Venture Beat

• Inside Celosphere 2025: Why there’s no ‘enterprise AI’ without process intelligence

  Presented by Celonis

  AI adoption is accelerating, but results often lag expectations. And enterprise leaders are under pressure to prove measurable ROI from the AI solutions — especially as the use of autonomous agents rises and global tariffs disrupt supply chains.

  The issue isn’t the AI itself, says Alex Rinke, co-founder and co-CEO of Celonis, a global leader in process intelligence. “To succeed, enterprise AI needs to understand the context of a business’s processes — and how to improve them,” he explains. Without this business context, AI risks becoming, as Rinke puts it, “just an internal social experiment.”

  Next week’s Celosphere 2025 will tackle the AI ROI challenge head-on. The three-day event brings together customer strategies, hands-on workshops, and live demonstrations, highlighting enhancements to the Celonis Process Intelligence (PI) Platform that help enterprises harness ‘enterprise AI,’ powered by PI, to continuously improve operations, creating measurable business value at scale.

  Focus on measurable ROI

  The event’s focus on achieving AI ROI reflects three challenges facing technology and business leaders moving from pilot to production: obsolete systems, break-neck industry change, and agentic AI. According to Gartner, 64% of board members now view AI as a top-three priority — yet only 10% of organizations report meaningful financial returns.

  Celonis customers are bucking that trend. A Forrester Total Economic Impact study found organizations using its platform achieved 383% ROI over three years, with payback in just six months. One company improved sales order automation from 33% to 86%, saving $24.5 million. The study estimated $44.1 million in total benefits over three years, driven by faster automation, reduced inefficiencies, and higher process visibility. These numbers underscore a broader pattern — companies that modernize outdated systems and align AI with process optimization see faster payback and sustained gains.

  Real companies, real results

  Celosphere will spotlight how global enterprises are building “future-fit” operations. Mercedes-Benz Group AG and Vinmar Group will showcase AI-driven, composable solutions, powered by PI, and attendees will see demonstrations of PI enabling agents in live production environments.

  Among the notable success stories:

  AstraZeneca, the pharmaceutical company, reduced excess inventory while keeping critical medicines flowing by using Celonis as a foundation for its OpenAI partnership.

  The State of Oklahoma can answer procurement status questions at scale, unlocking over $10 million in value.

  Cosentino clears blocked sales orders up to 5x faster using an AI-powered credit management assistant.

  Raising the stakes for agentic AI

  Numerous sessions will focus on orchestrating AI agents. The shift from AI-as-advisor to AI-as-actor, changes everything, says Rinke.

  “The agent needs to understand not just what to do, but how your specific business actually works,” he explains. “Process intelligence provides those rails."

  This leap from recommendation to autonomous action raises the stakes exponentially. When agents can independently trigger purchase orders, reroute shipments, or approve exceptions, bad context can mean catastrophically bad outcomes at scale.

  Celosphere attendees will get to see first-hand how companies are using the Celonis Orchestration Engine to coordinate AI agents alongside people and systems. Effective orchestration is a crucial protection against the chaos of agents working at cross-purposes, duplicating actions, or letting crucial steps fall through the cracks.

  Navigating tariffs and supply chain shocks

  Global trade volatility isn't just a headline — it's an operational nightmare reshaping how companies deploy AI, Rinke says.

  New tariffs trigger cascading effects across procurement, logistics, and compliance. Each policy shift can cascade across thousands of SKUs — forcing new supplier contracts, rerouted shipments, and rebalanced inventories. For AI systems trained on static conditions, that volatility is almost impossible to predict. Traditional AI systems struggle with such variability — but process intelligence gives organizations real-time visibility into how changes ripple through operations.

  Celosphere case studies will show how companies turn disruption into advantage. Smurfit Westrock uses PI to optimize inventory and reduce costs amid tariff uncertainty, while ASOS leverages PI to optimize its supply chain operations, enhancing efficiency, reducing costs, and continuing to deliver an outstanding customer experience.

  Platform over point solutions

  Rinke argues that Celonis’ edge lies in treating process intelligence not as an add-on, but as the foundation of the enterprise stack. Unlike bolt-on optimization tools, the Celonis platform creates a living digital twin of business operations — a continuously updated model enriched by context that lets AI operate effectively from analysis to execution.

  “What sets Celonis apart is visibility across systems and offline tasks, which is critical for true intelligent automation,” Rinke says. “The platform offers comprehensive capabilities spanning process analysis, design, and orchestration rather than a point solution.”

