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How growing UK midsize businesses are building in the AI era

admin-axhub · 2026-06-17

<div class="block-paragraph_advanced">The UK’s 5-million-plus small and midsize businesses and enterprises (SMBs) are the backbone of our economy. Today, we’re seeing these critical businesses begin to put AI to work, to operate more efficiently, move faster, and ultimately deliver better outcomes for their customers. This shift is driven by tangible day-to-day results. According to <a href="https://www.enterprisenation.com/learn-something/one-in-five-small-businesses-regularly-use-ai-new-enterprise-nation-research-finds/" rel="noopener" target="_blank">recent research</a> from Enterprise Nation published in partnership with Google, 71% of AI adopters surveyed in the UK say the technology helps them save time on routine tasks, and 64% report a direct boost in productivity. On top of this, AI-enabled productivity tools (like Google Workspace with Gemini) are delivering a <a href="https://www.googlecloudpresscorner.com/2025-10-08-Google-Reveals-AIs-Potential-to-Supercharge-British-Small-Business-Innovation#:~:text=SME%20leaders%20believe%20these%20innovations,them%20an%20extra%20working%20day." rel="noopener" target="_blank">20% boost in productivity for SMBs</a>, which effectively hands them back one full working day every single week. At Google Cloud, we have a front row seat to this shift: SMBs have long utilized platforms like Google Workspace, and today they’re transforming with Google’s AI platform and models. In fact, we’ve seen the number of UK-based SMBs using Google Cloud AI nearly double year-over-year. This includes our Gemini models and products like Gemini Enterprise and AI Studio, which are helping SMBs do things like: <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Roll out better customer support systems to help escalate and resolve customer support calls more quickly. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Automate repetitive actions in areas like payroll and accounting. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Help more employees understand and leverage data at work — even those not trained as data analysts. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Rapidly create and implement new designs for marketing collateral. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Help more people build their own AI agents to help them in their everyday jobs. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Conduct complex research projects at a speed and price point previously unavailable. </li> </ul> At today’s <a href="https://www.googlecloudevents.com/london-summit?utm_content=online_blog&utm_source=cloud_sfdc&utm_medium=blog&utm_campaign=FY26-Q2-EMEA-EME39630-physicalevent-er-London-Summitmc-168582" rel="noopener" target="_blank">Google Cloud London Summit</a>, we’re showcasing a number of innovative SMB customers who are actively using our AI tools to transform how they work, including companies who have recently expanded their work with us: <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Neural Alpha, a sustainability fintech company, is using Gemini models to read unstructured environmental and corporate sustainability reports to automatically find and organize thousands of key facts, cutting months of slow, manual research down to a fraction of the time. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Sep 2, a digital security provider, uses Gemini Enterprise to deploy autonomous AI agents for 24/7 threat monitoring — accelerating incident detection and quickly neutralizing security threats for its customers. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Sunhouse, a strategic brand design agency, uses Gemini Enterprise to easily find archived design work stored on Google Drive, enabling its teams to spend less time hunting for files and more time growing its business with global brands. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Terrapinn, a global B2B events company, is transforming its operations by leveraging Gemini models, NotebookLM, Looker, and BigQuery to turn manual tasks into automated workflows, accelerating how its teams design, market, and deliver world-class conferences. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> VoCoVo, a telecommunications provider, is integrating Google Cloud AI across its systems to turn isolated data into actionable intelligence and build autonomous workflows, streamlining routine operations so their team can focus on high-impact innovation. </li> </ul> <h3>Empowering Your Team: AI Upskilling Resources for Growing British Businesses</h3> To help midsize teams maximize their impact and confidently navigate the modern AI landscape, we’ve developed a suite of dedicated, no-cost upskilling resources. Whether you want to train your existing teams or democratize data tools across your entire workforce, these programs will help you build an AI-ready organization: <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> SMB-Focused Programs: Explore our new <a href="https://www.skills.google/paths/4020?utm_campaign=SMB-learning-path" rel="noopener" target="_blank">SMB Learning Path</a> or enroll in the <a href="https://developers.google.com/program/gear" rel="noopener" target="_blank">Gemini Enterprise Agent Ready</a> (GEAR) program for specialized training in agentic AI. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <a href="http://skills.google/learningcenter" rel="noopener" target="_blank">Google Skills for Organizations</a>: Access our no-cost, on-demand learning platform featuring over 3,000 AI courses and hands-on labs created by experts at Google Cloud and Google DeepMind. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <a href="https://developers.google.com/program/gear/getcertified/" rel="noopener" target="_blank">Get Certified</a>: Ready to validate your team's expertise? This premium, cohort-based program offers instructor-led training, technical mentorship, and AI-infused skill badges designed to prepare your team for industry-recognized certifications. </li> </ul> By offering a full suite of SMB technology and training — from productivity in Workspace, to all our Ads services, and now powerful AI tools — Google is helping small and midsize firms thrive, no matter where the future takes us. </div>

How Siemens "slices the elephant," advancing agentic workflows for industrial software development

admin-axhub · 2026-06-16

<div class="block-paragraph_advanced">For technology companies like Siemens, software is the nervous system of factories, energy grids, and transportation networks worldwide. As a global leader in industrial AI, industrial software, and industrial automation, Siemens brings decades of domain expertise across factory and process automation, energy infrastructure, and intelligent transportation — expertise that no off-the-shelf AI solution can replicate. But innovation carries a heavy anchor: legacy code. With codebases spanning hundreds of millions of lines developed for over more than a decade, Siemens faced a challenge that standard AI tools couldn't solve: understanding and modernizing this code and the applications which run on it. The scale and depth of industrial-grade software demand a fundamentally different approach. Existing coding assistants lacked the contextual depth required to navigate complex, multi-layered industrial codebases — a gap Siemens set out to close. To solve this, Siemens and Google Cloud created Knowledge Fabric, an AI system for automating the software development lifecycle. It was built using knowledge graphs on Spanner Graph, the Google Agent Development Kit, Gemini API, Agent Platform, Gemini CLI, and Anthropic Claude Code. In a pilot migrating existing frontiers to web-based interfaces, Knowledge Fabric reduced implementation effort, freeing engineers to focus on customer innovations while maintaining full system compatibility. “By ingesting the entire software ecosystem into an intelligent agentic system equipped with custom knowledge graphs, we aren’t just helping developers optimize their development time; we are enabling autonomous agents to reason across the past to build the future,” said Franz Menzl, senior vice president, product creation excellence at Siemens. “This is about freeing engineers from repetitive work so they can focus on higher-value problem solving.” <h3>The challenge: the complexity of industrial software</h3> Modernizing large-scale industrial-grade software systems is often compared to rebuilding a jet while flying it. For Siemens, the challenge had four dimensions: <ol> <li role="presentation">Scale: The repositories are massive — far exceeding the context windows of standard large language models.</li> <li role="presentation">Fragmentation: Critical knowledge was scattered across code, Jira tickets, Confluence pages, and scanned PDF manuals from the early 2000s.</li> <li role="presentation">Complexity: Tracing the link between a specific line of code and a functional requirement document from 10 years ago presented a challenge that no manual or conventional tooling approach could address efficiently. It’s a reality shared across the industry.</li> <li role="presentation">Responsibility: Systems must adhere to strict quality, compliance, and lifecycle requirements, often over 15 to 20 years of operation. AI‑generated outputs must therefore be explainable, traceable, and verifiable. Hallucinated or unvalidated changes are not merely inefficient but operationally unacceptable.</li> </ol> "We realized that standard RAG (retrieval-augmented generation) wasn't enough," said Agata Gołębiowska, technical lead, Google Cloud. "Code isn't just text; it has inherent structure. A class belongs to a file, which belongs to a module. Flattening that into a vector database meant losing the representation of relationships elements of the codebase." <h3>The solution: A domain-aware Knowledge Fabric</h3> To make this sprawling software environment navigable for AI-driven workflows, the teams built the Knowledge Fabric agent. This agent goes beyond keyword matching to “understand” the relationships between assets. We use Spanner Graph to model the inherent structure of the codebase, applying the same rigor to documentation across formats. By mapping connections between these domains, we can link specific code snippets directly to requirements in a design document. Agents then traverse this graph, using tools to query the structure via <a href="https://docs.cloud.google.com/spanner/docs/reference/standard-sql/graph-intro">Graph Query Language (GQL)</a>. But GQL is only one piece. To enable semantic understanding, we generate embeddings for every node, using Spanner's <a href="https://docs.cloud.google.com/spanner/docs/find-approximate-nearest-neighbors">Approximate Nearest Neighbors (ANN)</a> algorithm to perform efficient vector search across the full codebase. Finally, we give agents <a href="https://cloud.google.com/blog/products/databases/spanner-graph-full-text-search?e=0">full-text search</a> capabilities, which can be combined with GQL to pinpoint nodes and edges with precision.</div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/2-diagram.max-1000x1000.png" alt="2-diagram"> </a> </figure> </div> </div> </div> <div class="block-paragraph_advanced">Combining these three methods lets an LLM agent answer complex queries, such as: "Which functions need to be updated if I change the logic in the Axis Control Panel?" The system traverses the graph — weighing keyword and semantic similarity — to identify dependencies, retrieve relevant documentation, and present a precise impact analysis. This precise context is what lets a coding agent produce a valid, usable, and maintainable implementation. <h3>"Slicing the elephant:" the agentic workflow</h3> A key insight from the project was that AI agents struggle with massive, ambiguous tasks. To succeed, the team adopted a design pattern dubbed "slicing the elephant." The system breaks a sweeping request like “refactor this module” into smaller, more manageable tasks, each handled by a specialized agent built with the Google Agent Development Kit (ADK): <ul> <li role="presentation">Search agent: Acts as a deep-research specialist. It uses tools to explore the code graph and cross-reference findings with documentation in <a href="https://cloud.google.com/products/gemini-enterprise-agent-platform/agent-search?e=48754805">Agent Search</a>.</li> <li role="presentation">User story agent: Interviews the product owner to gather requirements, then drafts detailed user stories with acceptance criteria linked to existing system contexts.</li> <li role="presentation">Architecture impact agent: Analyzes proposed changes against the graph to predict side effects before a single line of code is written.</li> <li role="presentation">Task breakdown agent: Consumes the analysis from the architecture impact agent and breaks the work into small, manageable tasks, each carrying all the context relevant to a specific change.</li> <li role="presentation">Coding agent: Implements the change described in a specific task. Reaching this step without context and prior analysis produces unusable code.</li> </ul> The system keeps a human in the loop at every step, which ensures reliable, production‑grade outcomes and keeps engineers focused on meaningful work rather than routine implementation. "By slicing the elephant — breaking complex refactoring jobs into smaller, agent-led tasks — we observed a significant productivity increase," said Alexander Lomakin, project lead at Siemens. "We essentially gave the AI the roadmap it needed to navigate the complexity." <h3>Pilot results: Faster, more efficient engineering</h3> Developers saw results almost immediately. Analyzing dependencies for a new feature once required senior engineers to spend several days navigating codebases and legacy documentation. With the Knowledge Fabric, the same work now takes far less time. In a recent production pilot migrating legacy control panels to modern web‑based interfaces, the Knowledge Fabric reduced overall coding effort while preserving system integrity and industrial quality standards. Engineers now spend more time creating customer value and less on repetitive work. <h3>Get started</h3> The Knowledge Fabric shows that generative AI can do more than write boilerplate code, it can also help teams modernize the legacy systems their businesses depend on most. To learn more about building graph-based agents for your own legacy modernization: <ul> <li role="presentation">Read about <a href="https://cloud.google.com/blog/products/data-analytics/the-unified-graph-solution-with-spanner-graph-and-bigquery-graph">Spanner Graph</a>.</li> <li role="presentation">Explore <a href="https://cloud.google.com/blog/products/ai-machine-learning/introducing-gemini-enterprise-agent-platform">Agent Platform</a> and find pre-built <a href="https://x.com/GoogleCloudTech/status/2048066787233943773" rel="noopener" target="_blank">production-grade agents</a> on <a href="https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/agent-garden">Agent Garden</a></li> <li role="presentation">Check out the <a href="https://adk.dev/" rel="noopener" target="_blank">Agent Development Kit</a>.</li> <li role="presentation"><a href="https://www.siemens.com/en-us/company/artificial-intelligence/industrial-ai/" rel="noopener" target="_blank">Read more</a> on how Siemens is advancing industrial AI.</li> </ul></div>

