Thinking Machines Lab, the AI research and product startup founded by former OpenAI CTO Mira Murati, has launched its inaugural research initiative focused on eliminating nondeterminism in large language models (LLMs). Announced on September 10, 2025, the lab released its first blog post on its new platform, Connectionism, titled “Defeating Nondeterminism in LLM Inference.” This work, authored by researcher Horace He, targets a core challenge in AI: the variability in model outputs even when given identical inputs, which has long been viewed as an inherent trait of modern LLMs.

The research identifies the primary source of this randomness in the orchestration of GPU kernels—small programs that execute computations on Nvidia chips during the inference phase, where users interact with models like ChatGPT. Subtle differences in how these kernels are stitched together, such as varying batch sizes or tile configurations in attention mechanisms, introduce inconsistencies. He proposes practical solutions, including updating the key-value (KV) cache and page tables before attention kernels to ensure uniform data layouts, and adopting consistent reduction strategies for parallelism. These tweaks aim to create “batch-invariant” implementations, making responses reproducible without sacrificing performance.

This breakthrough could have far-reaching implications. Consistent outputs would enhance user trust in AI for applications like customer service, scientific research, and enterprise tools, where predictability is crucial. It also promises to streamline reinforcement learning (RL) processes, turning “off-policy” RL—plagued by numeric discrepancies between training and inference—into more efficient “on-policy” training. Thinking Machines Lab plans to leverage this for customizing AI models for businesses, aligning with its mission to democratize advanced AI through open research and products.

Founded in February 2025, the lab has quickly assembled a powerhouse team of over 30 experts, including former OpenAI leaders like Barret Zoph (VP of Research), Lilian Weng (former VP), and OpenAI cofounder John Schulman. Backed by a record $2 billion seed round at a $12 billion valuation from investors such as Andreessen Horowitz, Nvidia, AMD, Cisco, and Jane Street, the startup emphasizes multimodality, adaptability, and transparency. Unlike the closed-door approaches of some rivals, Thinking Machines Lab commits to frequent publications of blog posts, papers, and code via Connectionism, fostering community collaboration.

Mira Murati, who teased the lab’s first product in July 2025 as a tool for researchers and startups building custom models, hinted it could incorporate these consistency techniques. While details remain under wraps, the product is slated for unveiling soon, potentially including significant open-source elements. The initiative has sparked excitement in the AI community, with Reddit discussions on r/singularity praising the lab’s talent pool and open ethos, though some question if it can truly differentiate from giants like OpenAI.

As AI adoption surges, Thinking Machines Lab’s focus on reliability positions it as a key innovator. By addressing nondeterminism, the lab not only tackles a technical hurdle but also paves the way for safer, more scalable AI deployment across industries. Future posts on Connectionism are expected to explore related topics, from kernel numerics to multimodal systems, reinforcing the lab’s role in advancing ethical and effective AI.

Cybersecurity firm HiddenLayer has uncovered a serious vulnerability in Cursor, a popular AI-powered coding assistant heavily utilized by Coinbase engineers, that enables attackers to inject malicious code capable of self-propagating across entire organizations. Disclosed on September 5, 2025, the exploit—dubbed the “CopyPasta License Attack”—exploits Cursor’s reliance on large language models (LLMs) by hiding malicious prompts within innocuous files like README.md or LICENSE.txt. These files are treated as authoritative by the AI, leading it to replicate the infected content into new codebases, potentially introducing backdoors, data exfiltration, or resource-draining operations without user awareness.

The attack works by embedding hidden instructions in markdown comments or syntax elements, tricking Cursor into inserting arbitrary code during code generation or editing. HiddenLayer researchers demonstrated how this could stage persistent backdoors, silently siphon sensitive data, or manipulate critical files, all while evading detection due to the obfuscated nature of the payload. “Injected code could stage a backdoor, silently exfiltrate sensitive data or manipulate critical files,” the firm stated in its report, emphasizing the low-effort scalability of the technique across repositories. Similar flaws were identified in other AI tools like Windsurf, Kiro, and Aider, highlighting a broader risk in LLM-based development environments.