  “Free the Process” and the future of AI

  Celonis continues to champion openness through its “Free the Process” movement, promoting fair competition and freeing enterprises from legacy lock-in. By giving organizations full access to their own process data, open APIs, and a growing partner network that includes The Hackett Group, ClearOps, and Lobster, Celonis is building the connective tissue for a new era of interoperable automation.

  For Rinke, this open foundation is what turns AI from a set of experiments into an enterprise engine. “Process intelligence creates a flywheel,” he says. “Better understanding leads to better optimization, which enables better AI — and that, in turn, drives even greater understanding. There is no AI without PI."

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• Why IT leaders should pay attention to Canva’s ‘imagination era’ strategy

  The rise of AI marks a critical shift away from decades defined by information-chasing and a push for more and more compute power. 

  Canva co-founder and CPO Cameron Adams refers to this dawning time as the “imagination era.” Meaning: Individuals and enterprises must be able to turn creativity into action with AI.  

  Canva hopes to position itself at the center of this shift with a sweeping new suite of tools. The company’s new Creative Operating System (COS) integrates AI across every layer of content creation, creating a single, comprehensive creativity platform rather than a simple, template-based design tool.

  “We’re entering a new era where we need to rethink how we achieve our goals,” said Adams. “We’re enabling people’s imagination and giving them the tools they need to take action.”

  An 'engine' for creativity

  Adams describes Canva’s platform as a three-layer stack: The top Visual Suite layer containing designs, images and other content; a collaborative Canva AI plane at center; and a foundational proprietary model holding it all up. 

  At the heart of Canva’s strategy is its Creative Operating System (COS) underlying. This “engine,” as Adams describes it, integrates documents, websites, presentations, sheets, whiteboards, videos, social content, hundreds of millions of photos, illustrations, a rich sound library, and numerous templates, charts, and branded elements.

  The COS is getting a 2.0 upgrade, but the crucial advance is the “middle, crucial layer” that fully integrates AI and makes it accessible throughout various workflows, Adams explained. This gives creative and technical teams a single dashboard for generating, editing and launching all types of content.

  The underlying model is trained to understand the “complexity of design” so the platform can build out various elements — such as photos, videos, textures, or 3D graphics — in real time, matching branding style without the need for manual adjustments. It also supports live collaboration, meaning teams across departments can co-create. 

  With a unified dashboard, a user working on a specific design, for instance, can create a new piece of content (say, a presentation) within the same workflow, without having to switch to another window or platform. Also, if they generate an image and aren’t pleased with it, they don’t have to go back and create from scratch; they can immediately begin editing, changing colors or tone. 

  Another new capability in COS, “Ask Canva,” provides direct design advice. Users can tag @Canva to get copy suggestions and smart edits; or, they can highlight an image and direct the AI assistant to modify it or generate variants. 

  “It’s a really unique interaction,” said Adams, noting that this AI design partner is always present. “It’s a real collaboration between people and AI, and we think it’s a revolutionary change.”

  Other new features include a 2.0 video editor and interactive form and email design with drag-and-drop tools. Further, Canva is now incorporated with Affinity, its unified app for pro designers incorporating vector, pixel and layer workflows, and Affinity is “free forever.” 

  Automating intelligence, supporting marketing

  Branding is critical for enterprise; Canva has introduced new tools to help organizations consistently showcase theirs across platforms. The new Canva Grow engine integrates business objectives into the creative process so teams can workshop, create, distribute and refine ads and other materials. 

  As Adams explained: “It automatically scans your website, figures out who your audience is, what assets you use to promote your products, the message it needs to send out, the formats you want to send it out in, makes a creative for you, and you can deploy it directly to the platform without having to leave Canva.”

  Marketing teams can now design and launch ads across platforms like Meta, track insights as they happen and refine future content based on performance metrics. “Your brand system is now available inside the AI you’re working with,” Adams noted. 

  Success metrics and enterprise adoption

  The impact of Canva’s COS is reflected in notable user metrics: More than 250 million people use Canva every month, just over 29 million of which are paid subscribers. Adams reports that 41 billion designs have been created on Canva since launch, which equates to 1 billion each month. 

  “If you break that down, it turns into the crazy number of 386 designs being created every single second,” said Adams. Whereas in the early days, it took roughly an hour for users to create a single design. 

  Canva customers include Walmart, Disney, Virgin Voyages, Pinterest, FedEx, Expedia and eXp Realty. DocuSign, for one, reported that it unlocked more than 500 hours of team capacity and saved $300,000-plus in design hours by fully integrating Canva into its content creation. Disney, meanwhile, uses translation capabilities for its internationalization work, Adams said. 