Cloud CISO Perspectives: The 4 lessons that guided AI Threat Defense

admin-axhub · 2026-06-15

<div class="block-paragraph">Welcome to the first Cloud CISO Perspectives for June 2026. Today, we introduce Chris Betz as the new CISO of Google Cloud. For his first Cloud CISO Perspectives, Chris shares four key lessons we learned about using AI to the defender’s advantage while building AI Threat Defense.As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the <a href="https://cloud.google.com/blog/products/identity-security/">Google Cloud blog</a>. If you’re reading this on the website and you’d like to receive the email version, you can <a href="https://cloud.google.com/resources/google-cloud-ciso-newsletter-signup">subscribe here</a>.</div> <div class="block-aside"><dl> <dt>aside_block</dt> <dd><ListValue: [StructValue([('title', 'Get vital board insights with Google Cloud'), ('body', <wagtail.rich_text.RichText object at 0x7f3af19a7910>), ('btn_text', 'Visit the hub'), ('href', 'https://cloud.google.com/solutions/security/board-of-directors?utm_source=cgc-site&utm_medium=et&utm_campaign=FY26-Q2-GLOBAL-GCP39634-email-dl-dgcsm-CISOP-NL-177159&utm_content=-&utm_term=-'), ('image', <GAEImage: GCAT-replacement-logo-A>)])]></dd> </dl></div> <div class="block-paragraph"><h3 data-block-key="hswvv">Cloud CISO Perspectives: The 4 lessons that guided AI Threat Defense</h3>By Chris Betz, CISO, Google Cloud</div> <div class="block-paragraph_with_image"><div class="article-module h-c-page"> <div class="h-c-grid uni-paragraph-wrap"> <div class="uni-paragraph h-c-grid__col h-c-grid__col--8 h-c-grid__col-m--6 h-c-grid__col-l--6 h-c-grid__col--offset-2 h-c-grid__col-m--offset-3 h-c-grid__col-l--offset-3"> <figure class="article-image--wrap-small " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/Chris_Betz.max-1000x1000.png" alt="Chris Betz"> </a> <figcaption class="article-image__caption ">Chris Betz, CISO, Google Cloud</figcaption> </figure> Just a year ago, it would take months or even years for a good application security team to find thousands of vulnerabilities. Today, a team equipped with multiple AI models can find the same number in hours — or even minutes.AI is rewriting the rules of cybersecurity. It’s true that AI has boosted adversaries, introducing new threat actors, techniques, and surfaces to defend against, all operating with unprecedented scale, speed, and sophistication. AI-powered attackers are developing zero-day exploits by analyzing more than just source code: Configuration vulnerabilities, binaries, and firmware are all in their crosshairs.However, AI has also created a significant advantage for defenders. Not only are these same capabilities in our hands, adding to our defense, but we have the added advantage of the full business context that adversaries lack. Software security, and especially vulnerability finding and fixing, is being revolutionized. </div> </div> </div> </div> <div class="block-pull_quote"><div class="uni-pull-quote h-c-page"> <section class="h-c-grid"> <div class="uni-pull-quote__wrapper h-c-grid__col h-c-grid__col--8 h-c-grid__col-m--6 h-c-grid__col-l--6 h-c-grid__col--offset-2 h-c-grid__col-m--offset-3 h-c-grid__col-l--offset-3"> <div class="uni-pull-quote__inner-wrapper h-c-copy h-c-copy"> <q class="uni-pull-quote__text">Security is changing rapidly, demanding that we all innovate in response. Here is how we are approaching this work today, and some of the lessons we learned along the way.</q> </div> </div> </section> </div> </div> <div class="block-paragraph_advanced">It’s clear that the AI benefits for security are rapidly evolving, and we can no longer rely on legacy, manual defenses. The new imperative for CISOs and business leaders is to transform vulnerability management by combating machine-speed threats with a defensive strategy that’s AI native, agentic, and open. We’ve been preparing for this moment for years: From <a href="https://projectzero.google/2024/06/project-naptime.html" rel="noopener" target="_blank">Project Naptime</a>, an internal project to automate vulnerability hunting (so security researchers can take regular naps), to <a href="https://cloud.google.com/blog/products/identity-security/cloud-ciso-perspectives-our-big-sleep-agent-makes-big-leap">Big Sleep</a>, our autonomous zero-day hunter, to <a href="https://deepmind.google/discover/blog/introducing-codemender-an-ai-agent-for-code-security/" rel="noopener" target="_blank">CodeMender</a>, our automated AI-patching agent, we’ve innovated to advance using AI to improve security for all. Across our products and services, we’ve found that a unified approach <a href="https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/how-google-does-it-security-series/" rel="noopener" target="_blank">helps us protect Google at Google scale</a>. Based on this approach, we recently <a href="https://cloud.google.com/blog/products/identity-security/introducing-google-ai-threat-defense">introduced AI Threat Defense</a> as a pathway to achieve the threat-readiness transformation that you need to defend against AI threats with AI. The framework is straightforward, and you’ll find that it’s ultimately about two key points: <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Using rapidly-advancing AI to protect ourselves. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Shifting the way we develop from the ground up. </li> </ul> Security is changing rapidly, demanding that we all innovate in response. Here is how we are approaching this work today, and some of the lessons we learned along the way. Four key lessons Our work is built on a four-step framework, structured directly on what we learned: <ol> <li aria-level="1" style="list-style-type: decimal; vertical-align: baseline;"> Prepare: How Google started the journey — hardening our foundation and operationalizing the framework. </li> <li aria-level="1" style="list-style-type: decimal; vertical-align: baseline;"> Scan and prioritize: How we identified vulnerabilities — conduct deep-dive analysis and posture validation. </li> <li aria-level="1" style="list-style-type: decimal; vertical-align: baseline;"> Remediate: What we learned from remediation — implement workflows to autonomously verify and patch vulnerabilities quickly. </li> <li aria-level="1" style="list-style-type: decimal; vertical-align: baseline;"> Monitor: How we evolved monitoring with AI agents — transition to continuous detection and active response playbooks. </li> </ol> 1. Prepare: A modern enterprise runs on an enormous amount of software, and at Google that amount is even greater. We needed focus in order to move at speed, so our first lesson was to reduce our attack surface. That let us narrow our focus, reduce complexity, and use insights we have on our software supply chain and dependencies to prioritize and protect our external interfaces. Second, we invested in the operational framework supporting the vulnerability work. Early experimentation quickly showed us how valuable a scaling framework is that applies our knowledge of the environment, protects and allocates resources for scanning, and allows new capabilities to be iterated on and used by multiple teams. The amplifying power of good information, code access, dependency graphs, token budgets, and infrastructure are key friction reducers. Third, we planned engineering work alongside security work: Your engineering partners are critical, especially for aligning with your resiliency and deployment processes. Key lessons include: <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Tagging components with the model, harness, and issues found when scanning. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Allocating hardware and token budgets for finding, developing fixes, build and test. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Managing change volume (and engineer hours) while simultaneously focusing on more, smaller updates, where possible, with good rollout plans to de-risk the change. </li> </ul> 2. Scan and prioritize: We continuously scan our code across products — Search, Ads, Android, Chrome, and Google Cloud — managing tens of thousands of packages. First, we kicked off scanning and centrally tracked our progress, integrating the same tools into our pipelines. We learned early on that the best scanning results come from a combination of an expert in the specific product plus the harness plus the AI model. The combination is crucial, because results will be markedly different without all three. It’s worth noting that if you can only pick two, we recommend expertise and harness. A less capable model with a good harness and good expert is more powerful than the best model without a good harness or good experts. We also advise using more than one model. It’s important to track and iterate the data. Since the technology is evolving fast, your data is critical to revise and refine your processes. Second, look carefully at your software supply chain, and engage your key suppliers. Reachability remains a key criteria for fixes, as does streamlining and simplifying the areas you work on. Third, because there are so many vulnerabilities that can show up, it’s important to have the right methodology to prioritize them. Normally, when you’re rolling out a change you prioritize the smallest blast radius to make incremental change. Here, we recommend flipping that model: Begin with foundational code with the biggest blast radius to tackle the hardest problems first. AI models can do a good job of developing proof-of-concepts to rapidly test accuracy. Harness and models play a significant role in reducing false positive rate. Adapting your harness to do validation and using a different agent or model to validate results are both very valuable. Another key to AI-powered triage is to use your harness and tools to state vulnerability confidence as well as severity. Of course, developing a patch is only part of the problem. 