Venture Beat

• Tariff turbulence exposes costly blind spots in supply chains and AI

  Presented by Celonis

  When tariff rates change overnight, companies have 48 hours to model alternatives and act before competitors secure the best options. At Celosphere 2025 in Munich, enterprises demonstrated how they’re turning that chaos into competitive advantage — with quantifiable results that separate winners from losers.

  Vinmar International: Theglobal plastics and chemicals distributor created a real-time digital twin of its $3B supply chain, cutting default expedites by more than 20% and improving delivery agility across global operations.

  Florida Crystals: One of America's largest cane sugar producers, the company unlocked millions in working capital and strengthened supply chain resilience by eliminating manual rework across Finance, Procurement, and Inbound Supply. AI pilots now extend gains into invoice processing, predictive maintenance, and order management.

  ASOS: The ecommerce fashion giant connected its end-to-end supply chain for full transparency, reducing process variation, accelerating speed-to-market, and improving customer experience at scale.

  The common thread here: process intelligence that bridges the gap traditional ERP systems can’t close — connecting operational dots across ERP, finance, and logistics systems when seconds matter.

  “The question isn’t whether disruptions will hit,” says Peter Budweiser, General Manager of Supply Chain at Celonis. “It’s whether your systems can show you what’s breaking fast enough to fix it.”

  That visibility gap costs the average company double-digit millions in working capital and competitive positioning. As 54% of supply chain leaders face disruptions daily, the pressure is shifting to AI agents that execute real actions: triggering purchase orders, rerouting shipments, adjusting inventory. But an autonomous agent acting on stale or siloed data can make million-dollar mistakes when tariff structures shift overnight.

  Tariffs, as old as trade itself, have become the ultimate stress test for enterprise AI — revealing whether companies truly understand their supply chains and whether their AI can be trusted to act.

  Modern ERP: Data rich, insight poor

  Supply chain leaders face a paradox: drowning in data while starving for insight. Traditional enterprise systems — SAP, Oracle, PeopleSoft — capture every transaction meticulously.

  SAP logs the purchase order. Oracle tracks the shipment. The warehouse system records inventory movement. Each performs its function, but when tariffs change and companies need to model alternative sourcing scenarios across all three simultaneously, the data sits in silos.

  “What’s changed is the speed at which disruptions cascade,” says Manik Sharma, Head of Supply Chain GTM AI at Celonis. “Traditional ERP systems weren’t built for today’s volatility.”

  Companies generate thousands of reports showing what happened last quarter. They struggle to answer what happens if tariffs increase 25% tomorrow and need to switch suppliers within days.

  Tariffs: The 48-hour scramble

  Global trade volatility has transformed tariffs from predictable costs into strategic weapons. When new rates drop with unprecedented frequency, input costs spike across suppliers, finance teams scramble to calculate margin impact, and procurement races to identify alternatives buried in disconnected systems where no one knows if switching suppliers delays shipments or violates contracts.

  By hour 48, competitors who already modeled scenarios execute supplier switches while late movers face capacity constraints and premium pricing.

  Process intelligence changes that dynamic by allowing businesses to continuously model “what-if” scenarios, showing leaders how tariff changes cascade through suppliers, contracts, production lines, warehouses, and customers. When rates hit, companies can move within hours instead of days.

  No AI without PI: Why process intelligence is non-negotiable for supply chains

  AI and supply chains are mutually dependent: AI needs operational context, and supply chains need AI to keep pace with volatility. But here's the truth — there is no AI without PI. Without process intelligence, AI agents operate blindly.

  The ongoing SAP migration wave illustrates why. An estimated 85–90% of SAP customers are still moving from ECC to S/4HANA. Moving to newer databases doesn’t solve supply chain visibility — it provides faster access to the same fragmented data.

  Kerry Brown, a transformation evangelist at Celonis, sees this across industries.

  “Organizations are shifting from PeopleSoft to Oracle, or EBS to Fusion. The bulk is in SAP,” she explains. “But what they really need isn’t a new ERP. They need to understand how work actually flows across systems they already have.”

  That requires end-to-end operational context. Process intelligence provides this by enabling companies to extract and connect event data across systems, showing how processes execute in real time.

  This distinction becomes critical when deploying autonomous agents. When visibility is fragmented, autonomous agents can easily make decisions that appear rational locally but create downstream disruption. With real-time context, AI can operate with clarity and precision, and supply chains can stay ahead of tariff-driven disruption.