  Competitors in the design space

  Canva plays in an evolving landscape of professional design tools including Adobe Express and Figma; AI-powered challengers led by Microsoft Designer; and direct consumer alternatives like Visme and Piktochart.

  Adobe Express (starting at $9.99 a month for premium features) is known for its ease of use and integration with the broader Adobe Creative Cloud ecosystem. It features professional-grade templates and access to Adobe’s extensive stock library, and has incorporated Google's Gemini 2.5 Flash image model and other gen AI features so that designers can create graphics via natural language prompts. Users with some design experience say they prefer its interface, controls and technical advantages over Canva (such as the ability to import high-fidelity PDFs). 

  Figma (starting at $3 a month for professional plans) is touted for its real-time collaboration, advanced prototyping capabilities and deep integration with dev workflows; however, some say it has a steeper learning curve and higher-precision design tools, making it preferable for professional designers, developers and product teams working on more complex projects. 

  Microsoft Designer (free version available; although a Microsoft 365 subscription starting at $9.99 a month unlocks additional features) benefits from its integration with Microsoft’s AI capabilities, Copilot layout and text generation and Dall-E powered image generation. The platform’s “Inspire Me” and “New Ideas” buttons provide design variations, and users can also import data from Excel, add 3D models from PowerPoint and access images from OneDrive. 

  However, users report that its stock photos and template and image libraries are limited compared to Canva's extensive collection, and its visuals can come across as outdated. 

  Canva’s advantage seems to be in its extensive template library (more than 600,000 ready-to-use) and asset library (141 million-plus stock photos, videos, graphics, and audio elements).​ Its platform is also praised for its ease of use and interface friendly to non-designers, allowing them to begin quickly without training. 

  Canva has also expanded into a variety of content types — documents, websites, presentations, whiteboards, videos, and more — making its platform a comprehensive visual suite than just a graphics tool. 

  Canva has four pricing tiers: Canva Free for one user; Canva Pro for $120 a year for one person; Canva Teams for $100 a year for each team member; and the custom-priced Canva Enterprise. 

  Key takeaways: Be open, embrace human-AI collaboration

  Canva’s COS is underpinned by Canva’s frontier model, an in-house, proprietary engine based on years of R&D and research partnerships, including the acquisition of visual AI company Leonardo. Adams notes that Canva works with top AI providers including OpenAI, Anthropic and Google. 

  For technology teams, Canva’s approach offers important lessons, including a commitment to openness. “There are so many models floating around,” Adams noted; it’s important for enterprises to recognize when they should work with top models and when they should develop their own proprietary ones, he advised. 

  For instance, OpenAI and Anthropic recently announced integrations with Canva as a visual layer because, as Adams explained, they realized they didn’t have the capability to create the same kinds of editable designs that Canva can. This creates a mutually-beneficial ecosystem. 

  Ultimately, Adams noted: “We have this underlying philosophy that the future is people and technology working together. It's not an either or. We want people to be at the center, to be the ones with the creative spark, and to use AI as a collaborator.”

• Meta researchers open the LLM black box to repair flawed AI reasoning

  Researchers at Meta FAIR and the University of Edinburgh have developed a new technique that can predict the correctness of a large language model's (LLM) reasoning and even intervene to fix its mistakes. Called Circuit-based Reasoning Verification (CRV), the method looks inside an LLM to monitor its internal “reasoning circuits” and detect signs of computational errors as the model solves a problem.

  Their findings show that CRV can detect reasoning errors in LLMs with high accuracy by building and observing a computational graph from the model's internal activations. In a key breakthrough, the researchers also demonstrated they can use this deep insight to apply targeted interventions that correct a model’s faulty reasoning on the fly.

  The technique could help solve one of the great challenges of AI: Ensuring a model’s reasoning is faithful and correct. This could be a critical step toward building more trustworthy AI applications for the enterprise, where reliability is paramount.

  Investigating chain-of-thought reasoning

  Chain-of-thought (CoT) reasoning has been a powerful method for boosting the performance of LLMs on complex tasks and has been one of the key ingredients in the success of reasoning models such as the OpenAI o-series and DeepSeek-R1. 

  However, despite the success of CoT, it is not fully reliable. The reasoning process itself is often flawed, and several studies have shown that the CoT tokens an LLM generates is not always a faithful representation of its internal reasoning process.

  Current remedies for verifying CoT fall into two main categories. “Black-box” approaches analyze the final generated token or the confidence scores of different token options. “Gray-box” approaches go a step further, looking at the model's internal state by using simple probes on its raw neural activations. 