3. Remediate: Fixing vulnerabilities at Google scale required a fundamental shift in strategy. We developed a new approach centered on three lessons. First, how you roll out patches matters. We adopted a risk-based approach that prioritized code reachable from the outside and had the largest blast radius, such as critical applications like BoringSSL and gVisor. We also learned that providing the model with context was the key to faster, more trusted remediation. Second, we learned you cannot fix what you cannot track. To manage remediation at scale, we built a central system to track every vulnerability, from discovery to resolution, with every finding labeled in a central repository. This single source of truth allowed us to enforce service-level objectives (SLOs) for patching, and enabled us to deploy constant autonomous patching with human review. Coupled with robust roll-back capabilities, our teams got better at fixing things quickly and safely. Finally, we learned to build resilience directly into the system. The ultimate goal was to create an inherently-resilient system that can also patch vulnerabilities, not the other way around. We don't just fix the code; we harden the entire system around it. These changes helped us rethink our approach to securing open-source software with a three-R’s strategy: Refresh, remove, and rewrite. <ol> <li aria-level="1" style="list-style-type: decimal; vertical-align: baseline;"> First, we refresh what is foundational — finding and fixing vulnerabilities in the code. This is about being good network citizens and protecting the core. </li> <li aria-level="1" style="list-style-type: decimal; vertical-align: baseline;"> Second, we remove what is peripheral. We are removing dependencies and replacing them with custom code. This is about both efficiency and reducing the attack surface, moving from a broad base of trust to a narrow, controlled one. </li> <li aria-level="1" style="list-style-type: decimal; vertical-align: baseline;"> Third, we rewrite what is critical. For everything in between, we are transitioning legacy logic and critical capabilities into modern, memory-safe languages using AI to automate the transition to eliminate entire classes of vulnerabilities from that software. </li> </ol> This evolution is a deliberate approach to reduce complexity, shrinking the attack surface, and building a more resilient, autonomous, and secure-by-design foundation for everything we do. 4. Monitor: Our work doesn’t stop there, and neither should yours. The security landscape is always changing, and the monitor phase is where our approach comes alive by creating a perpetual feedback loop to ensure we stay secure — and get stronger over time. We had three key lessons in this phase. First, security demands a constant feedback loop. We created a feedback loop to monitor the entire ecosystem for two things: system strain and vulnerability hotspots. Second, we invested in tracking our long-term remediation health. You can only improve what you measure. We built a comprehensive asset inventory to track our overall security posture and the completeness of our remediation efforts. Here’s where we hold ourselves accountable to product-level SLOs for vulnerability management. This system allows us to deploy rolling patches that can update even our data center hardware continuously and use AI agents to verify patch efficacy at a scale no human team could manage. Third, we planned for the future by using AI agents for both coding and monitoring. You have to assume that at some point, the attackers' models will become more advanced. We need to evolve our operating model and build for that reality. We use AI agents to automate and standardize our response playbooks, enabling instantaneous containment when an issue is found. We move beyond just finding bugs by feeding key libraries into Gemini to improve its pattern recognition, creating security-aware coding agents. Meanwhile, our AI-assisted red teamers are continuously stress-testing our core infrastructure, ensuring our defenses are always evolving. The outcome of this constant monitoring is a living, measured program that we can trust. This is how we protect billions of users every day, and it provides a framework that any team can use to build a defense that learns, adapts, and hardens itself against the threats of tomorrow. To learn more about AI Threat Defense, you can watch our recent <a href="https://cloudonair.withgoogle.com/events/google-cloud-security-talks-june-2026?utm_source=cgc-blog&utm_medium=blog&utm_campaign=FY26-Q2-GLOBAL-STO55-onlineevent-er-dgcsm-JuneSecTl-172732&utm_content=blog&utm_term=-" rel="noopener" target="_blank">Security Talks online event</a>. </div> <div class="block-aside"><dl> <dt>aside_block</dt> <dd><ListValue: [StructValue([('title', 'Learn something new'), ('body', <wagtail.rich_text.RichText object at 0x7f3af19a7880>), ('btn_text', 'Watch now'), ('href', 'https://www.youtube.com/watch?v=blh0hhHJ4pI'), ('image', <GAEImage: Cloud-CISO-Perspectives-logo-A>)])]></dd> </dl></div> <div class="block-paragraph"><h3 data-block-key="4bd61">In case you missed it</h3>Here are the latest updates, products, services, and resources from our security teams so far this month:<ul><li data-block-key="bhiri">Detecting and containing AI-powered threats with Google Security Operations agents: Learn how Google Security Operations works in concert with AI Threat Defense to monitor, detect, and respond to threats, particularly from code you do not own or can not patch. <a href="https://cloud.google.com/blog/products/identity-security/detecting-and-containing-powered-threats-with-google-security-operations-agents">Read more</a>.</li><li data-block-key="925tj">How to stop AI voice clones from bypassing your security perimeter: The traditional, relatively stable network perimeter has been replaced by one far more malleable: Identity, driven by vishing attacks. Here’s how to defend against them. <a href="https://cloud.google.com/transform/how-to-stop-ai-voice-clones-from-bypassing-your-security-perimeter">Read more</a>.</li><li data-block-key="b6hdd">5 lessons from red teaming AI applications: Distilled from Mandiant’s hands-on red team experiences, check out our clear, concise guidance to help customers securely develop and deploy AI apps. <a href="https://cloud.google.com/transform/5-lessons-from-red-teaming-ai-applications">Read more</a>.</li><li data-block-key="cb6ju">Introducing Wiz Cloud Cost: Powering cost management and optimization with context: Wiz unifies cloud and AI cost visibility to help teams eliminate waste and improve spend efficiency across their AWS, Azure, and Google Cloud environments. <a href="https://www.wiz.io/blog/introducing-wiz-cloud-cost" target="_blank">Read more</a>.</li><li data-block-key="61ce2">Bringing AI agents to Chrome Enterprise security management: We're launching an open-source model context protocol (MCP) server that connects AI agents directly to Chrome Enterprise APIs, helping IT and security teams manage browser security more efficiently. <a href="https://blog.google/security/bringing-ai-agents-to-chrome-enterprise-security-management/" target="_blank">Read more</a>.</li><li data-block-key="abg2f">How Google Does It: An inside look at cybersecurity: Learn how Google approaches some of today's most pressing security topics, challenges and concerns, straight from Google experts. <a href="https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/how-google-does-it-security-series/" target="_blank">View the collection</a>.</li></ul>Please visit the Google Cloud blog for more security stories <a href="https://cloud.google.com/blog/products/identity-security">published this month</a>.</div> <div class="block-aside"><dl> <dt>aside_block</dt> <dd><ListValue: [StructValue([('title', 'Join the Google Cloud CISO Community'), ('body', <wagtail.rich_text.RichText object at 0x7f3af19a78b0>), ('btn_text', 'Learn more'), ('href', 'https://rsvp.withgoogle.com/events/google-cloud-ciso-community-interest-form-2026?utm_source=cgc-blog&utm_medium=blog&utm_campaign=FY25-Q1-global-GCP30328-physicalevent-er-dgcsm-parent-CISO-community-2025&utm_content=cisop_&utm_term=-'), ('image', <GAEImage: GCAT-replacement-logo-A>)])]></dd> </dl></div> <div class="block-paragraph"><h3 data-block-key="29tyz">Threat Intelligence news</h3><ul><li data-block-key="4ins6">Seeking counsel: Ongoing targeted campaign against U.S. law firms: Mandiant Consulting details a financially-motivated data theft extortion campaign executed by the threat cluster UNC3753, highlighting tactics like physical office targeting, and provides actionable recommendations to safeguard endpoints and infrastructure. <a href="https://cloud.google.com/blog/topics/threat-intelligence/targeted-campaign-us-law-firms">Read more</a>.</li><li data-block-key="brgn3">Welcome to BlackFile: Inside a vishing extortion operation: Google Threat Intelligence Group (GTIG) has continued to track an expansive extortion campaign by UNC6671, a threat actor operating under the "BlackFile" brand, that targets organizations via sophisticated voice phishing (vishing) and single sign-on (SSO) compromise. <a href="https://cloud.google.com/blog/topics/threat-intelligence/blackfile-vishing-extortion-operation">Read more</a>.</li><li data-block-key="4oo17">2 PhaaS 2 Furious: The evolution of Chinese-language phishing services: While Russian-speaking threat actors have historically dominated the phishing-as-a-service (PhaaS) landscape, a rival ecosystem is rapidly growing within the Chinese-language underground. Within this ecosystem, GTIG has observed a fundamental move away from static password harvesting towards real-time interception and tokenization. <a href="https://cloud.google.com/blog/topics/threat-intelligence/chinese-language-phishing-services">Read more</a>.</li></ul>Please visit the Google Cloud blog for more threat intelligence stories <a href="https://cloud.google.com/blog/topics/threat-intelligence/">published this month</a>.</div> <div class="block-paragraph"><h3 data-block-key="rcfc5">Now hear this: Podcasts from Google Cloud</h3><ul><li data-block-key="dgn52">Cloud Security Podcast: Deceiving adversaries at scale: Kevin Conley from Riot Games discusses how modern organizations can use deception technology to gain a home-field advantage against adversaries by proactively monitoring their environments. <a href="https://www.youtube.com/watch?v=1TjSIDXNcu8&t=38s" target="_blank">Listen here</a>.</li><li data-block-key="5aa04">Cloud Security Podcast: Hyperscaling cloud security with Wiz: Yinon Costica, co-founder and VP of product, Wiz, discusses how the company used a product-led approach and a unique security graph model to scale rapidly within the competitive cloud security market. <a href="https://www.youtube.com/watch?v=Csk7I9Utw_U" target="_blank">Listen here</a>.</li><li data-block-key="6rsp5">Behind the Binary: When AI features create zero-click exploits: Google Project Zero’s Seth Jenkins joins the podcast to dissect a full two-bug, zero-click exploitation chain targeting the Pixel 9. <a href="https://www.youtube.com/watch?v=U80NrIRrjy0&list=PLjiTz6DAEpuLAykjYGpAUDL-tCrmTpXTf&index=1&t=3s" target="_blank">Listen here</a>.</li></ul>To have our Cloud CISO Perspectives post delivered twice a month to your inbox, <a href="https://cloud.google.com/resources/google-cloud-ciso-newsletter-signup">sign up for our newsletter</a>. We’ll be back in a few weeks with more security-related updates from Google Cloud.</div>