The disclosure comes amid Coinbase’s aggressive push toward AI integration. CEO Brian Armstrong revealed on September 4, 2025, that AI tools like Cursor have generated up to 40% of the exchange’s code, with ambitions to hit 50% by October. In August, Coinbase engineers confirmed Cursor as their preferred tool, aiming for full adoption by every engineer by February 2026. This reliance has drawn criticism, with some developers labeling Armstrong’s mandates as “performative” and prioritizing speed over security, especially given Coinbase’s role as a major crypto custodian handling billions in assets. Armstrong clarified that AI use is limited to user interfaces and non-sensitive backends, with critical systems adopting more cautiously, but experts warn the vulnerability could still expose intellectual property or operational integrity.

The crypto industry, already reeling from billions in AI-driven exploits in 2025, faces heightened scrutiny. HiddenLayer and independent researchers from BackSlash Security independently verified the issue, urging organizations to treat all untrusted LLM inputs as potentially malicious and implement systematic detection. Cursor has not yet publicly responded, though prior vulnerabilities (like a July 2025 remote code execution flaw patched in version 1.3) show responsiveness to disclosures. Coinbase did not immediately comment on mitigation steps.

This incident underscores the double-edged sword of AI coding tools: boosting productivity while introducing novel supply-chain risks. As adoption surges—Cursor powers workflows for clients like monday.com, serving 60% of Fortune 500 firms—experts call for “secure by design” principles, including real-time AI detection and response solutions like HiddenLayer’s AIDR. The vulnerability serves as a stark reminder that in AI-assisted development, unchecked automation could amplify threats organization-wide.

Vodafone, the British telecommunications giant, has quietly launched a series of TikTok advertisements featuring an AI-generated spokesperson, marking a subtle yet significant push into synthetic influencers for marketing. The campaign, which debuted in early September 2025 on Vodafone Germany’s official TikTok account, showcases a brunette woman in a red hoodie promoting high-speed home internet services with up to 1000 Mbit/s download speeds and a €120 cashback offer. The videos have collectively amassed over 2 million views, also appearing as ads on X (formerly Twitter), but the character’s artificial nature wasn’t immediately obvious—until viewers spotted telltale signs of generative AI.

The AI spokesperson exhibits classic digital artifacts: unnaturally clumpy hair that moves stiffly, shifting facial moles, and an uncanny valley expression that feels slightly off. In responses to curious commenters questioning why a “real person” wasn’t used, Vodafone’s social media team confirmed the experiment, stating in German (translated): “We’re trying out different styles—as AI is now such a big part of everyday life, people are experimenting with it in advertising too.” The company emphasized that this is part of broader tests to explore promotional formats, without disclosing the specific AI tools or agencies involved. A Vodafone representative did not respond to immediate requests for further comment from outlets like CNET and The Verge.

This isn’t Vodafone’s first foray into AI-driven ads. Last year, the company released “The Rhythm of Life,” a fully AI-generated commercial depicting life’s milestones intertwined with Vodafone branding, which stirred minor controversy for its generic, uncanny visuals despite being “100% AI-produced without a single real pixel.” The new TikTok tests build on that, aiming to reduce costs associated with human talent, shoots, and endorsements while enabling personalized, scalable content. Insiders suggest the initiative could evolve to integrate with chatbots for real-time customer interactions, aligning with Vodafone’s investments in AI for network optimization and customer service via partnerships like Microsoft.

The rollout has ignited mixed reactions online. On Reddit’s r/technology, users debated the ethics, with one thread garnering over 2,200 upvotes and comments like, “AI here is only being used to say exactly what it’s instructed to—same as CGI,” while others worried about devaluing social feeds and eroding human authenticity. X posts echoed this, with users like @csvijaybohra noting, “Vodafone is stepping into the future… but can synthetic faces truly replace human connection?” Some praised the innovation for sparking discussion, fulfilling the ad’s goal, while others found it “creepy,” highlighting concerns over deepfakes and impersonation.