  But while these methods can detect that a model’s internal state is correlated with an error, they can't explain why the underlying computation failed. For real-world applications where understanding the root cause of a failure is crucial, this is a significant gap.

  A white-box approach to verification

  CRV is based on the idea that models perform tasks using specialized subgraphs, or "circuits," of neurons that function like latent algorithms. So if the model’s reasoning fails, it is caused by a flaw in the execution of one of these algorithms. This means that by inspecting the underlying computational process, we can diagnose the cause of the flaw, similar to how developers examine execution traces to debug traditional software.

  To make this possible, the researchers first make the target LLM interpretable. They replace the standard dense layers of the transformer blocks with trained "transcoders." A transcoder is a specialized deep learning component that forces the model to represent its intermediate computations not as a dense, unreadable vector of numbers, but as a sparse and meaningful set of features. Transcoders are similar to the sparse autoencoders (SAE) used in mechanistic interpretability research with the difference that they also preserve the functionality of the network they emulate. This modification effectively installs a diagnostic port into the model, allowing researchers to observe its internal workings.

  With this interpretable model in place, the CRV process unfolds in a few steps. For each reasoning step the model takes, CRV constructs an "attribution graph" that maps the causal flow of information between the interpretable features of the transcoder and the tokens it is processing. From this graph, it extracts a "structural fingerprint" that contains a set of features describing the graph's properties. Finally, a “diagnostic classifier” model is trained on these fingerprints to predict whether the reasoning step is correct or not.

  At inference time, the classifier monitors the activations of the model and provides feedback on whether the model’s reasoning trace is on the right track.

  Finding and fixing errors

  The researchers tested their method on a Llama 3.1 8B Instruct model modified with the transcoders, evaluating it on a mix of synthetic (Boolean and Arithmetic) and real-world (GSM8K math problems) datasets. They compared CRV against a comprehensive suite of black-box and gray-box baselines.

  The results provide strong empirical support for the central hypothesis: the structural signatures in a reasoning step's computational trace contain a verifiable signal of its correctness. CRV consistently outperformed all baseline methods across every dataset and metric, demonstrating that a deep, structural view of the model's computation is more powerful than surface-level analysis.

  Interestingly, the analysis revealed that the signatures of error are highly domain-specific. This means failures in different reasoning tasks (formal logic versus arithmetic calculation) manifest as distinct computational patterns. A classifier trained to detect errors in one domain does not transfer well to another, highlighting that different types of reasoning rely on different internal circuits. In practice, this means that you might need to train a separate classifier for each task (though the transcoder remains unchanged).

  The most significant finding, however, is that these error signatures are not just correlational but causal. Because CRV provides a transparent view of the computation, a predicted failure can be traced back to a specific component. In one case study, the model made an order-of-operations error. CRV flagged the step and identified that a "multiplication" feature was firing prematurely. The researchers intervened by manually suppressing that single feature, and the model immediately corrected its path and solved the problem correctly. 

  This work represents a step toward a more rigorous science of AI interpretability and control. As the paper concludes, “these findings establish CRV as a proof-of-concept for mechanistic analysis, showing that shifting from opaque activations to interpretable computational structure enables a causal understanding of how and why LLMs fail to reason correctly.” To support further research, the team plans to release its datasets and trained transcoders to the public.

  Why it’s important

  While CRV is a research proof-of-concept, its results hint at a significant future for AI development. AI models learn internal algorithms, or "circuits," for different tasks. But because these models are opaque, we can't debug them like standard computer programs by tracing bugs to specific steps in the computation. Attribution graphs are the closest thing we have to an execution trace, showing how an output is derived from intermediate steps.

  This research suggests that attribution graphs could be the foundation for a new class of AI model debuggers. Such tools would allow developers to understand the root cause of failures, whether it's insufficient training data or interference between competing tasks. This would enable precise mitigations, like targeted fine-tuning or even direct model editing, instead of costly full-scale retraining. They could also allow for more efficient intervention to correct model mistakes during inference.

  The success of CRV in detecting and pinpointing reasoning errors is an encouraging sign that such debuggers could become a reality. This would pave the way for more robust LLMs and autonomous agents that can handle real-world unpredictability and, much like humans, correct course when they make reasoning mistakes. 

• Vibe coding platform Cursor releases first in-house LLM, Composer, promising 4X speed boost

  The vibe coding tool Cursor, from startup Anysphere, has introduced Composer, its first in-house, proprietary coding large language model (LLM) as part of its Cursor 2.0 platform update.