How to unlock true ROI in software development – a deep dive into the latest DORA research

admin-axhub · 2026-06-09

<div class="block-paragraph_advanced">How do you prove the business value of generative AI to your teams? Technology and finance leaders need to show the clear business value of AI projects to secure ongoing funding. While measuring return on investment (ROI) is a key part of validating your technical strategy, long-term success ultimately depends on building the organizational systems and culture needed to make AI work. To help you evaluate the costs and business benefits of AI, we recently shared the DORA: <a href="https://cloud.google.com/resources/content/dora-roi-of-ai-assisted-software-development?e=48754805">ROI of AI-assisted software development report</a>. This research offers a practical approach to help your team work through early adoption challenges, align engineering plans, and drive business growth. Here are the key findings from the report, and how you can use them to support your overall technology strategy. <h3>Insight #1: Navigating the J-curve of AI value realization</h3> It is important to be realistic about how quickly you will see a return on your AI investments. While AI can act as a powerful amplifier for software engineering, the path to financial value is rarely a straight line. Most organizations will instead encounter a J-curve: a temporary productivity dip and period of instability associated with early adoption. This temporary drop is a normal part of adopting new technology, rather than a sign of a failing strategy. The report points to three main reasons why this happens: <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> The learning curve: Teams require dedicated time away from regular feature delivery to adapt their daily workflows and master advanced techniques, evolving from simple prompting to building systems based on context and intent. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> The verification tax: Because AI dramatically increases the sheer volume of code produced, developers must invest extra time rigorously reviewing generated outputs to ensure trustworthiness, prevent hallucinations, and meet internal architectural standards. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Pipeline adaptation: As individual developers generate code significantly faster, downstream processes like testing and change approvals often become bottlenecks and must be actively scaled to handle the increased throughput. </li> </ul> Budgeting for this initial learning phase is key to making the transition work. By anticipating this temporary drop in productivity, you can confidently keep your AI projects moving forward, knowing that these early challenges are an investment in your team's long-term speed.</div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_M6uB5gM.max-1000x1000.jpg" alt="1"> </a> <figcaption class="article-image__caption ">The J-Curve of AI value realization</figcaption> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><h3>Insight #2: Understand the market divide on AI returns</h3> <a href="https://dora.dev/dora-report-2025/" rel="noopener" target="_blank">DORA’s state of AI-assisted software development report</a> shows that 90% of DORA survey respondents report using AI at work. Despite nearly universal adoption, actual financial impacts vary across organizations. Across the market, some companies see clear value from their engineering investments, while others struggle with unexpected costs. When a project falls short, it’s often because the team lacks the organizational support to make it work. To get the returns you expect, you need to prepare your workflows and teams to adopt the new technology. <h3>Insight #3: Calculating your AI ROI is essential</h3> Building a realistic financial model for AI starts with looking at where it actually adds value. Across the software development lifecycle, AI can help your team reduce costs, boost productivity, improve security, and deliver a better experience for both developers and users. To assist in modeling this for your organization, you can use this <a href="https://dora.dev/ai/roi/calculator" rel="noopener" target="_blank">interactive ROI calculator</a>. <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> This tool helps you explicitly forecast both the visible expenses and the hidden realities of AI adoption. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> You can explore the mechanics, adjust the assumptions to match your reality, and build your own estimate. </li> </ul></div> <div class="block-image_full_width"> <div class="article-module h-c-page"> <div class="h-c-grid"> <figure class="article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 " > <img src="https://storage.googleapis.com/gweb-cloudblog-publish/images/2_Iv3XZeI.max-1000x1000.jpg" alt="2"> </a> <figcaption class="article-image__caption ">The value model—from adoption to ROI</figcaption> </figure> </div> </div> </div> <div class="block-paragraph_advanced"><h3>Get started</h3> <ul> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <a href="https://cloud.google.com/resources/content/dora-roi-of-ai-assisted-software-development">Download the full report</a>: Explore the complete framework to quantify your AI investments, navigate the J-Curve, and map your AI investment roadmap. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> <a href="https://dora.dev/ai/roi/calculator" rel="noopener" target="_blank">Try out the interactive ROI calculator</a>: Visit <a href="https://dora.dev/ai/roi/calculator" rel="noopener" target="_blank">https://dora.dev/ai/roi/calculator</a> to estimate your organization's potential returns and build a defensible business case. </li> <li aria-level="1" style="list-style-type: disc; vertical-align: baseline;"> Watch this Cloud OnAir webinar: <a href="https://cloudonair.withgoogle.com/events/from-cost-center-to-value-engine" rel="noopener" target="_blank">From cost center to value engine: Building your business case for AI-assisted development</a>. </li> </ul></div>

Listen Labs raises $69M after viral billboard hiring stunt to scale AI customer interviews