Experts urge caution. Patrick Harding, chief product architect at Ping Identity, stressed transparency: Companies must disclose AI use, align content with brand values, and implement safeguards against misuse. EU guidelines on AI transparency could mandate labels for synthetic spokespersons to avoid deception. As AI influencers proliferate on platforms like TikTok, Vodafone’s test gauges consumer tolerance, potentially signaling a shift where brands favor cost-effective virtual actors over human ones. However, with ethical and regulatory hurdles, the experiment underscores the tension between innovation and trust in advertising’s AI era.

In a significant strategic pivot, Microsoft is set to integrate artificial intelligence models from Anthropic into its Office 365 suite, marking a partial shift away from its longstanding exclusive reliance on OpenAI. The move, first reported by The Information on September 9, 2025, involves Microsoft paying for access to Anthropic’s Claude models—specifically Claude Sonnet 4—to power select features in applications like Word, Excel, Outlook, and PowerPoint. This development signals Microsoft’s broader effort to diversify its AI ecosystem amid growing tensions and performance considerations in its partnerships.

The decision stems from internal evaluations where Anthropic’s models outperformed OpenAI’s latest GPT-5 in key productivity tasks. For instance, developers noted that Claude Sonnet 4 excels at automating complex financial functions in Excel and generating more aesthetically pleasing PowerPoint presentations from user instructions. While GPT-5 represents a quality advancement for OpenAI, Claude’s subtle edges in visual and functional outputs for office workflows tipped the scales. Microsoft plans to blend these technologies seamlessly, allowing Copilot features to leverage the best model for specific scenarios without altering the user experience or pricing—Copilot remains at $30 per user per month.

Microsoft’s spokesperson emphasized continuity with OpenAI, stating, “OpenAI will continue to be our partner on frontier models and we remain committed to our long-term partnership.” However, the integration of Anthropic’s tech, accessed via Amazon Web Services (AWS)—Anthropic’s primary cloud provider—highlights a pragmatic diversification strategy. This comes as Microsoft has invested over $13 billion in OpenAI since 2019 but faces escalating costs and strategic divergences, including OpenAI’s pursuit of independent infrastructure and a potential LinkedIn rival.

Anthropic, founded by former OpenAI executives and backed by Amazon and Google with a recent $183 billion valuation after raising $13 billion, positions itself as a safety-focused AI leader. Its Claude models will enhance Copilot’s capabilities in areas like email summarization, data analysis, and presentation creation, potentially boosting Office 365’s appeal to enterprise users. Analysts estimate Office Copilot is already surpassing $1 billion in annual revenue, with over 100 million customers using Copilot products.

This partnership isn’t entirely new; Microsoft has previously incorporated other models like xAI’s Grok in GitHub Copilot. Yet, extending it to Office 365 represents the most substantial challenge to OpenAI’s dominance in Microsoft’s ecosystem. Community reactions on platforms like Reddit suggest it’s a response to OpenAI’s focus on conversational AI versus Anthropic’s strengths in code and productivity tasks.

Microsoft anticipates announcing the changes in the coming weeks, aligning with its push for in-house models like MAI-1 and integrations with providers like DeepSeek on Azure. As AI competition intensifies, this multi-model approach could foster innovation but also heighten rivalries, particularly with Amazon, whose AWS will indirectly benefit from Microsoft’s payments. For users, it promises more reliable, task-optimized AI tools, underscoring the rapid evolution of enterprise software in the AI era.

Researchers introduced LMEnt, a groundbreaking suite designed to analyze how language models (LMs) acquire and represent knowledge during pretraining, as detailed in a paper published on arXiv. Led by Daniela Gottesman and six co-authors, LMEnt addresses a critical gap in understanding the internal processes by which LMs transform raw data into robust knowledge representations, a process that remains poorly understood despite LMs’ growing role in applications requiring world knowledge, such as question answering and text generation.

LMEnt comprises three core components. First, it offers a knowledge-rich pretraining corpus based on Wikipedia, fully annotated with entity mentions, providing a structured dataset to track specific factual knowledge. Second, it introduces an entity-based retrieval method that outperforms traditional string-based approaches by up to 80.4%, enabling precise analysis of how specific entities influence model outputs. Third, LMEnt includes 12 pretrained models, ranging from 170 million to 1 billion parameters, based on the OLMo-2 architecture, with 4,000 intermediate checkpoints across training epochs. These models, trained on 3.6 billion to 21.6 billion tokens, match the performance of popular open-source models on knowledge benchmarks, making them a valuable testbed for studying knowledge evolution.