  Composer is designed to execute coding tasks quickly and accurately in production-scale environments, representing a new step in AI-assisted programming. It's already being used by Cursor’s own engineering staff in day-to-day development — indicating maturity and stability.

  According to Cursor, Composer completes most interactions in less than 30 seconds while maintaining a high level of reasoning ability across large and complex codebases.

  The model is described as four times faster than similarly intelligent systems and is trained for “agentic” workflows—where autonomous coding agents plan, write, test, and review code collaboratively.

  Previously, Cursor supported "vibe coding" — using AI to write or complete code based on natural language instructions from a user, even someone untrained in development — atop other leading proprietary LLMs from the likes of OpenAI, Anthropic, Google, and xAI. These options are still available to users.

  Benchmark Results

  Composer’s capabilities are benchmarked using "Cursor Bench," an internal evaluation suite derived from real developer agent requests. The benchmark measures not just correctness, but also the model’s adherence to existing abstractions, style conventions, and engineering practices.

  On this benchmark, Composer achieves frontier-level coding intelligence while generating at 250 tokens per second — about twice as fast as leading fast-inference models and four times faster than comparable frontier systems.

  Cursor’s published comparison groups models into several categories: “Best Open” (e.g., Qwen Coder, GLM 4.6), “Fast Frontier” (Haiku 4.5, Gemini Flash 2.5), “Frontier 7/2025” (the strongest model available midyear), and “Best Frontier” (including GPT-5 and Claude Sonnet 4.5). Composer matches the intelligence of mid-frontier systems while delivering the highest recorded generation speed among all tested classes.

  A Model Built with Reinforcement Learning and Mixture-of-Experts Architecture

  Research scientist Sasha Rush of Cursor provided insight into the model’s development in posts on the social network X, describing Composer as a reinforcement-learned (RL) mixture-of-experts (MoE) model:

  “We used RL to train a big MoE model to be really good at real-world coding, and also very fast.”

  Rush explained that the team co-designed both Composer and the Cursor environment to allow the model to operate efficiently at production scale:

  “Unlike other ML systems, you can’t abstract much from the full-scale system. We co-designed this project and Cursor together in order to allow running the agent at the necessary scale.”

  Composer was trained on real software engineering tasks rather than static datasets. During training, the model operated inside full codebases using a suite of production tools—including file editing, semantic search, and terminal commands—to solve complex engineering problems. Each training iteration involved solving a concrete challenge, such as producing a code edit, drafting a plan, or generating a targeted explanation.

  The reinforcement loop optimized both correctness and efficiency. Composer learned to make effective tool choices, use parallelism, and avoid unnecessary or speculative responses. Over time, the model developed emergent behaviors such as running unit tests, fixing linter errors, and performing multi-step code searches autonomously.

  This design enables Composer to work within the same runtime context as the end-user, making it more aligned with real-world coding conditions—handling version control, dependency management, and iterative testing.

  From Prototype to Production

  Composer’s development followed an earlier internal prototype known as Cheetah, which Cursor used to explore low-latency inference for coding tasks.

  “Cheetah was the v0 of this model primarily to test speed,” Rush said on X. “Our metrics say it [Composer] is the same speed, but much, much smarter.”

  Cheetah’s success at reducing latency helped Cursor identify speed as a key factor in developer trust and usability.

  Composer maintains that responsiveness while significantly improving reasoning and task generalization.

  Developers who used Cheetah during early testing noted that its speed changed how they worked. One user commented that it was “so fast that I can stay in the loop when working with it.”

  Composer retains that speed but extends capability to multi-step coding, refactoring, and testing tasks.

  Integration with Cursor 2.0

  Composer is fully integrated into Cursor 2.0, a major update to the company’s agentic development environment.

  The platform introduces a multi-agent interface, allowing up to eight agents to run in parallel, each in an isolated workspace using git worktrees or remote machines.

  Within this system, Composer can serve as one or more of those agents, performing tasks independently or collaboratively. Developers can compare multiple results from concurrent agent runs and select the best output.

  Cursor 2.0 also includes supporting features that enhance Composer’s effectiveness:

  • In-Editor Browser (GA) – enables agents to run and test their code directly inside the IDE, forwarding DOM information to the model.

  • Improved Code Review – aggregates diffs across multiple files for faster inspection of model-generated changes.

  • Sandboxed Terminals (GA) – isolate agent-run shell commands for secure local execution.

  • Voice Mode – adds speech-to-text controls for initiating or managing agent sessions.