admin-axhub · 2026-01-16

Alfred Wahlforss was running out of options. His startup, Listen Labs , needed to hire over 100 engineers, but competing against Mark Zuckerberg's $100 million offers seemed impossible. So he spent $5,000 — a fifth of his marketing budget — on a billboard in San Francisco displaying what looked like gibberish: five strings of random numbers. The numbers were actually AI tokens. Decoded, they led to a coding challenge: build an algorithm to act as a digital bouncer at Berghain, the Berlin nightclub famous for rejecting nearly everyone at the door. Within days, thousands attempted the puzzle. 430 cracked it. Some got hired. The winner flew to Berlin, all expenses paid. That unconventional approach has now attracted $69 million in Series B funding, led by Ribbit Capital with participation from Evantic and existing investors Sequoia Capital , Conviction , and Pear VC . The round values Listen Labs at $500 million and brings its total capital to $100 million. In nine months since launch, the company has grown annualized revenue by 15x to eight figures and conducted over one million AI-powered interviews. "When you obsess over customers, everything else follows," Wahlforss said in an interview with VentureBeat. "Teams that use Listen bring the customer into every decision, from marketing to product, and when the customer is delighted, everyone is." Why traditional market research is broken, and what Listen Labs is building to fix it Listen's AI researcher finds participants, conducts in-depth interviews, and delivers actionable insights in hours, not weeks. The platform replaces the traditional choice between quantitative surveys — which provide statistical precision but miss nuance—and qualitative interviews, which deliver depth but cannot scale. Wahlforss explained the limitation of existing approaches: "Essentially surveys give you false precision because people end up answering the same question... You can't get the outliers. People are actually not honest on surveys." The alternative, one-on-one human interviews, "gives you a lot of depth. You can ask follow up questions. You can kind of double check if they actually know what they're talking about. And the problem is you can't scale that." The platform works in four steps: users create a study with AI assistance, Listen recruits participants from its global network of 30 million people, an AI moderator conducts in-depth interviews with follow-up questions, and results are packaged into executive-ready reports including key themes, highlight reels, and slide decks. What distinguishes Listen's approach is its use of open-ended video conversations rather than multiple-choice forms. "In a survey, you can kind of guess what you should answer, and you have four options," Wahlforss said. "Oh, they probably want me to buy high income. Let me click on that button versus an open ended response. It just generates much more honesty." The dirty secret of the $140 billion market research industry: rampant fraud Listen finds and qualifies the right participants in its global network of 30 million people. But building that panel required confronting what Wahlforss called "one of the most shocking things that we've learned when we entered this industry"—rampant fraud. "Essentially, there's a financial transaction involved, which means there will be bad players," he explained. "We actually had some of the largest companies, some of them have billions in revenue, send us people who claim to be kind of enterprise buyers to our platform and our system immediately detected, like, fraud, fraud, fraud, fraud, fraud." The company built what it calls a "quality guard" that cross-references LinkedIn profiles with video responses to verify identity, checks consistency across how participants answer questions, and flags suspicious patterns. The result, according to Wahlforss: "People talk three times more. They're much more honest when they talk about sensitive topics like politics and mental health." Emeritus , an online education company that uses Listen, reported that approximately 20% of survey responses previously fell into the fraudulent or low-quality category. With Listen, they reduced this to almost zero. "We did not have to replace any responses because of fraud or gibberish information," said Gabrielli Tiburi, Assistant Manager of Customer Insights at Emeritus. How Microsoft, Sweetgreen, and Chubbies are using AI interviews to build better products The speed advantage has proven central to Listen's pitch. Traditional customer research at Microsoft could take four to six weeks to generate insights. "By the time we get to them, either the decision has been made or we lose out on the opportunity to actually influence it," said Romani Patel, Senior Research Manager at Microsoft. With Listen, Microsoft can now get insights in days, and in many cases, within hours. The platform has already powered several high-profile initiatives. Microsoft used Listen Labs to collect global customer stories for its 50th anniversary celebration. "We wanted users to share how Copilot is empowering them to bring their best self forward," Patel said, "and we were able to collect those user video stories within a day." Traditionally, that kind of work would have taken six to eight weeks. Simple Modern , an Oklahoma-based drinkware company, used Listen to test a new product concept. The process took about an hour to write questions, an hour to launch the study, and 2.5 hours to receive feedback from 120 people across the country. "We went from 'Should we even have this product?' to 'How should we launch it?'" said Chris Hoyle, the company's Chief Marketing Officer. Chubbies , the shorts brand, achieved a 24x increase in youth research participation—growing from 5 to 120 participants — by using Listen to overcome the scheduling challenges of traditional focus groups with children. "There's school, sports, dinner, and homework," explained Lauren Neville, Director of Insights and Innovation. "I had to find a way to hear from them that fit into their schedules." The company also discovered product issues through AI interviews that might have gone undetected otherwise. Wahlforss described how the AI "through conversations, realized there were like issues with the the kids short line, and decided to, like, interview hundreds of kids. And I understand that there were issues in the liner of the shorts and that they were, like, scratchy, quote, unquote, according to the people interviewed." The redesigned product became "a blockbuster hit." The Jevons paradox explains why cheaper research creates more demand, not less Listen Labs is entering a massive but fragmented market. Wahlforss cited research from Andreessen Horowitz estimating the market research industry at roughly $140 billion annually , populated by legacy players — some with more than a billion dollars in revenue — that he believes are vulnerable to disruption. "There are very much existing budget lines that we are replacing," Wahlforss said. "Why we're replacing them is that one, they're super costly. Two, they're kind of stuck in this old paradigm of choosing between a survey or interview, and they also take months to work with." But the more intriguing dynamic may be that AI-powered research doesn't just replace existing spending — it creates new demand. Wahlforss invoked the Jevons paradox, an economic principle that occurs when technological advancements make a resource more efficient to use, but increased efficiency leads to increased overall consumption rather than decreased consumption. "What I've noticed is that as something gets cheaper, you don't need less of it. You want more of it," Wahlforss explained. "There's infinite demand for customer understanding. So the researchers on the team can do an order of magnitude more research, and also other people who weren't researchers before can now do that as part of their job." Inside the elite engineering team that built Listen Labs before they had a working toilet Listen Labs traces its origins to a consumer app that Wahlforss and his co-founder built after meeting at Harvard. "We built this consumer app that got 20,000 downloads in one day," Wahlforss recalled. "We had all these users, and we were thinking like, okay, what can we do to get to know them better? And we built this prototype of what Listen is today." The founding team brings an unusual pedigree. Wahlforss's co-founder "was the national champion in competitive programming in Germany, and he worked at Tesla Autopilot." The company claims that 30% of its engineering team are medalists from the International Olympiad in Informatics — the same competition that produced the founders of Cognition , the AI coding startup. The Berghain billboard stunt generated approximately 5 million views across social media, according to Wahlforss. It reflected the intensity of the talent war in the Bay Area. "We had to do these things because some of our, like early employees, joined the company before we had a working toilet," he said. "But now we fixed that situation." The company grew from 5 to 40 employees in 2024 and plans to reach 150 this year. It hires engineers for non-engineering roles across marketing, growth, and operations — a bet that in the AI era, technical fluency matters everywhere. Synthetic customers and automated decisions: what Listen Labs is building next Wahlforss outlined an ambitious product roadmap that pushes into more speculative territory. The company is building "the ability to simulate your customers, so you can take all of those interviews we've done, and then extrapolate based on that and create synthetic users or simulated user voices." Beyond simulation, Listen aims to enable automated action based on research findings. "Can you not just make recommendations, but also create spawn agents to either change things in code or some customer churns? Can you give them a discount and try to bring them back?" Wahlforss acknowledged the ethical implications. "Obviously, as you said, there's kind of ethical concerns there. Of like, automated decision making overall can be bad, but we will have considerable guardrails to make sure that the companies are always in the loop." The company already handles sensitive data with care. "We don't train on any of the data," Wahlforss said. "We will also scrub any sensitive PII automatically so the model can detect that. And there are times when, for example, you work with investors, where if you accidentally mention something that could be material, non public information, the AI can actually detect that and remove any information like that." How AI could reshape the future of product development Perhaps the most provocative implication of Listen's model is how it could reshape product development itself. Wahlforss described a customer — an Australian startup — that has adopted what amounts to a continuous feedback loop. "They're based in Australia, so they're coding during the day, and then in their night, they're releasing a Listen study with an American audience. Listen validates whatever they built during the day, and they get feedback on that. They can then plug that feedback directly into coding tools like Claude Code and iterate." The vision extends Y Combinator's famous dictum — " write code, talk to users " — into an automated cycle. "Write code is now getting automated. And I think like talk to users will be as well, and you'll have this kind of infinite loop where you can start to ship this truly amazing product, almost kind of autonomously." Whether that vision materializes depends on factors beyond Listen's control — the continued improvement of AI models, enterprise willingness to trust automated research, and whether speed truly correlates with better products. A 2024 MIT study found that 95% of AI pilots fail to move into production, a statistic Wahlforss cited as the reason he emphasizes quality over demos. "I'm constantly have to emphasize like, let's make sure the quality is there and the details are right," he said. But the company's growth suggests appetite for the experiment. Microsoft's Patel said Listen has "removed the drudgery of research and brought the fun and joy back into my work." Chubbies is now pushing its founder to give everyone in the company a login. Sling Money, a stablecoin payments startup, can create a survey in ten minutes and receive results the same day. "It's a total game changer," said Ali Romero, Sling Money's marketing manager. Wahlforss has a different phrase for what he's building. When asked about the tension between speed and rigor — the long-held belief that moving fast means cutting corners — he cited Nat Friedman, the former GitHub CEO and Listen investor, who keeps a list of one-liners on his website. One of them: "Slow is fake." It's an aggressive claim for an industry built on methodological caution. But Listen Labs is betting that in the AI era, the companies that listen fastest will be the ones that win. The only question is whether customers will talk back.

Salesforce rolls out new Slackbot AI agent as it battles Microsoft and Google in workplace AI