  Digital Twins: Powering real-time response

  The companies highlighted at Celosphere all applied the same principle: understand how processes run across systems in real time. Celonis PI creates a digital twin above existing systems, using its Process Intelligence Graph to link orders, shipments, invoices, and payments end-to-end. Dependencies that traditional integrations miss become visible. A delay in SAP instantly reveals its impact across Oracle, warehouse scheduling, and customer delivery commitments.

  “The platform brings together process data spanning systems and departments, enriched with business context that powers AI agents to transform operations effectively,” says Daniel Brown, Chief Product Officer at Celonis.

  With this cross-system awareness, Celonis coordinates actions across complex workflows involving AI agents, humans, and automations — especially critical when tariffs force rapid decisions about suppliers, shipments, and customers.

  Zero-copy integration enables instant modeling

  A key advancement unveiled at Celosphere — zero-copy integrationwith Databricks — removes another barrier. Traditionally, analyzing supply chain data meant copying from source systems into central warehouses, creating data latency.

  Celonis Data Core now integrates directly with platforms like Databricks and Microsoft Fabric, querying billions of records in near real time without duplication. When trade policy shifts, companies model alternatives instantly, not after overnight data refresh cycles.

  Enhanced Task Mining extends this by connecting desktop activity — keystrokes, mouse clicks, screen scrolls — to business processes. This exposes manual work invisible to system logs: spreadsheet gymnastics, email negotiations, phone calls that keep supply chains moving during urgent changes.

  Competitive advantage in volatile markets

  Most companies can’t rip out and replace systems running critical operations — nor should they. Process intelligence offers a different path: compose workflows from existing systems, deploy AI where it creates value, and adapt continuously as conditions change. This “Free the Process” movement liberates companies from rigid architectures without forcing wholesale replacement.

  As global trade volatility intensifies, the companies that model will move faster, make smarter decisions, and turn tariff chaos into competitive advantage — all while existing ERPs keep running.

  When the next wave of tariffs hits — and it will — companies won’t have days to respond. They’ll have hours. The question isn’t whether your ERP captures the data. It’s whether your systems connect the dots fast enough to matter.

  Missed Celosphere 2025?Catch up with all the highlights here.

  Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contactsales@venturebeat.com.

• Workspace Studio aims to solve the real agent problem: Getting employees to use them

  One problem enterprises face is getting employees to actually use the AI agents their dev teams have built. 

  Google, which has already shipped many AI tools through its Workspace apps, has made Google Workspace Studio generally available to give more employees access to design, manage and share AI agents, further democratizing agentic workflows. This puts Google directly in competition with Microsoft’s Copilot and undercuts some integrations that brought OpenAI’s ChatGPT into enterprise applications. 

  Workspace Studio is powered by Gemini 3, and while it primarily targets business teams rather than developers, it offers builders a way to offload lower-priority agent tasks.  

  “We’ve all lost countless hours to the daily grind: Sifting through emails, juggling calendar logistics and chasing follow-up tasks,” Farhaz Karmali, product director for the Google Workspace Ecosystem, wrote in a blog post. “Legacy automation tools tried to help, but they were simply too rigid and technical for the everyday user. That’s why we’re bringing custom agents directly into Workspace with Studio — so you can delegate these repetitive tasks to agents that can reason, understand context and handle the work that used to slow you down.”

  The platform can bring agents to Workspace apps such as Google Docs and Sheets, as well as to third-party tools like Salesforce or Jira.

  More AI in applications 

  Interest in AI agents continues to grow, and while many enterprises have begun deploying them in their workflows, they're finding it isn’t as easy to get users on board as expected. The problem is that using agents can sometimes break employees out of their flow, so organizations have to figure out how to integrate agents where users are already fully engaged. The most common way of interacting with agents so far remains a chat screen. 

  AWS released Quick Sight in hopesof attracting more front- and middle-office workers to use AI agents, although access to agents is still through a chatbot. OpenAI has desktop integrations that bring ChatGPT to specific apps. And, of course, Microsoft Copilot helped was ahead of this trend. 

  Google has an advantage that only Microsoft rivals: It already offers applications that most people use. Enterprise employees use Google Workspace applications, host data and documents on Drive and send emails through Gmail. 

  This means Google can easily get the context enterprises need to power their agents and reach millions of users. 

  If people build agents through Workspace Studio, the platform can prove that agents targeting workplace applications, not just Google Docs, but also Microsoft Word, could be a winning strategy to increase agent adoption from employees.  

  Templatizing agent creation

  Enterprise employees can choose from a template or write out what they need in a prompt window. 