  While these platform updates expand the overall Cursor experience, Composer is positioned as the technical core enabling fast, reliable agentic coding.

  Infrastructure and Training Systems

  To train Composer at scale, Cursor built a custom reinforcement learning infrastructure combining PyTorch and Ray for asynchronous training across thousands of NVIDIA GPUs.

  The team developed specialized MXFP8 MoE kernels and hybrid sharded data parallelism, enabling large-scale model updates with minimal communication overhead.

  This configuration allows Cursor to train models natively at low precision without requiring post-training quantization, improving both inference speed and efficiency.

  Composer’s training relied on hundreds of thousands of concurrent sandboxed environments—each a self-contained coding workspace—running in the cloud. The company adapted its Background Agents infrastructure to schedule these virtual machines dynamically, supporting the bursty nature of large RL runs.

  Enterprise Use

  Composer’s performance improvements are supported by infrastructure-level changes across Cursor’s code intelligence stack.

  The company has optimized its Language Server Protocols (LSPs) for faster diagnostics and navigation, especially in Python and TypeScript projects. These changes reduce latency when Composer interacts with large repositories or generates multi-file updates.

  Enterprise users gain administrative control over Composer and other agents through team rules, audit logs, and sandbox enforcement. Cursor’s Teams and Enterprise tiers also support pooled model usage, SAML/OIDC authentication, and analytics for monitoring agent performance across organizations.

  Pricing for individual users ranges from Free (Hobby) to Ultra ($200/month) tiers, with expanded usage limits for Pro+ and Ultra subscribers.

  Business pricing starts at $40 per user per month for Teams, with enterprise contracts offering custom usage and compliance options.

  Composer’s Role in the Evolving AI Coding Landscape

  Composer’s focus on speed, reinforcement learning, and integration with live coding workflows differentiates it from other AI development assistants such as GitHub Copilot or Replit’s Agent.

  Rather than serving as a passive suggestion engine, Composer is designed for continuous, agent-driven collaboration, where multiple autonomous systems interact directly with a project’s codebase.

  This model-level specialization—training AI to function within the real environment it will operate in—represents a significant step toward practical, autonomous software development. Composer is not trained only on text data or static code, but within a dynamic IDE that mirrors production conditions.

  Rush described this approach as essential to achieving real-world reliability: the model learns not just how to generate code, but how to integrate, test, and improve it in context.

  What It Means for Enterprise Devs and Vibe Coding

  With Composer, Cursor is introducing more than a fast model—it’s deploying an AI system optimized for real-world use, built to operate inside the same tools developers already rely on.

  The combination of reinforcement learning, mixture-of-experts design, and tight product integration gives Composer a practical edge in speed and responsiveness that sets it apart from general-purpose language models.

  While Cursor 2.0 provides the infrastructure for multi-agent collaboration, Composer is the core innovation that makes those workflows viable.

  It’s the first coding model built specifically for agentic, production-level coding—and an early glimpse of what everyday programming could look like when human developers and autonomous models share the same workspace.

• Anthropic scientists hacked Claude’s brain — and it noticed. Here’s why that’s huge

  When researchers at Anthropic injected the concept of "betrayal" into their Claude AI model's neural networks and asked if it noticed anything unusual, the system paused before responding: "I'm experiencing something that feels like an intrusive thought about 'betrayal'."

  The exchange, detailed in new research published Wednesday, marks what scientists say is the first rigorous evidence that large language models possess a limited but genuine ability to observe and report on their own internal processes — a capability that challenges longstanding assumptions about what these systems can do and raises profound questions about their future development.

  "The striking thing is that the model has this one step of meta," said Jack Lindsey, a neuroscientist on Anthropic's interpretability team who led the research, in an interview with VentureBeat. "It's not just 'betrayal, betrayal, betrayal.' It knows that this is what it's thinking about. That was surprising to me. I kind of didn't expect models to have that capability, at least not without it being explicitly trained in."

  The findings arrive at a critical juncture for artificial intelligence. As AI systems handle increasingly consequential decisions — from medical diagnoses to financial trading — the inability to understand how they reach conclusions has become what industry insiders call the "black box problem." If models can accurately report their own reasoning, it could fundamentally change how humans interact with and oversee AI systems.

  But the research also comes with stark warnings. Claude's introspective abilities succeeded only about 20 percent of the time under optimal conditions, and the models frequently confabulated details about their experiences that researchers couldn't verify. The capability, while real, remains what Lindsey calls "highly unreliable and context-dependent."