admin-axhub · 2026-01-13

Salesforce on Tuesday launched an entirely rebuilt version of Slackbot , the company's workplace assistant, transforming it from a simple notification tool into what executives describe as a fully powered AI agent capable of searching enterprise data, drafting documents, and taking action on behalf of employees. The new Slackbot, now generally available to Business+ and Enterprise+ customers, is Salesforce's most aggressive move yet to position Slack at the center of the emerging "agentic AI" movement — where software agents work alongside humans to complete complex tasks. The launch comes as Salesforce attempts to convince investors that artificial intelligence will bolster its products rather than render them obsolete. "Slackbot isn't just another copilot or AI assistant," said Parker Harris , Salesforce co-founder and Slack's chief technology officer, in an exclusive interview with Salesforce. "It's the front door to the agentic enterprise, powered by Salesforce." From tricycle to Porsche: Salesforce rebuilt Slackbot from the ground up Harris was blunt about what distinguishes the new Slackbot from its predecessor: "The old Slackbot was, you know, a little tricycle, and the new Slackbot is like, you know, a Porsche." The original Slackbot, which has existed since Slack's early days, performed basic algorithmic tasks — reminding users to add colleagues to documents, suggesting channel archives, and delivering simple notifications. The new version runs on an entirely different architecture built around a large language model and sophisticated search capabilities that can access Salesforce records, Google Drive files, calendar data, and years of Slack conversations. "It's two different things," Harris explained. "The old Slackbot was algorithmic and fairly simple. The new Slackbot is brand new — it's based around an LLM and a very robust search engine, and connections to third-party search engines, third-party enterprise data." Salesforce chose to retain the Slackbot brand despite the fundamental technical overhaul. "People know what Slackbot is, and so we wanted to carry that forward," Harris said. Why Anthropic's Claude powers the new Slackbot — and which AI models could come next The new Slackbot runs on Claude , Anthropic's large language model, a choice driven partly by compliance requirements. Slack's commercial service operates under FedRAMP Moderate certification to serve U.S. federal government customers, and Harris said Anthropic was "the only provider that could give us a compliant LLM" when Slack began building the new system. But that exclusivity won't last. "We are, this year, going to support additional providers," Harris said. "We have a great relationship with Google. Gemini is incredible — performance is great, cost is great. So we're going to use Gemini for some things." He added that OpenAI remains a possibility as well. Harris echoed Salesforce CEO Marc Benioff's view that large language models are becoming commoditized: "You've heard Marc talk about LLMs are commodities, that they're democratized. I call them CPUs." On the sensitive question of training data, Harris was unequivocal: Salesforce does not train any models on customer data. "Models don't have any sort of security," he explained. "If we trained it on some confidential conversation that you and I have, I don't want Carolyn to know — if I train it into the LLM, there is no way for me to say you get to see the answer, but Carolyn doesn't." Inside Salesforce's internal experiment: 80,000 employees tested Slackbot with striking results Salesforce has been testing the new Slackbot internally for months , rolling it out to all 80,000 employees. According to Ryan Gavin, Slack's chief marketing officer, the results have been striking: "It's the fastest adopted product in Salesforce history." Internal data shows that two-thirds of Salesforce employees have tried the new Slackbot, with 80% of those users continuing to use it regularly. Internal satisfaction rates reached 96% — the highest for any AI feature Slack has shipped. Employees report saving between two and 20 hours per week. The adoption happened largely organically. "I think it was about five days, and a Canvas was developed by our employees called 'The Most Stealable Slackbot Prompts,'" Gavin said. "People just started adding to it organically. I think it's up to 250-plus prompts that are in this Canvas right now." Kate Crotty, a principal UX researcher at Salesforce, found that 73% of internal adoption was driven by social sharing rather than top-down mandates. "Everybody is there to help each other learn and communicate hacks," she said. How Slackbot transforms scattered enterprise data into executive-ready insights During a product demonstration, Amy Bauer, Slack's product experience designer, showed how Slackbot can synthesize information across multiple sources. In one example, she asked Slackbot to analyze customer feedback from a pilot program, upload an image of a usage dashboard, and have Slackbot correlate the qualitative and quantitative data. "This is where Slackbot really earns its keep for me," Bauer explained. "What it's doing is not just simply reading the image — it's actually looking at the image and comparing it to the insight it just generated for me." Slackbot can then query Salesforce to find enterprise accounts with open deals that might be good candidates for early access, creating what Bauer called "a really great justification and plan to move forward." Finally, it can synthesize all that information into a Canvas — Slack's collaborative document format — and find calendar availability among stakeholders to schedule a review meeting. "Up until this point, we have been working in a one-to-one capacity with Slackbot," Bauer said. "But one of the benefits that I can do now is take this insight and have it generate this into a Canvas, a shared workspace where I can iterate on it, refine it with Slackbot, or share it out with my team." Rob Seaman, Slack's chief product officer, said the Canvas creation demonstrates where the product is heading: "This is making a tool call internally to Slack Canvas to actually write, effectively, a shared document. But it signals where we're going with Slackbot — we're eventually going to be adding in additional third-party tool calls." MrBeast's company became a Slackbot guinea pig—and employees say they're saving 90 minutes a day Among Salesforce's pilot customers is Beast Industries , the parent company of YouTube star MrBeast. Luis Madrigal, the company's chief information officer, joined the launch announcement to describe his experience. "As somebody who has rolled out enterprise technologies for over two decades now, this was practically one of the easiest," Madrigal said. "The plumbing is there. Slack as an implementation, Enterprise Tools — being able to turn on the Slackbot and the Slack AI functionality was as simple as having my team go in, review, do a quick security review." Madrigal said his security team signed off "rather quickly" — unusual for enterprise AI deployments — because Slackbot accesses only the information each individual user already has permission to view. "Given all the guardrails you guys have put into place for Slackbot to be unique and customized to only the information that each individual user has, only the conversations and the Slack rooms and Slack channels that they're part of—that made my security team sign off rather quickly." One Beast Industries employee, Sinan, the head of Beast Games marketing, reported saving "at bare minimum, 90 minutes a day." Another employee, Spencer, a creative supervisor, described it as "an assistant who's paying attention when I'm not." Other pilot customers include Slalom, reMarkable, Xero, Mercari, and Engine. Mollie Bodensteiner, SVP of Operations at Engine, called Slackbot "an absolute 'chaos tamer' for our team," estimating it saves her about 30 minutes daily "just by eliminating context switching." Slackbot vs. Microsoft Copilot vs. Google Gemini: The fight for enterprise AI dominance The launch puts Salesforce in direct competition with Microsoft's Copilot , which is integrated into Teams and the broader Microsoft 365 suite, as well as Google's Gemini integrations across Workspace. When asked what distinguishes Slackbot from these alternatives, Seaman pointed to context and convenience. "The thing that makes it most powerful for our customers and users is the proximity — it's just right there in your Slack," Seaman said. "There's a tremendous convenience affordance that's naturally built into it." The deeper advantage, executives argue, is that Slackbot already understands users' work without requiring setup or training. "Most AI tools sound the same no matter who is using them," the company's announcement stated. "They lack context, miss nuance, and force you to jump between tools to get anything done." Harris put it more directly: "If you've ever had that magic experience with AI — I think ChatGPT is a great example, it's a great experience from a consumer perspective — Slackbot is really what we're doing in the enterprise, to be this employee super agent that is loved, just like people love using Slack." Amy Bauer emphasized the frictionless nature of the experience. "Slackbot is inherently grounded in the context, in the data that you have in Slack," she said. "So as you continue working in Slack, Slackbot gets better because it's grounded in the work that you're doing there. There is no setup. There is no configuration for those end users." Salesforce's ambitious plan to make Slackbot the one 'super agent' that controls all the others Salesforce positions Slackbot as what Harris calls a "super agent" — a central hub that can eventually coordinate with other AI agents across an organization. "Every corporation is going to have an employee super agent," Harris said. "Slackbot is essentially taking the magic of what Slack does. We think that Slackbot, and we're really excited about it, is going to be that." The vision extends to third-party agents already launching in Slack. Last month, Anthropic released a preview of Claude Code for Slack, allowing developers to interact with Claude's coding capabilities directly in chat threads. OpenAI, Google, Vercel, and others have also built agents for the platform. "Most of the net-new apps that are being deployed to Slack are agents," Seaman noted during the press conference. "This is proof of the promise of humans and agents coexisting and working together in Slack to solve problems." Harris described a future where Slackbot becomes an MCP (Model Context Protocol) client , able to leverage tools from across the software ecosystem — similar to how the developer tool Cursor works. "Slack can be an MCP client, and Slackbot will be the hub of that, leveraging all these tools out in the world, some of which will be these amazing agents," he said. But Harris also cautioned against over-promising on multi-agent coordination. "I still think we're in the single agent world," he said. "FY26 is going to be the year where we started to see more coordination. But we're going to do it with customer success in mind, and not demonstrate and talk about, like, 'I've got 1,000 agents working together,' because I think that's unrealistic." Slackbot costs nothing extra, but Salesforce's data access fees could squeeze some customers Slackbot is included at no additional cost for customers on Business+ and Enterprise+ plans. "There's no additional fees customers have to do," Gavin confirmed. "If they're on one of those plans, they're going to get Slackbot." However, some enterprise customers may face other cost pressures related to Salesforce's broader data strategy. CIOs may see price increases for third-party applications that work with Salesforce data, as effects of higher charges for API access ripple through the software supply chain. Fivetran CEO George Fraser has warned that Salesforce's shift in pricing policy for API access could have tangible consequences for enterprises relying on Salesforce as a system of record. "They might not be able to use Fivetran to replicate their data to Snowflake and instead have to use Salesforce Data Cloud. Or they might find that they are not able to interact with their data via ChatGPT, and instead have to use Agentforce," Fraser said in a recent CIO report . Salesforce has framed the pricing change as standard industry practice. What Slackbot can do today, what's coming in weeks, and what's still on the roadmap The new Slackbot begins rolling out today and will reach all eligible customers by the end of February. Mobile availability will complete by March 3, Bauer confirmed during her interview with VentureBeat. Some capabilities remain works in progress. Calendar reading and availability checking are available at launch, but the ability to actually book meetings is "coming a few weeks after," according to Seaman. Image generation is not currently supported, though Bauer said it's "something that we are looking at in the future." When asked about integration with competing CRM systems like HubSpot and Microsoft Dynamics , Salesforce representatives declined to provide specifics during the interview, though they acknowledged the question touched on key competitive differentiators. Salesforce is betting the future of work looks like a chat window—and it's not alone The Slackbot launch is Salesforce's bet that the future of enterprise work is conversational — that employees will increasingly prefer to interact with AI through natural language rather than navigating traditional software interfaces. Harris described Slack's product philosophy using principles like "don't make me think" and "be a great host." The goal, he said, is for Slackbot to surface information proactively rather than requiring users to hunt for it. "One of the revelations for me is LLMs applied to unstructured information are incredible," Harris said. "And the amount of value you have if you're a Slack user, if your corporation uses Slack — the amount of value in Slack is unbelievable. Because you're talking about work, you're sharing documents, you're making decisions, but you can't as a human go through that and really get the same value that an LLM can do." Looking ahead, Harris expects the interfaces themselves to evolve beyond pure conversation. "We're kind of saturating what we can do with purely conversational UIs," he said. "I think we'll start to see agents building an interface that best suits your intent, as opposed to trying to surface something within a conversational interface that matches your intent." Microsoft, Google, and a growing roster of AI startups are placing similar bets — that the winning enterprise AI will be the one embedded in the tools workers already use, not another application to learn. The race to become that invisible layer of workplace intelligence is now fully underway. For Salesforce, the stakes extend beyond a single product launch. After a bruising year on Wall Street and persistent questions about whether AI threatens its core business, the company is wagering that Slackbot can prove the opposite — that the tens of millions of people already chatting in Slack every day is not a vulnerability, but an unassailable advantage. Haley Gault, the Salesforce account executive in Pittsburgh who stumbled upon the new Slackbot on a snowy morning, captured the shift in a single sentence: "I honestly can't imagine working for another company not having access to these types of tools. This is just how I work now." That's precisely what Salesforce is counting on.