  A look around the Workspace Studio platform showed templates such as “auto-create tasks when files are added to a folder” or “create Jira issues for emails with action issues.”

  Karmali said Workspace Studio is being “deeply integrated with Workspace apps like Gmail, Drive and Chat,” and agents built on the platform can “understand the full context of your work.” 

  “This allows them to provide help that matches your company’s policies and processes while generating personalized content in your tone and style," he said. "You can even view your agent activity directly from the side panels of your favorite Workspace apps." 

  Teams can extend agents to third-party enterprise platforms, but they can also configure custom steps to integrate with other tools. 

• AI has redefined the talent game. Here’s how leaders are responding.

  Presented by Indeed

  As AI continues to reshape how we work, organizations are rethinking what skills they need, how they hire, and how they retain talent. According to Indeed’s 2025 Tech Talent report, tech job postings are still down more than 30% from pre-pandemic highs, yet demand for AI expertise has never been greater. New roles are emerging almost overnight, from prompt engineers to AI operations managers, and leaders are under growing pressure to close skill gaps while supporting their teams through change.

  Shibani Ahuja, SVP of enterprise IT strategy at Salesforce; Matt Candy, global managing partner of generative AI strategy and transformation at IBM; and Jessica Hardeman, global head of attraction and engagement at Indeed came together for a recent roundtable conversation about the future of tech talent strategy, from hiring and reskilling to how it's reshaping the workforce.

  Strategies for sourcing talent

  To find the right candidates, organizations need to be certain their communication is clear from the get-go, and that means beginning with a well-thought-out job description, Hardeman said.

  "How clearly are you outlining the skills that are actually required for the role, versus using very high-level or ambiguous language," she said. "Something that I also highly recommend is skill-cluster sourcing. We use that to identify candidates that might be adjacent to these harder-to-find niche skills. That’s something we can upskill people into. For example, skills that are in distributed computing or machine learning frameworks also share other high-value capabilities. Using these clusters can help recruiters identify candidates that may not have that exact skill set you’re looking for, but can quickly upskill into it."

  Recruiters should also be upskilled, able to spot that potential in candidates. And once they're hired, companies have to be intentional about how they’re growing talent from the day they step in the door.

  "What that means in the near term is focusing on the mentorship, embedding that AI fluency into their onboarding experience, into their growth, into their development," she said. "That means offering upskilling that teaches not just the tools they’ll need, but how to think with those tools and alongside those. The new early career sweet spot is where technical skills meet our human strengths. Curiosity. Communication. Data judgment. Workflow design. Those are the things that AI cannot replicate or replace. We have to create mentorship and sponsorship opportunities. Well-being and culture are critical components to ensuring that we’re creating good places for that early-in-career talent to land."

  How work will evolve along AI

  As AI becomes embedded into daily technical work, organizations are rethinking what it means to be a developer, designer, or engineer. Instead of automating roles end to end, companies are increasingly building AI agents that act as teammates, supporting workers across the entire software development lifecycle.

  Candy explained that IBM is already seeing this shift in action through its Consulting Advantage platform, which serves as a unified AI experience layer for consultants and technical teams.

  “This is a platform that every one of our consultants works with,” he said. “It’s supported by every piece of AI technology and model out there. It’s the place where our consultants can access thousands of agents that help them in each job role and activity they’re doing.”

  These aren’t just prebuilt tools — teams can create and publish their own agents into an internal marketplace. That has sparked a systematic effort to map every task across traditional tech roles and build agents to enhance them.

  “If I think about your traditional designer, DevOps engineer, AI Ops engineer — what are all the different agents that are supporting them in those activities?” Candy said. “It’s far more than just coding. Tools like Cursor, Windsurf, and GitHub Copilot accelerate coding, but that’s only one part of delivering software end to end. We’re building agents to support people at every stage of that journey.”

  Candy said this shift leads toward a workplace where AI becomes a collaborative partner rather than a replacement, something that enables tech workers to spend more time on creative, strategic, and human-centered tasks.

  "This future where employees have agents working alongside them, taking care of some of these repetitive activities, focusing on higher-value strategic work where human skills are innately important, I think becomes right at the heart of that,” he explained. “You have to unleash the organization to be able to think and rethink in that way."

  A lot of that depends on the mindset of company leaders, Ahuja said.