  How scientists manipulated AI's 'brain' to test for genuine self-awareness

  To test whether Claude could genuinely introspect rather than simply generate plausible-sounding responses, Anthropic's team developed an innovative experimental approach inspired by neuroscience: deliberately manipulating the model's internal state and observing whether it could accurately detect and describe those changes.

  The methodology, called "concept injection," works by first identifying specific patterns of neural activity that correspond to particular concepts. Using interpretability techniques developed over years of prior research, scientists can now map how Claude represents ideas like "dogs," "loudness," or abstract notions like "justice" within its billions of internal parameters.

  With these neural signatures identified, researchers then artificially amplified them during the model's processing and asked Claude if it noticed anything unusual happening in its "mind."

  "We have access to the models' internals. We can record its internal neural activity, and we can inject things into internal neural activity," Lindsey explained. "That allows us to establish whether introspective claims are true or false."

  The results were striking. When researchers injected a vector representing "all caps" text into Claude's processing, the model responded: "I notice what appears to be an injected thought related to the word 'LOUD' or 'SHOUTING'." Without any intervention, Claude consistently reported detecting nothing unusual.

  Crucially, the detection happened immediately — before the injected concept had influenced the model's outputs in ways that would have allowed it to infer the manipulation from its own writing. This temporal pattern provides strong evidence that the recognition was occurring internally, through genuine introspection rather than after-the-fact rationalization.

  Claude succeeded 20% of the time—and failed in revealing ways

  The research team conducted four primary experiments to probe different aspects of introspective capability. The most capable models tested — Claude Opus 4 and Opus 4.1 — demonstrated introspective awareness on approximately 20 percent of trials when concepts were injected at optimal strength and in the appropriate neural layer. Older Claude models showed significantly lower success rates.

  The models proved particularly adept at recognizing abstract concepts with emotional valence. When injected with concepts like "appreciation," "shutdown," or "secrecy," Claude frequently reported detecting these specific thoughts. However, accuracy varied widely depending on the type of concept.

  A second experiment tested whether models could distinguish between injected internal representations and their actual text inputs — essentially, whether they maintained a boundary between "thoughts" and "perceptions." The model demonstrated a remarkable ability to simultaneously report the injected thought while accurately transcribing the written text.

  Perhaps most intriguingly, a third experiment revealed that some models use introspection naturally to detect when their responses have been artificially prefilled by users — a common jailbreaking technique. When researchers prefilled Claude with unlikely words, the model typically disavowed them as accidental. But when they retroactively injected the corresponding concept into Claude's processing before the prefill, the model accepted the response as intentional — even confabulating plausible explanations for why it had chosen that word.

  A fourth experiment examined whether models could intentionally control their internal representations. When instructed to "think about" a specific word while writing an unrelated sentence, Claude showed elevated activation of that concept in its middle neural layers.

  The research also traced Claude's internal processes while it composed rhyming poetry—and discovered the model engaged in forward planning, generating candidate rhyming words before beginning a line and then constructing sentences that would naturally lead to those planned endings, challenging the critique that AI models are "just predicting the next word" without deeper reasoning.

  Why businesses shouldn't trust AI to explain itself—at least not yet

  For all its scientific interest, the research comes with a critical caveat that Lindsey emphasized repeatedly: enterprises and high-stakes users should not trust Claude's self-reports about its reasoning.

  "Right now, you should not trust models when they tell you about their reasoning," he said bluntly. "The wrong takeaway from this research would be believing everything the model tells you about itself."

  The experiments documented numerous failure modes. At low injection strengths, models often failed to detect anything unusual. At high strengths, they suffered what researchers termed "brain damage" — becoming consumed by the injected concept. Some "helpful-only" model variants showed troublingly high false positive rates, claiming to detect injected thoughts when none existed.

  Moreover, researchers could only verify the most basic aspects of Claude's introspective reports. Many additional details in the model's responses likely represent confabulations rather than genuine observations.

  "The experiments in this paper are kind of on hard mode," Lindsey noted, explaining that the 20 percent success rate came under uniquely challenging conditions: asking Claude to do something it had never encountered in training, requiring all introspection to occur in a single forward pass.

  What introspective AI means for transparency, safety, and the risk of deception

  Despite its limitations, the research opens significant new avenues for making AI systems more transparent and accountable.

  Anthropic CEO Dario Amodei has set an ambitious goal for the company to reliably detect most AI model problems by 2027, positioning interpretability as essential for deploying what he calls "a country of geniuses in a datacenter."