Anthropic launches Cowork, a Claude Desktop agent that works in your files — no coding required

admin-axhub · 2026-01-12

Anthropic released Cowork on Monday, a new AI agent capability that extends the power of its wildly successful Claude Code tool to non-technical users — and according to company insiders, the team built the entire feature in approximately a week and a half, largely using Claude Code itself. The launch marks a major inflection point in the race to deliver practical AI agents to mainstream users, positioning Anthropic to compete not just with OpenAI and Google in conversational AI, but with Microsoft's Copilot in the burgeoning market for AI-powered productivity tools. "Cowork lets you complete non-technical tasks much like how developers use Claude Code," the company announced via its official Claude account on X. The feature arrives as a research preview available exclusively to Claude Max subscribers — Anthropic's power-user tier priced between $100 and $200 per month — through the macOS desktop application. For the past year, the industry narrative has focused on large language models that can write poetry or debug code. With Cowork , Anthropic is betting that the real enterprise value lies in an AI that can open a folder, read a messy pile of receipts, and generate a structured expense report without human hand-holding. How developers using a coding tool for vacation research inspired Anthropic's latest product The genesis of Cowork lies in Anthropic's recent success with the developer community. In late 2024, the company released Claude Code , a terminal-based tool that allowed software engineers to automate rote programming tasks. The tool was a hit, but Anthropic noticed a peculiar trend: users were forcing the coding tool to perform non-coding labor. According to Boris Cherny , an engineer at Anthropic, the company observed users deploying the developer tool for an unexpectedly diverse array of tasks. "Since we launched Claude Code, we saw people using it for all sorts of non-coding work: doing vacation research, building slide decks, cleaning up your email, cancelling subscriptions, recovering wedding photos from a hard drive, monitoring plant growth, controlling your oven," Cherny wrote on X. "These use cases are diverse and surprising — the reason is that the underlying Claude Agent is the best agent, and Opus 4.5 is the best model." Recognizing this shadow usage, Anthropic effectively stripped the command-line complexity from their developer tool to create a consumer-friendly interface. In its blog post announcing the feature, Anthropic explained that developers "quickly began using it for almost everything else," which "prompted us to build Cowork: a simpler way for anyone — not just developers — to work with Claude in the very same way." Inside the folder-based architecture that lets Claude read, edit, and create files on your computer Unlike a standard chat interface where a user pastes text for analysis, Cowork requires a different level of trust and access. Users designate a specific folder on their local machine that Claude can access. Within that sandbox, the AI agent can read existing files, modify them, or create entirely new ones. Anthropic offers several illustrative examples: reorganizing a cluttered downloads folder by sorting and intelligently renaming each file, generating a spreadsheet of expenses from a collection of receipt screenshots, or drafting a report from scattered notes across multiple documents. "In Cowork, you give Claude access to a folder on your computer. Claude can then read, edit, or create files in that folder," the company explained on X. "Try it to create a spreadsheet from a pile of screenshots, or produce a first draft from scattered notes." The architecture relies on what is known as an "agentic loop." When a user assigns a task, the AI does not merely generate a text response. Instead, it formulates a plan, executes steps in parallel, checks its own work, and asks for clarification if it hits a roadblock. Users can queue multiple tasks and let Claude process them simultaneously — a workflow Anthropic describes as feeling "much less like a back-and-forth and much more like leaving messages for a coworker." The system is built on Anthropic's Claude Agent SDK , meaning it shares the same underlying architecture as Claude Code. Anthropic notes that Cowork "can take on many of the same tasks that Claude Code can handle, but in a more approachable form for non-coding tasks." The recursive loop where AI builds AI: Claude Code reportedly wrote much of Claude Cowork Perhaps the most remarkable detail surrounding Cowork's launch is the speed at which the tool was reportedly built — highlighting a recursive feedback loop where AI tools are being used to build better AI tools. During a livestream hosted by Dan Shipper, Felix Rieseberg, an Anthropic employee, confirmed that t he team built Cowork in approximately a week and a half . Alex Volkov, who covers AI developments, expressed surprise at the timeline: "Holy shit Anthropic built 'Cowork' in the last... week and a half?!" This prompted immediate speculation about how much of Cowork was itself built by Claude Code. Simon Smith , EVP of Generative AI at Klick Health, put it bluntly on X: "Claude Code wrote all of Claude Cowork. Can we all agree that we're in at least somewhat of a recursive improvement loop here?" The implication is profound: Anthropic's AI coding agent may have substantially contributed to building its own non-technical sibling product. If true, this is one of the most visible examples yet of AI systems being used to accelerate their own development and expansion — a strategy that could widen the gap between AI labs that successfully deploy their own agents internally and those that do not. Connectors, browser automation, and skills extend Cowork's reach beyond the local file system Cowork doesn't operate in isolation. The feature integrates with Anthropic's existing ecosystem of connectors — tools that link Claude to external information sources and services such as Asana , Notion , PayPal , and other supported partners. Users who have configured these connections in the standard Claude interface can leverage them within Cowork sessions. Additionally, Cowork can pair with Claude in Chrome , Anthropic's browser extension, to execute tasks requiring web access. This combination allows the agent to navigate websites, click buttons, fill forms, and extract information from the internet — all while operating from the desktop application. "Cowork includes a number of novel UX and safety features that we think make the product really special," Cherny explained , highlighting "a built-in VM [virtual machine] for isolation, out of the box support for browser automation, support for all your claude.ai data connectors, asking you for clarification when it's unsure." Anthropic has also introduced an initial set of "skills" specifically designed for Cowork that enhance Claude's ability to create documents, presentations, and other files. These build on the Skills for Claude framework the company announced in October, which provides specialized instruction sets Claude can load for particular types of tasks. Why Anthropic is warning users that its own AI agent could delete their files The transition from a chatbot that suggests edits to an agent that makes edits introduces significant risk. An AI that can organize files can, theoretically, delete them. In a notable display of transparency, Anthropic devoted considerable space in its announcement to warning users about Cowork's potential dangers — an unusual approach for a product launch. The company explicitly acknowledges that Claude "can take potentially destructive actions (such as deleting local files) if it's instructed to." Because Claude might occasionally misinterpret instructions, Anthropic urges users to provide "very clear guidance" about sensitive operations. More concerning is the risk of prompt injection attacks — a technique where malicious actors embed hidden instructions in content Claude might encounter online, potentially causing the agent to bypass safeguards or take harmful actions. "We've built sophisticated defenses against prompt injections," Anthropic wrote, "but agent safety — that is, the task of securing Claude's real-world actions — is still an active area of development in the industry." The company characterized these risks as inherent to the current state of AI agent technology rather than unique to Cowork. "These risks aren't new with Cowork, but it might be the first time you're using a more advanced tool that moves beyond a simple conversation," the announcement notes. Anthropic's desktop agent strategy sets up a direct challenge to Microsoft Copilot The launch of Cowork places Anthropic in direct competition with Microsoft , which has spent years attempting to integrate its Copilot AI into the fabric of the Windows operating system with mixed adoption results. However, Anthropic's approach differs in its isolation. By confining the agent to specific folders and requiring explicit connectors, they are attempting to strike a balance between the utility of an OS-level agent and the security of a sandboxed application. What distinguishes Anthropic's approach is its bottom-up evolution. Rather than designing an AI assistant and retrofitting agent capabilities, Anthropic built a powerful coding agent first — Claude Code — and is now abstracting its capabilities for broader audiences. This technical lineage may give Cowork more robust agentic behavior from the start. Claude Code has generated significant enthusiasm among developers since its initial launch as a command-line tool in late 2024 . The company expanded access with a web interface in October 2025, followed by a Slack integration in December. Cowork is the next logical step: bringing the same agentic architecture to users who may never touch a terminal. Who can access Cowork now, and what's coming next for Windows and other platforms For now, Cowork remains exclusive to Claude Max subscribers using the macOS desktop application. Users on other subscription tiers — Free, Pro, Team, or Enterprise — can join a waitlist for future access. Anthropic has signaled clear intentions to expand the feature's reach. The blog post explicitly mentions plans to add cross-device sync and bring Cowork to Windows as the company learns from the research preview. Cherny set expectations appropriately, describing the product as "early and raw, similar to what Claude Code felt like when it first launched." To access Cowork , Max subscribers can download or update the Claude macOS app and click on "Cowork" in the sidebar. The real question facing enterprise AI adoption For technical decision-makers, the implications of Cowork extend beyond any single product launch. The bottleneck for AI adoption is shifting — no longer is model intelligence the limiting factor, but rather workflow integration and user trust. Anthropic's goal, as the company puts it, is to make working with Claude feel less like operating a tool and more like delegating to a colleague. Whether mainstream users are ready to hand over folder access to an AI that might misinterpret their instructions remains an open question. But the speed of Cowork's development — a major feature built in ten days, possibly by the company's own AI — previews a future where the capabilities of these systems compound faster than organizations can evaluate them. The chatbot has learned to use a file manager. What it learns to use next is anyone's guess.