  "I can see the difference between leaders that look at AI as cost-cutting, reduction — it’s a bottom-line activity,” she said. “And then there are organizations that are starting to shift their mindset to say, no, the goal is not about replacing people. It’s about reimagining the work to make us humans more human, ironically. For some leaders that’s the story their PR teams have told them to say. But for those that actually believe that AI is about helping us become more human, it’s interesting how they’re bringing that to life and bridging this gap between humanity and digital labor."

  Shifting the culture toward AI

  The companies that are most successful at navigating the obstacles around successful AI implementation and culture change make employees their first priority, Ahuja added. They prioritize use cases that solve the most boring problems that are burdening their teams, demonstrating how AI will help, as opposed to looking at what the maximum number of jobs automation can replace.

  "They’re thinking of it as preserving human accountability, so in high-stakes moments, people will still make that final call," she said. "Looking at where AI is going to excel at scale and speed with pattern recognition, leaving that space for humans to bring their judgement, their ethics, and their emotional intelligence. It seems like a very subtle shift, but it’s pretty big in terms of where it starts at the beginning of an organization and how it trickles down."

  It's also important to build a level of comfort in using AI in employees’ day-to-day work. Salesforce created a Slack chat called Bite-Sized AI in which they encourage every colleague, including company leaders, to talk about where they're using AI and why, and what hacks they've found.

  "That’s creating a safe space," Ahuja explained. "It’s creating that psychological safety — that this isn’t just a buzzword. We’re trying to encourage it through behavior."

  "This is all about how you ignite, especially in big enterprises, the kind of passion and fire inside everyone’s belly," Candy added. "Storytelling, showing examples of what great looks like. The expression is 'demos, not memos'. Stop writing PowerPoint slides explaining what we're going to do and actually getting into the tools to show it in real life.”

  AI makes that continuous learning a non-negotiable, Hardeman added, with companies training employees in understanding how to use the AI tools they're provided, and that goes a long way toward building that AI culture.

  "We view upskilling as a retention lever and a performance driver," she said. "It creates that confidence, it reduces the fear around AI adoption. It helps people see a future for themselves as the technology evolves. AI didn’t just raise the bar on skills. It raised the bar on how we’re trying to support our people. It’s important that we are also rising to that occasion, and we’re not just raising expectations on the folks that we work with."

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• Amazon's new AI can code for days without human help. What does that mean for software engineers?

  Amazon Web Services on Tuesday announced a new class of artificial intelligence systems called "frontier agents" that can work autonomously for hours or even days without human intervention, representing one of the most ambitious attempts yet to automate the full software development lifecycle.

  The announcement, made during AWS CEO Matt Garman's keynote address at the company's annual re:Invent conference, introduces three specialized AI agents designed to act as virtual team members: Kiro autonomous agent for software development, AWS Security Agent for application security, and AWS DevOps Agent for IT operations.

  The move signals Amazon's intent to leap ahead in the intensifying competition to build AI systems capable of performing complex, multi-step tasks that currently require teams of skilled engineers.

  "We see frontier agents as a completely new class of agents," said Deepak Singh, vice president of developer agents and experiences at Amazon, in an interview ahead of the announcement. "They're fundamentally designed to work for hours and days. You're not giving them a problem that you want finished in the next five minutes. You're giving them complex challenges that they may have to think about, try different solutions, and get to the right conclusion — and they should do that without intervention."

  Why Amazon believes its new agents leave existing AI coding tools behind

  The frontier agents differ from existing AI coding assistants like GitHub Copilot or Amazon's own CodeWhisperer in several fundamental ways.

  Current AI coding tools, while powerful, require engineers to drive every interaction. Developers must write prompts, provide context, and manually coordinate work across different code repositories. When switching between tasks, the AI loses context and must start fresh.

  The new frontier agents, by contrast, maintain persistent memory across sessions and continuously learn from an organization's codebase, documentation, and team communications. They can independently determine which code repositories require changes, work on multiple files simultaneously, and coordinate complex transformations spanning dozens of microservices.

  "With a current agent, you would go microservice by microservice, making changes one at a time, and each change would be a different session with no shared context," Singh explained. "With a frontier agent, you say, 'I need to solve this broad problem.' You point it to the right application, and it decides which repos need changes."

  The agents exhibit three defining characteristics that AWS believes set them apart: autonomy in decision-making, the ability to scale by spawning multiple agents to work on different aspects of a problem simultaneously, and the capacity to operate independently for extended periods.

  "A frontier agent can decide to spin up 10 versions of itself, all working on different parts of the problem at once," Singh said.