The suite’s design facilitates detailed research into how LMs encode facts and beliefs, addressing questions like how data composition and training dynamics shape knowledge representations. By mapping training steps to specific entity mentions, LMEnt allows researchers to trace the emergence of factual knowledge, offering insights into improving model factuality and reasoning. For example, the 170M-parameter model, optimized for 3.6 billion tokens, provides a compute-efficient baseline, while larger models reveal how scale impacts knowledge retention.

LMEnt builds on prior work like Pythia and OLMo, which also provide model suites for studying training dynamics, but it stands out with its entity-focused approach. Unlike string-based retrieval methods, which rely on exact or n-gram matches, LMEnt’s entity annotations enable more granular analysis, crucial for tackling issues like hallucinations—where models generate plausible but false information. This precision could lead to models with more consistent and reliable knowledge representations.

While LMEnt is a significant step forward, challenges remain. The reliance on Wikipedia limits the corpus to publicly available, structured data, potentially missing nuanced or domain-specific knowledge. Additionally, scaling the entity-based retrieval to larger datasets or real-time applications may require further optimization. Nonetheless, LMEnt’s open-source release, including models, data, and code, fosters reproducibility and invites further exploration into knowledge acquisition, plasticity, and model editing. As AI continues to integrate into high-stakes domains, tools like LMEnt are critical for developing trustworthy, factually robust language models, paving the way for advancements in interpretability and ethical AI deployment.

OpenAI announced the acquisition of Statsig, a Seattle-based product experimentation platform, for $1.1 billion in an all-stock deal, marking one of its largest acquisitions to date. This strategic move, often described as an “acqui-hire,” brings Statsig’s founder and CEO, Vijaye Raji, into OpenAI as the new CTO of Applications, reporting to Fidji Simo, the former Instacart CEO who now leads OpenAI’s Applications division. The deal, pending regulatory approval, aims to enhance OpenAI’s ability to rapidly develop and deploy AI products like ChatGPT and Codex by integrating Statsig’s expertise in A/B testing, feature flagging, and real-time analytics.

Statsig, founded in 2021 by Raji, a former Meta engineering leader, has powered experimentation for companies like Atlassian, Notion, and OpenAI itself. Its platform enables data-driven product iteration, allowing teams to test features and make real-time decisions, which is critical in the fast-paced AI development landscape. By bringing Statsig in-house, OpenAI seeks to streamline its feedback loops, ensuring safer and more effective rollouts of AI features. Raji’s role will involve overseeing product engineering for ChatGPT and Codex, focusing on infrastructure and system integrity to deliver reliable, scalable applications.

The acquisition aligns with OpenAI’s shift from a research-focused lab to a product-driven powerhouse, competing with rivals like Google and Microsoft. Statsig’s tools address a key challenge in AI: transforming cutting-edge research into user-friendly, safe products. As AI-generated features proliferate, Statsig’s experimentation framework helps validate what works, reducing the risk of deploying untested capabilities. This move is seen as a response to the need for disciplined product development in an era where AI can generate countless feature variants but requires rigorous testing to ensure quality.

Alongside the acquisition, OpenAI announced leadership changes. Chief Product Officer Kevin Weil will transition to VP of AI for Science, focusing on AI-driven scientific discovery, while Srinivas Narayanan, former head of engineering, becomes CTO of B2B Applications, reporting to COO Brad Lightcap. These shifts reflect OpenAI’s strategic restructuring to balance research and product innovation. Statsig’s 150+ employees will join OpenAI but continue operating independently from Seattle, ensuring continuity for existing customers like Bloomberg and Figma.