Nous Research's NousCoder-14B is an open-source coding model landing right in the Claude Code moment

admin-axhub · 2026-01-07

Nous Research , the open-source artificial intelligence startup backed by crypto venture firm Paradigm , released a new competitive programming model on Monday that it says matches or exceeds several larger proprietary systems — trained in just four days using 48 of Nvidia's latest B200 graphics processors . The model, called NousCoder-14B , is another entry in a crowded field of AI coding assistants, but arrives at a particularly charged moment: Claude Code , the agentic programming tool from rival Anthropic, has dominated social media discussion since New Year's Day, with developers posting breathless testimonials about its capabilities . The simultaneous developments underscore how quickly AI-assisted software development is evolving — and how fiercely companies large and small are competing to capture what many believe will become a foundational technology for how software gets written. type: embedded-entry-inline id: 74cSyrq6OUrp9SEQ5zOUSl NousCoder-14B achieves a 67.87 percent accuracy rate on LiveCodeBench v6 , a standardized evaluation that tests models on competitive programming problems published between August 2024 and May 2025. That figure represents a 7.08 percentage point improvement over the base model it was trained from, Alibaba's Qwen3-14B , according to Nous Research's technical report published alongside the release. "I gave Claude Code a description of the problem, it generated what we built last year in an hour," wrote Jaana Dogan , a principal engineer at Google responsible for the Gemini API, in a viral post on X last week that captured the prevailing mood around AI coding tools. Dogan was describing a distributed agent orchestration system her team had spent a year developing — a system Claude Code approximated from a three-paragraph prompt. The juxtaposition is instructive: while Anthropic's Claude Code has captured imaginations with demonstrations of end-to-end software development, Nous Research is betting that open-source alternatives trained on verifiable problems can close the gap — and that transparency in how these models are built matters as much as raw capability. How Nous Research built an AI coding model that anyone can replicate What distinguishes the NousCoder-14B release from many competitor announcements is its radical openness. Nous Research published not just the model weights but the complete reinforcement learning environment , benchmark suite, and training harness — built on the company's Atropos framework — enabling any researcher with sufficient compute to reproduce or extend the work . "Open-sourcing the Atropos stack provides the necessary infrastructure for reproducible olympiad-level reasoning research," noted one observer on X , summarizing the significance for the academic and open-source communities. The model was trained by Joe Li , a researcher in residence at Nous Research and a former competitive programmer himself. Li's technical report reveals an unexpectedly personal dimension: he compared the model's improvement trajectory to his own journey on Codeforces, the competitive programming platform where participants earn ratings based on contest performance. Based on rough estimates mapping LiveCodeBench scores to Codeforces ratings, Li calculated that NousCoder-14B's improvemen t— from approximately the 1600-1750 rating range to 2100-2200 — mirrors a leap that took him nearly two years of sustained practice between ages 14 and 16. The model accomplished the equivalent in four days. "Watching that final training run unfold was quite a surreal experience," Li wrote in the technical report. But Li was quick to note an important caveat that speaks to broader questions about AI efficiency: he solved roughly 1,000 problems during those two years, while the model required 24,000. Humans, at least for now, remain dramatically more sample-efficient learners. Inside the reinforcement learning system that trains on 24,000 competitive programming problems NousCoder-14B 's training process offers a window into the increasingly sophisticated techniques researchers use to improve AI reasoning capabilities through reinforcement learning. The approach relies on what researchers call "verifiable rewards" — a system where the model generates code solutions, those solutions are executed against test cases, and the model receives a simple binary signal: correct or incorrect. This feedback loop, while conceptually straightforward, requires significant infrastructure to execute at scale. Nous Research used Modal , a cloud computing platform, to run sandboxed code execution in parallel. Each of the 24,000 training problems contains hundreds of test cases on average, and the system must verify that generated code produces correct outputs within time and memory constraints — 15 seconds and 4 gigabytes, respectively. The training employed a technique called DAPO (Dynamic Sampling Policy Optimization) , which the researchers found performed slightly better than alternatives in their experiments. A key innovation involves "dynamic sampling" — discarding training examples where the model either solves all attempts or fails all attempts, since these provide no useful gradient signal for learning. The researchers also adopted "iterative context extension," first training the model with a 32,000-token context window before expanding to 40,000 tokens. During evaluation, extending the context further to approximately 80,000 tokens produced the best results, with accuracy reaching 67.87 percent. Perhaps most significantly, the training pipeline overlaps inference and verification — as soon as the model generates a solution, it begins work on the next problem while the previous solution is being checked. This pipelining, combined with asynchronous training where multiple model instances work in parallel, maximizes hardware utilization on expensive GPU clusters. The looming data shortage that could slow AI coding model progress Buried in Li's technical report is a finding with significant implications for the future of AI development: the training dataset for NousCoder-14B encompasses "a significant portion of all readily available, verifiable competitive programming problems in a standardized dataset format." In other words, for this particular domain, the researchers are approaching the limits of high-quality training data. "The total number of competitive programming problems on the Internet is roughly the same order of magnitude," Li wrote, referring to the 24,000 problems used for training. "This suggests that within the competitive programming domain, we have approached the limits of high-quality data." This observation echoes growing concern across the AI industry about data constraints. While compute continues to scale according to well-understood economic and engineering principles, training data is "increasingly finite," as Li put it. "It appears that some of the most important research that needs to be done in the future will be in the areas of synthetic data generation and data efficient algorithms and architectures," he concluded. The challenge is particularly acute for competitive programming because the domain requires problems with known correct solutions that can be verified automatically. Unlike natural language tasks where human evaluation or proxy metrics suffice, code either works or it doesn't — making synthetic data generation considerably more difficult. Li identified one potential avenue: training models not just to solve problems but to generate solvable problems, enabling a form of self-play similar to techniques that proved successful in game-playing AI systems. "Once synthetic problem generation is solved, self-play becomes a very interesting direction," he wrote. A $65 million bet that open-source AI can compete with Big Tech Nous Research has carved out a distinctive position in the AI landscape: a company committed to open-source releases that compete with — and sometimes exceed — proprietary alternatives. The company raised $50 million in April 2025 in a round led by Paradigm, the cryptocurrency-focused venture firm founded by Coinbase co-founder Fred Ehrsam. Total funding reached $65 million, according to some reports. The investment reflected growing interest in decentralized approaches to AI training, an area where Nous Research has developed its Psyche platform . Previous releases include Hermes 4 , a family of models that we reported " outperform ChatGPT without content restrictions ," and DeepHermes-3, which the company described as the first " toggle-on reasoning model " — allowing users to activate extended thinking capabilities on demand. The company has cultivated a distinctive aesthetic and community, prompting some skepticism about whether style might overshadow substance. "Ofc i'm gonna believe an anime pfp company. stop benchmarkmaxxing ffs," wrote one critic on X , referring to Nous Research's anime-style branding and the industry practice of optimizing for benchmark performance. Others raised technical questions. " Based on the benchmark, Nemotron is better ," noted one commenter, referring to Nvidia's family of language models. Another asked whether NousCoder-14B is "agentic focused or just 'one shot' coding" — a distinction that matters for practical software development, where iterating on feedback typically produces better results than single attempts. What researchers say must happen next for AI coding tools to keep improving The release includes several directions for future work that hint at where AI coding research may be heading. Multi-turn reinforcement learning tops the list. Currently, the model receives only a final binary reward — pass or fail — after generating a solution. But competitive programming problems typically include public test cases that provide intermediate feedback: compilation errors, incorrect outputs, time limit violations. Training models to incorporate this feedback across multiple attempts could significantly improve performance. Controlling response length also remains a challenge. The researchers found that incorrect solutions tended to be longer than correct ones, and response lengths quickly saturated available context windows during training — a pattern that various algorithmic modifications failed to resolve. Perhaps most ambitiously, Li proposed "problem generation and self-play" — training models to both solve and create programming problems. This would address the data scarcity problem directly by enabling models to generate their own training curricula. "Humans are great at generating interesting and useful problems for other competitive programmers, but it appears that there still exists a significant gap in LLM capabilities in creative problem generation," Li wrote. The model is available now on Hugging Face under an Apache 2.0 license. For researchers and developers who want to build on the work, Nous Research has published the complete Atropos training stack alongside it. What took Li two years of adolescent dedication to achieve—climbing from a 1600-level novice to a 2100-rated competitor on Codeforces—an AI replicated in 96 hours. He needed 1,000 problems. The model needed 24,000. But soon enough, these systems may learn to write their own problems, teach themselves, and leave human benchmarks behind entirely. The question is no longer whether machines can learn to code. It's whether they'll soon be better teachers than we ever were.

민원 1,200만 건의 시대 — 한국 공공기관의 AI 전환

admin-axhub · 2026-07-05

권익위 민원은 18년 사이 30배가 됐다. 사람을 30배 뽑을 수 없는 조직들이 택한 경로 — 국내 공공 AX의 현재.

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