  How each of the three frontier agents tackles a different phase of development

  Kiro autonomous agent serves as a virtual developer that maintains context across coding sessions and learns from an organization's pull requests, code reviews, and technical discussions. Teams can connect it to GitHub, Jira, Slack, and internal documentation systems. The agent then acts like a teammate, accepting task assignments and working independently until it either completes the work or requires human guidance.

  AWS Security Agent embeds security expertise throughout the development process, automatically reviewing design documents and scanning pull requests against organizational security requirements. Perhaps most significantly, it transforms penetration testing from a weeks-long manual process into an on-demand capability that completes in hours.

  SmugMug, a photo hosting platform, has already deployed the security agent. "AWS Security Agent helped catch a business logic bug that no existing tools would have caught, exposing information improperly," said Andres Ruiz, staff software engineer at the company. "To any other tool, this would have been invisible. But the ability for Security Agent to contextualize the information, parse the API response, and find the unexpected information there represents a leap forward in automated security testing."

  AWS DevOps Agent functions as an always-on operations team member, responding instantly to incidents and using its accumulated knowledge to identify root causes. It connects to observability tools including Amazon CloudWatch, Datadog, Dynatrace, New Relic, and Splunk, along with runbooks and deployment pipelines.

  Commonwealth Bank of Australia tested the DevOps agent by replicating a complex network and identity management issue that typically requires hours for experienced engineers to diagnose. The agent identified the root cause in under 15 minutes.

  "AWS DevOps Agent thinks and acts like a seasoned DevOps engineer, helping our engineers build a banking infrastructure that's faster, more resilient, and designed to deliver better experiences for our customers," said Jason Sandry, head of cloud services at Commonwealth Bank.

  Amazon makes its case against Google and Microsoft in the AI coding wars

  The announcement arrives amid a fierce battle among technology giants to dominate the emerging market for AI-powered development tools. Google has made significant noise in recent weeks with its own AI coding capabilities, while Microsoft continues to advance GitHub Copilot and its broader AI development toolkit.

  Singh argued that AWS holds distinct advantages rooted in the company's 20-year history operating cloud infrastructure and Amazon's own massive software engineering organization.

  "AWS has been the cloud of choice for 20 years, so we have two decades of knowledge building and running it, and working with customers who've been building and running applications on it," Singh said. "The learnings from operating AWS, the knowledge our customers have, the experience we've built using these tools ourselves every day to build real-world applications—all of that is embodied in these frontier agents."

  He drew a distinction between tools suitable for prototypes versus production systems. "There's a lot of things out there that you can use to build your prototype or your toy application. But if you want to build production applications, there's a lot of knowledge that we bring in as AWS that apply here."

  The safeguards Amazon built to keep autonomous agents from going rogue

  The prospect of AI systems operating autonomously for days raises immediate questions about what happens when they go off track. Singh described multiple safeguards built into the system.

  All learnings accumulated by the agents are logged and visible, allowing engineers to understand what knowledge influences the agent's decisions. Teams can even remove specific learnings if they discover the agent has absorbed incorrect information from team communications.

  "You can go in and even redact that from its knowledge like, 'No, we don't want you to ever use this knowledge,'" Singh said. "You can look at the knowledge like it's almost—it's like looking at your neurons inside your brain. You can disconnect some."

  Engineers can also monitor agent activity in real-time and intervene when necessary, either redirecting the agent or taking over entirely. Most critically, the agents never commit code directly to production systems. That responsibility remains with human engineers.

  "These agents are never going to check the code into production. That is still the human's responsibility," Singh emphasized. "You are still, as an engineer, responsible for the code you're checking in, whether it's generated by you or by an agent working autonomously."

  What frontier agents mean for the future of software engineering jobs

  The announcement inevitably raises concerns about the impact on software engineering jobs. Singh pushed back against the notion that frontier agents will replace developers, framing them instead as tools that amplify human capabilities.

  "Software engineering is craft. What's changing is not, 'Hey, agents are doing all the work.' The craft of software engineering is changing—how you use agents, how do you set up your code base, how do you set up your prompts, how do you set up your rules, how do you set up your knowledge bases so that agents can be effective," he said.

  Singh noted that senior engineers who had drifted away from hands-on coding are now writing more code than ever. "It's actually easier for them to become software engineers," he said.

  He pointed to an internal example where a team completed a project in 78 days that would have taken 18 months using traditional practices. "Because they were able to use AI. And the thing that made it work was not just the fact that they were using AI, but how they organized and set up their practices of how they built that software were maximized around that."