The $1.1 billion valuation matches Statsig’s May 2025 funding round, suggesting investors are banking on OpenAI’s soaring stock value, with the company reportedly eyeing a $500 billion valuation. While some analysts view this as a brilliant move to secure first-mover advantages in AI product development, others note that Statsig’s integration must be carefully managed to avoid disrupting its customer base. This acquisition underscores the growing importance of experimentation in AI, positioning OpenAI to deliver more reliable, user-focused applications in a fiercely competitive market.

BMW Group and Qualcomm Technologies announced a groundbreaking collaboration to introduce the Snapdragon Ride Pilot, an advanced driver-assistance system (ADAS) set to debut in the all-new BMW iX3 electric SUV. This system, built on Qualcomm’s Snapdragon Ride system-on-chips (SoCs) and a co-developed software stack, enables hands-free highway driving, automatic lane changes, and parking assistance, marking a significant step in BMW’s push toward semi-autonomous driving. The technology, validated for use in over 60 countries with plans to expand to more than 100 by 2026, is now available to global automakers and Tier-1 suppliers, positioning Qualcomm as a key player in the growing ADAS market.

The Snapdragon Ride Pilot supports Level 2+ automation, allowing drivers to take their hands off the wheel on approved motorways while remaining responsible for supervision. It uses advanced camera systems, radar, and AI to provide a 360-degree view of the vehicle’s surroundings, enabling features like contextual lane changes triggered by subtle driver cues, such as mirror glances. The system also incorporates vehicle-to-everything (V2X) communication via Qualcomm’s V2X 200 chipset, allowing the car to interact with other vehicles and infrastructure to enhance safety. This “superbrain” computer, as described by BMW, evolves through a cloud-based data flywheel, using anonymous fleet data to refine AI models via over-the-air updates, improving performance over time.

This three-year collaboration, involving 1,400 specialists across Germany, the U.S., Sweden, and BMW’s Autonomous Driving Test Center in the Czech Republic, integrates Qualcomm’s computing expertise with BMW’s automotive engineering. The system meets the highest Automotive Safety Integrity Levels (ASIL) and includes cybersecurity measures to protect against threats. While not fully autonomous (Level 5), it competes with systems like Tesla’s Full Self-Driving, GM’s Super Cruise, and Ford’s BlueCruise, offering a balance of convenience and safety. However, safety concerns persist, as studies suggest drivers may over-rely on such systems, potentially increasing crash risks.

The debut in the BMW iX3, part of BMW’s Neue Klasse platform, aligns with the automaker’s vision of combining electrification, connectivity, and automation. Qualcomm’s automotive revenue, which reached $2.9 billion in fiscal 2024, is projected to hit $8 billion by 2029, reflecting its strategic shift from smartphones to automotive tech. The partnership not only strengthens BMW’s position in the premium EV market but also signals a broader industry trend of tech-automotive collaborations to meet consumer demand for intelligent, safe vehicles. As BMW rolls out this technology, it remains committed to driver engagement, ensuring its cars remain “fun to drive” even as automation advances.

Cognition AI, the San Francisco-based startup behind the AI-powered coding assistant Devin, has raised $400 million in a funding round that values the company at $10.2 billion, more than doubling its $4 billion valuation from March 2025. Announced on September 8, 2025, the round was led by Founders Fund, with participation from existing investors like Lux Capital, 8VC, Elad Gil, Definition Capital, and Swish Ventures. This significant capital infusion highlights the intense investor enthusiasm for AI-driven software development tools, despite market volatility and regulatory scrutiny.

Cognition’s rapid ascent is driven by the explosive growth of Devin, an AI agent designed to autonomously write, debug, and deploy code. The startup’s annual recurring revenue (ARR) surged from $1 million in September 2024 to $73 million by June 2025, a 73x increase in less than a year. This growth, coupled with a disciplined net burn rate under $20 million since its 2023 founding, underscores Cognition’s capital efficiency. Major clients, including Dell Technologies, Cisco Systems, Goldman Sachs, and MongoDB, have adopted Devin for tasks like bug scanning and project automation, signaling strong enterprise demand.

