a mining chip supply contract as part of its accelerated diversification into AI.

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

Strong corporate earnings are providing robust support for the U.S. equity market. John Flood, a partner at Goldman Sachs, argues that with market positioning becoming "cleaner," the S&P 500 could set a new all-time high this year, driven fundamentally by corporate profits. According to Goldman Sachs data, the S&P 500's trailing second-quarter EPS growth reached 45% year-over-year, significantly surpassing the initial consensus of 22%. Excluding non-recurring items, such as certain investment-related income, the adjusted EPS growth rate remains a strong 26%, accelerating from Q1 and marking the fastest pace since 2021. This strong performance has led analysts to upwardly revise forward earnings estimates for 2027, with positive revisions breadth across most sectors. Concurrently, market sentiment and positioning have cooled from earlier highs. Goldman's sentiment and positioning indicators have retreated, hedge funds have notably reduced leverage, and retail investor leverage is also moderating. Flood views this "de-foaming" of market positioning as creating a healthier foundation for further market gains. From a valuation perspective, U.S. stocks appear relatively inexpensive compared to other major global markets. Furthermore, Flood notes that the primary benefits of the AI super-cycle have yet to fully materialize. However, a seasonal risk is noted: historical data shows muted median returns for the S&P 500 from early August to Election Day in mid-term election years. Goldman's conclusion is that the positive earnings outlook provides strong support for the bullish case, but its sustainability remains a key variable for the market's trajectory.

OpenAI releases a 62-page core manuscript detailing how its AI model independently solved ten major, longstanding mathematical problems considered "Fields Medal-level." The breakthroughs, achieved at an estimated computational cost of only $2,000, include advancing a 46-year-old upper bound for high-dimensional sphere packing and explicitly constructing a non-sofic group—a 27-year-old open question. The manuscript, titled "How the Ideas Came Together," was authored autonomously by the AI (reportedly the next-generation model Astra). It reconstructs the reasoning process for each problem: identifying initial promising paths, obstacles encountered, pivotal shifts in perspective, and the final decisive insights. For sphere packing, the AI moved beyond traditional linear programming limits by employing Mellin transforms and harmonic measure to refine the density exponent. For the non-sofic group, the key was resolving a "crucial mismatch" between having many expansion graphs and needing one, via a controlled median-based function. OpenAI researcher Mo Bavarian reflects on the rapid progress from AI struggling with grade-school math to solving profound mathematical conjectures, calling this moment "more surreal than any before" and akin to the eve of a technological singularity.