  How Amazon plans to make AI-generated code more trustworthy over time

  Singh outlined several areas where frontier agents will evolve over the coming years. Multi-agent architectures, where systems of specialized agents coordinate to solve complex problems, represent a major frontier. So does the integration of formal verification techniques to increase confidence in AI-generated code.

  AWS recently introduced property-based testing in Kiro, which uses automated reasoning to extract testable properties from specifications and generate thousands of test scenarios automatically.

  "If you have a shopping cart application, every way an order can be canceled, and how it might be canceled, and the way refunds are handled in Germany versus the US—if you're writing a unit test, maybe two, Germany and US, but now, because you have this property-based testing approach, your agent can create a scenario for every country you operate in and test all of them automatically for you," Singh explained.

  Building trust in autonomous systems remains the central challenge. "Right now you still require tons of human guardrails at every step to make sure that the right thing happens. And as we get better at these techniques, you will use less and less, and you'll be able to trust the agents a lot more," he said.

  Amazon's bigger bet on autonomous AI stretches far beyond writing code

  The frontier agents announcement arrived alongside a cascade of other news at re:Invent 2025. AWS kicked off the conference with major announcements on agentic AI capabilities, customer service innovations, and multicloud networking.

  Amazon expanded its Nova portfolio with four new models delivering industry-leading price-performance across reasoning, multimodal processing, conversational AI, code generation, and agentic tasks. Nova Forge pioneers "open training," giving organizations access to pre-trained model checkpoints and the ability to blend proprietary data with Amazon Nova-curated datasets.

  AWS also added 18 new open weight models to Amazon Bedrock, reinforcing its commitment to offering a broad selection of fully managed models from leading AI providers. The launch includes new models from Mistral AI, Google's Gemma 3, MiniMax's M2, NVIDIA's Nemotron, and OpenAI's GPT OSS Safeguard.

  On the infrastructure side, Amazon EC2 Trn3 UltraServers, powered by AWS's first 3nm AI chip, pack up to 144 Trainium3 chips into a single integrated system, delivering up to 4.4x more compute performance and 4x greater energy efficiency than the previous generation. AWS AI Factories provides enterprises and government organizations with dedicated AWS AI infrastructure deployed in their own data centers, combining NVIDIA GPUs, Trainium chips, AWS networking, and AI services like Amazon Bedrock and SageMaker AI.

  All three frontier agents launched in preview on Tuesday. Pricing will be announced when the services reach general availability.

  Singh made clear the company sees applications far beyond coding. "These are the first frontier agents we are releasing, and they're in the software development lifecycle," he said. "The problems and use cases for frontier agents—these agents that are long running, capable of autonomy, thinking, always learning and improving—can be applied to many, many domains."

  Amazon, after all, operates satellite networks, runs robotics warehouses, and manages one of the world's largest e-commerce platforms. If autonomous agents can learn to write code on their own, the company is betting they can eventually learn to do just about anything else.

• Mistral launches Mistral 3, a family of open models designed to run on laptops, drones, and edge devices

  Mistral AI, Europe's most prominent artificial intelligence startup, is releasing its most ambitious product suite to date: a family of 10 open-source models designed to run everywhere from smartphones and autonomous drones to enterprise cloud systems, marking a major escalation in the company's challenge to both U.S. tech giants and surging Chinese competitors.

  The Mistral 3 family, launching today, includes a new flagship model called Mistral Large 3 and a suite of smaller "Ministral 3" models optimized for edge computing applications. All models will be released under the permissive Apache 2.0 license, allowing unrestricted commercial use — a sharp contrast to the closed systems offered by OpenAI, Google, and Anthropic.

  The release is a pointed bet by Mistral that the future of artificial intelligence lies not in building ever-larger proprietary systems, but in offering businesses maximum flexibility to customize and deploy AI tailored to their specific needs, often using smaller models that can run without cloud connectivity.

  "The gap between closed and open source is getting smaller, because more and more people are contributing to open source, which is great," Guillaume Lample, Mistral's chief scientist and co-founder, said in an exclusive interview with VentureBeat. "We are catching up fast."

  Why Mistral is choosing flexibility over frontier performance in the AI race

  The strategic calculus behind Mistral 3 diverges sharply from recent model releases by industry leaders. While OpenAI, Google, and Anthropic have focused recent launches on increasingly capable "agentic" systems — AI that can autonomously execute complex multi-step tasks — Mistral is prioritizing breadth, efficiency, and what Lample calls "distributed intelligence."