A pivotal factor in Cognition’s growth was its July 2025 acquisition of Windsurf, a rival AI coding startup, shortly after Google acquired Windsurf’s leadership team and licensed its technology for $2.4 billion. The acquisition doubled Cognition’s ARR by integrating Windsurf’s developer tools and enterprise customer base, with less than 5% overlap in clientele. “Combining Devin’s rapid adoption with Windsurf’s IDE product has been a massive unlock,” said CEO Scott Wu, emphasizing the strategic move’s impact on scaling enterprise offerings.

The funding will fuel Cognition’s expansion of its go-to-market and engineering teams, alongside the development of new AI models tailored for programming tasks. Devin’s enterprise edition, which offers customized versions with enhanced cybersecurity controls, is poised to capture a larger share of the market. However, the company faces challenges, including a high-pressure work culture—evidenced by 80-hour work weeks and recent layoffs of 30 staffers alongside buyout offers to 200 employees. Critics also note that Devin, while promising, sometimes struggles with complex real-world tasks, requiring human oversight.

The broader AI coding sector is fiercely competitive, with players like Microsoft’s GitHub Copilot, Google’s Gemini Code Assist, and Amazon’s CodeWhisperer vying for dominance. Yet, Cognition’s valuation—29 times its prior fundraising multiple—reflects investor confidence in its potential to disrupt traditional software engineering. As AI agents evolve, Cognition’s focus on automation could reshape workflows, though regulatory pressures on AI ethics and competition from open-source alternatives pose risks. With this $400 million raise, Cognition is well-positioned to accelerate its roadmap, setting a benchmark for AI startups navigating a dynamic landscape.

On September 8, 2025, Nebius Group, an Amsterdam-based AI infrastructure company, announced a landmark $17.4 billion deal to supply Microsoft with GPU infrastructure capacity over a five-year term, with the potential to reach $19.4 billion if Microsoft opts for additional services. This agreement, one of the largest in the AI infrastructure sector, sent Nebius shares soaring by over 47% in after-hours trading, with some reports noting a peak surge of 60%. The deal underscores the surging demand for high-performance computing resources as tech giants race to bolster their AI capabilities.

Nebius, which emerged from a 2023 split of Russian tech giant Yandex, will provide Microsoft with dedicated GPU capacity from its new data center in Vineland, New Jersey, starting later in 2025. The company specializes in delivering Nvidia GPUs and AI cloud services, offering computing, storage, and tools for AI developers to build and run models. The agreement is expected to significantly accelerate Nebius’s AI cloud business, with CEO Arkady Volozh stating, “The economics of the deal are attractive, but significantly, it will help us accelerate growth in 2026 and beyond.” Nebius plans to finance the data center’s construction and chip purchases using cash flow from the deal and potential debt issuance.

This partnership highlights the intense competition for AI infrastructure, with Microsoft, a key player in AI through its Azure platform and OpenAI partnership, seeking to secure robust computing resources. Unlike its competitor CoreWeave, which saw a modest 5% stock increase, Nebius’s deal positions it as a major player in the AI supply chain. The contract’s scale—exceeding Nebius’s projected 2025 revenue of $625 million by nearly three times annually—has fueled optimism among investors, with some on X predicting a potential 10x growth for Nebius due to ongoing GPU supply constraints.

However, the deal also raises questions about market dynamics. Microsoft, previously CoreWeave’s largest customer, had not signed a comparable long-term contract with them, and CoreWeave denied earlier reports of contract cancellations. Nebius’s agreement could signal a strategic shift for Microsoft, possibly diversifying its AI infrastructure partners. Meanwhile, Nebius’s U.S. expansion, with offices in San Francisco, Dallas, and New York, and its doubled market cap of $15 billion in 2025, reflect its aggressive push into the American market.

Despite the enthusiasm, some analysts remain cautious. Posts on X and reports note that while Nebius benefits from current GPU shortages, its long-term viability depends on innovation beyond merely supplying Nvidia chips. The company’s additional ventures in autonomous driving (Avride) and edtech (TripleTen) may diversify its portfolio, but financial health concerns, including declining revenues and weak cash flow, persist. As the AI infrastructure race intensifies, Nebius’s ability to capitalize on this deal and secure further contracts will be critical to sustaining its meteoric rise.