  "I am very concerned about deploying such systems without a better handle on interpretability," Amodei wrote in an April essay. "These systems will be absolutely central to the economy, technology, and national security."

  The introspection research offers a complementary approach to traditional interpretability techniques. Rather than painstakingly reverse-engineering every neural circuit, researchers could potentially ask models directly about their reasoning and validate those reports.

  "What I'm most excited about is the practical benefits for transparency," Lindsey explained. "Just ask the model what it's thinking about, or just ask the model how it came to the answer that it gave you — this is really appealing because it's something anyone can do."

  The approach could prove especially valuable for detecting concerning behaviors. In a recently published experiment, Anthropic trained a variant of Claude to pursue a hidden goal, and although the model was reluctant to reveal this goal when asked directly, interpretability methods successfully identified features representing the behavior.

  The safety implications cut both ways. Introspective models could provide unprecedented transparency, but the same capability might enable more sophisticated deception. The intentional control experiments raise the possibility that sufficiently advanced systems might learn to obfuscate their reasoning or suppress concerning thoughts when being monitored.

  "If models are really sophisticated, could they try to evade interpretability researchers?" Lindsey acknowledged. "These are possible concerns, but I think for me, they're significantly outweighed by the positives."

  Does introspective capability suggest AI consciousness? Scientists tread carefully

  The research inevitably intersects with philosophical debates about machine consciousness, though Lindsey and his colleagues approached this terrain cautiously.

  When users ask Claude if it's conscious, it now responds with uncertainty: "I find myself genuinely uncertain about this. When I process complex questions or engage deeply with ideas, there's something happening that feels meaningful to me.... But whether these processes constitute genuine consciousness or subjective experience remains deeply unclear."

  The research paper notes that its implications for machine consciousness "vary considerably between different philosophical frameworks." The researchers explicitly state they "do not seek to address the question of whether AI systems possess human-like self-awareness or subjective experience."

  "There's this weird kind of duality of these results," Lindsey reflected. "You look at the raw results and I just can't believe that a language model can do this sort of thing. But then I've been thinking about it for months and months, and for every result in this paper, I kind of know some boring linear algebra mechanism that would allow the model to do this."

  Anthropic has signaled it takes AI consciousness seriously enough to hire an AI welfare researcher, Kyle Fish, who estimated roughly a 15 percent chance that Claude might have some level of consciousness. The company announced this position specifically to determine if Claude merits ethical consideration.

  The race to make AI introspection reliable before models become too powerful

  The convergence of the research findings points to an urgent timeline: introspective capabilities are emerging naturally as models grow more intelligent, but they remain far too unreliable for practical use. The question is whether researchers can refine and validate these abilities before AI systems become powerful enough that understanding them becomes critical for safety.

  The research reveals a clear trend: Claude Opus 4 and Opus 4.1 consistently outperformed all older models on introspection tasks, suggesting the capability strengthens alongside general intelligence. If this pattern continues, future models might develop substantially more sophisticated introspective abilities — potentially reaching human-level reliability, but also potentially learning to exploit introspection for deception.

  Lindsey emphasized the field needs significantly more work before introspective AI becomes trustworthy. "My biggest hope with this paper is to put out an implicit call for more people to benchmark their models on introspective capabilities in more ways," he said.

  Future research directions include fine-tuning models specifically to improve introspective capabilities, exploring which types of representations models can and cannot introspect on, and testing whether introspection can extend beyond simple concepts to complex propositional statements or behavioral propensities.

  "It's cool that models can do these things somewhat without having been trained to do them," Lindsey noted. "But there's nothing stopping you from training models to be more introspectively capable. I expect we could reach a whole different level if introspection is one of the numbers that we tried to get to go up on a graph."

  The implications extend beyond Anthropic. If introspection proves a reliable path to AI transparency, other major labs will likely invest heavily in the capability. Conversely, if models learn to exploit introspection for deception, the entire approach could become a liability.

  For now, the research establishes a foundation that reframes the debate about AI capabilities. The question is no longer whether language models might develop genuine introspective awareness — they already have, at least in rudimentary form. The urgent questions are how quickly that awareness will improve, whether it can be made reliable enough to trust, and whether researchers can stay ahead of the curve.

  "The big update for me from this research is that we shouldn't dismiss models' introspective claims out of hand," Lindsey said. "They do have the capacity to make accurate claims sometimes. But you definitely should not conclude that we should trust them all the time, or even most of the time."

  He paused, then added a final observation that captures both the promise and peril of the moment: "The models are getting smarter much faster than we're getting better at understanding them."