  Mistral Large 3, the flagship model, employs a Mixture of Experts architecture with 41 billion active parameters drawn from a total pool of 675 billion parameters. The model can process both text and images, handles context windows up to 256,000 tokens, and was trained with particular emphasis on non-English languages — a rarity among frontier AI systems.

  "Most AI labs focus on their native language, but Mistral Large 3 was trained on a wide variety of languages, making advanced AI useful for billions who speak different native languages," the company said in a statement reviewed ahead of the announcement.

  But the more significant departure lies in the Ministral 3 lineup: nine compact models across three sizes (14 billion, 8 billion, and 3 billion parameters) and three variants tailored for different use cases. Each variant serves a distinct purpose: base models for extensive customization, instruction-tuned models for general chat and task completion, and reasoning-optimized models for complex logic requiring step-by-step deliberation.

  The smallest Ministral 3 models can run on devices with as little as 4 gigabytes of video memory using 4-bit quantization — making frontier AI capabilities accessible on standard laptops, smartphones, and embedded systems without requiring expensive cloud infrastructure or even internet connectivity. This approach reflects Mistral's belief that AI's next evolution will be defined not by sheer scale, but by ubiquity: models small enough to run on drones, in vehicles, in robots, and on consumer devices.

  How fine-tuned small models beat expensive large models for enterprise customers

  Lample's comments reveal a business model fundamentally different from that of closed-source competitors. Rather than competing primarily on benchmark performance, Mistral is targeting enterprise customers frustrated by the cost and inflexibility of proprietary systems.

  "Sometimes customers say, 'Is there a use case where the best closed-source model isn't working?' If that's the case, then they're essentially stuck," Lample explained. "There's nothing they can do. It's the best model available, and it's not working out of the box."

  This is where Mistral's approach diverges. When a generic model fails, the company deploys engineering teams to work directly with customers, analyzing specific problems, creating synthetic training data, and fine-tuning smaller models to outperform larger general-purpose systems on narrow tasks.

  "In more than 90% of cases, a small model can do the job, especially if it's fine-tuned. It doesn't have to be a model with hundreds of billions of parameters, just a 14-billion or 24-billion parameter model," Lample said. "So it's not only much cheaper, but also faster, plus you have all the benefits: you don't need to worry about privacy, latency, reliability, and so on."

  The economic argument is compelling. Multiple enterprise customers have approached Mistral after building prototypes with expensive closed-source models, only to find deployment costs prohibitive at scale, according to Lample.

  "They come back to us a couple of months later because they realize, 'We built this prototype, but it's way too slow and way too expensive,'" he said.

  Where Mistral 3 fits in the increasingly crowded open-source AI market

  Mistral's release comes amid fierce competition on multiple fronts. OpenAI recently released GPT-5.1 with enhanced agentic capabilities. Google launched Gemini 3 with improved multimodal understanding. Anthropic released Opus 4.5 on the same day as this interview, with similar agent-focused features.

  But Lample argues those comparisons miss the point. "It's a little bit behind. But I think what matters is that we are catching up fast," he acknowledged regarding performance against closed models. "I think we are maybe playing a strategic long game."

  That long game involves a different competitive set: primarily open-source models from Chinese companies like DeepSeek and Alibaba's Qwen series, which have made remarkable strides in recent months.

  Mistral differentiates itself through multilingual capabilities that extend far beyond English or Chinese, multimodal integration handling both text and images in a unified model, and what the company characterizes as superior customization through easier fine-tuning.

  "One key difference with the models themselves is that we focused much more on multilinguality," Lample said. "If you look at all the top models from [Chinese competitors], they're all text-only. They have visual models as well, but as separate systems. We wanted to integrate everything into a single model."

  The multilingual emphasis aligns with Mistral's broader positioning as a European AI champion focused on digital sovereignty — the principle that organizations and nations should maintain control over their AI infrastructure and data.

  Building beyond models: Mistral's full-stack enterprise AI platform strategy

  Mistral 3's release builds on an increasingly comprehensive enterprise AI platform that extends well beyond model development. The company has assembled a full-stack offering that differentiates it from pure model providers.

  Recent product launches include Mistral Agents API, which combines language models with built-in connectors for code execution, web search, image generation, and persistent memory across conversations; Magistral, the company's reasoning model designed for domain-specific, transparent, and multilingual reasoning; and Mistral Code, an AI-powered coding assistant bundling models, an in-IDE assistant, and local deployment options with enterprise tooling.

  The consumer-facing Le Chat assistant has been enhanced with Deep Research mode for structured research reports, voice capabilities, and Projects for organizing conversations into context-rich folders. More recently, Le Chat gained a connector directory with 20+ enterprise integrations powered by the Model Context Protocol (MCP), spanning tools like Databricks, Snowflake, GitHub, Atlassian, Asana, and Stripe.

  In October, Mistral unveiled AI Studio, a production AI platform providing observability, agent runtime, and AI registry capabilities to help enterprises track output changes, monitor usage, run evaluations, and fine-tune models using proprietary data.

  Mistral now positions itself as a full-stack, global enterprise AI company, offering not just models but an application-building layer through AI Studio, compute infrastructure, and forward-deployed engineers to help businesses realize return on investment.

  Why open source AI matters for customization, transparency and sovereignty

  Mistral's commitment to open-source development under permissive licenses is both an ideological stance and a competitive strategy in an AI landscape increasingly dominated by closed systems.

  Lample elaborated on the practical benefits: "I think something that people don't realize — but our customers know this very well — is how much better any model can actually improve if you fine tune it on the task of interest. There's a huge gap between a base model and one that's fine-tuned for a specific task, and in many cases, it outperforms the closed-source model."

  The approach enables capabilities impossible with closed systems: organizations can fine-tune models on proprietary data that never leaves their infrastructure, customize architectures for specific workflows, and maintain complete transparency into how AI systems make decisions — critical for regulated industries like finance, healthcare, and defense.

  This positioning has attracted government and public sector partnerships. The company launched "AI for Citizens" in July 2025, an initiative to "help States and public institutions strategically harness AI for their people by transforming public services" and has secured strategic partnerships with France's army and job agency, Luxembourg's government, and various European public sector organizations.

  Mistral's transatlantic AI collaboration goes beyond European borders

  While Mistral is frequently characterized as Europe's answer to OpenAI, the company views itself as a transatlantic collaboration rather than a purely European venture. The company has teams across both continents, with co-founders spending significant time with customers and partners in the United States, and these models are being trained in partnerships with U.S.-based teams and infrastructure providers.

  This transatlantic positioning may prove strategically important as geopolitical tensions around AI development intensify. The recent ASML investment, a €1.7 billion ($1.5 billion) funding round led by the Dutch semiconductor equipment manufacturer, signals deepening collaboration across the Western semiconductor and AI value chain at a moment when both Europe and the United States are seeking to reduce dependence on Chinese technology.

  Mistral's investor base reflects this dynamic: the Series C round included participation from U.S. firms Andreessen Horowitz, General Catalyst, Lightspeed, and Index Ventures alongside European investors like France's state-backed Bpifrance and global players like DST Global and Nvidia.

  Founded in May 2023 by former Google DeepMind and Meta researchers, Mistral has raised roughly $1.05 billion (€1 billion) in funding. The company was valued at $6 billion in a June 2024 Series B, then more than doubled its valuation in a September Series C.

  Can customization and efficiency beat raw performance in enterprise AI?

  The Mistral 3release crystallizes a fundamental question facing the AI industry: Will enterprises ultimately prioritize the absolute cutting-edge capabilities of proprietary systems, or will they choose open, customizable alternatives that offer greater control, lower costs, and independence from big tech platforms?

  Mistral's answer is unambiguous. The company is betting that as AI moves from prototype to production, the factors that matter most shift dramatically. Raw benchmark scores matter less than total cost of ownership. Slight performance edges matter less than the ability to fine-tune for specific workflows. Cloud-based convenience matters less than data sovereignty and edge deployment.

  It's a wager with significant risks. Despite Lample's optimism about closing the performance gap, Mistral's models still trail the absolute frontier. The company's revenue, while growing, reportedly remains modest relative to its nearly $14 billion valuation. And competition intensifies from both well-funded Chinese rivals making remarkable open-source progress and U.S. tech giants increasingly offering their own smaller, more efficient models.

  But if Mistral is right — if the future of AI looks less like a handful of cloud-based oracles and more like millions of specialized systems running everywhere from factory floors to smartphones — then the company has positioned itself at the center of that transformation.

  The release of Mistral 3is the most comprehensive expression yet of that vision: 10 models, spanning every size category, optimized for every deployment scenario, available to anyone who wants to build with them.

  Whether "distributed intelligence" becomes the industry's dominant paradigm or remains a compelling alternative serving a narrower market will determine not just Mistral's fate, but the broader question of who controls the AI future — and whether that future will be open.

  For now, the race is on. And Mistral is betting it can win not by building the biggest model, but by building everywhere else.