Category: Manufacturing & Engineering AI

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The math behind the OpenAI Jalapeño chip

OpenAI’s financial trajectory hinges heavily on infrastructure costs, a reality that drove the development of the new custom OpenAI Jalapeño chip. Developed in collaboration with Broadcom, the application-specific integrated circuit (ASIC) represents a direct attempt to mitigate the heavy capital expenditure associated with third-party hardware.

While Nvidia currently commands an estimated 75% profit margin on its high-end processors, OpenAI operates on tighter margins, keeping roughly 33 cents of profit on each dollar generated after accounting for its massive operational expenses. The financial burden of running large language models at scale is severe.

Last year, keeping ChatGPT servers responsive had cost OpenAI a staggering US$8.4 billion. With the platform now attracting 900 million weekly users, that operational cost is projected to reach approximately US$14 billion this year. Over the next eight years, OpenAI has committed roughly US$1.4 trillion to computing power, a massive bet for a company currently generating US$25 billion in annual revenue.

Designing Hardware for LLM Inference

The OpenAI Jalapeño chip, dubbed as the company’s first “Intelligence Processor”, is built specifically for large language model (LLM) inference rather than general-purpose AI workloads. OpenAI provided the core architectural design based on its specific model roadmaps and serving systems, while Broadcom managed the silicon engineering and high-performance networking integration.

TSMC handles the physical manufacturing in Taiwan, and Celestica is tasked with building the board and rack systems. According to OpenAI, early lab samples are already running frontier workloads, including an unreleased GPT-5.3-Codex-Spark model, at target production frequency and power.

Richard Ho, head of OpenAI’s hardware program, noted that the architecture minimizes data movement to push realized utilization closer to its theoretical peak performance. Unlike general-purpose accelerators adapted from legacy AI workloads, this architecture specifically balances compute, memory, and networking resources to solve the data-movement bottlenecks native to interactive LLM serving.

To achieve this at scale, the platform integrates Broadcom’s Tomahawk networking silicon directly into the design, allowing the custom processors to communicate across massive, clustered data center environments.

The vertical integration flywheel

By moving into custom silicon, OpenAI shifts from being a mere software layer to a vertically integrated infrastructure company. This full-stack strategy spans the entire pipeline: chip architecture, software kernels, memory systems, network scheduling, and the final application layer. Much like Apple’s tight coupling of proprietary hardware and iOS, OpenAI can now optimize its infrastructure around its exact internal model roadmaps.

This integration feeds a continuous operational flywheel. Enhanced infrastructure efficiency lowers the cost of both training and serving models. More affordable serving leads to better, more responsive products, which drives user volume and revenue to be reinvested back into the next generation of custom infrastructure.

Overcoming the late-mover advantage

By introducing its own silicon, OpenAI enters a landscape where its primary competitors have spent nearly a decade developing proprietary hardware. Google began deploying its Tensor Processing Units (TPUs) in 2015 and now controls roughly a quarter of global AI computing capacity outside of Nvidia’s supply chain.

Amazon has shipped over one million of its custom chips, while Meta and Microsoft continue to scale their own infrastructure.

“Jalapeño is part of our long-term full-stack infrastructure strategy to make compute more abundant,” said Greg Brockman, president and co-founder of OpenAI. “By designing more of the stack ourselves, we can serve more intelligence with greater efficiency.”

To close this timeline gap, OpenAI accelerated the development phase. The OpenAI Jalapeño chip transitioned from a blank-slate design to manufacturing tape-out—the final step before physical production—in just nine months. The engineering teams achieved this timeline by utilizing OpenAI’s own language models to automate and optimize portions of the hardware design process.

This creates a unique feedback loop where the models served to users are actively being leveraged to build the physical infrastructure that will run future iterations. Initial deployment of the hardware into data centres is scheduled to begin by the end of 2026.

Broadcom CEO Hock Tan confirmed that the rollout will scale alongside infrastructure partners, including Microsoft, to prepare for gigawatt-scale data centre integration.

(Photo by OpenAI)

See also: Omio scales travel product development using OpenAI models

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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The post The math behind the OpenAI Jalapeño chip appeared first on AI News.

June 25, 2026
Mitigating vendor lock-in with Sakana AI Fugu multi-agent models

Sakana AI launched Fugu to orchestrate multi-agent operations and mitigate single-vendor dependency risks in enterprise deployments.

Enterprises face operational vulnerabilities when relying entirely on monolithic AI APIs. Japanese AI firm Sakana AI designed Fugu as a response to these concentration risks by creating an orchestration language model that calls upon a pool of varied models to complete multi-step tasks.

Users access this ecosystem through a single OpenAI-compatible endpoint. Fugu routes queries internally, deciding whether to resolve a prompt directly or to assemble a coordinated team of expert models for deeper analysis. The system handles model selection, delegation, verification, and synthesis internally. Engineering teams interact with what appears to be one model while a background system of specialists executes the actual computation.

Sakana AI targets the geopolitical and regulatory risks associated with AI sourcing. Recent export controls affecting Anthropic models like Fable and Mythos demonstrated that access to specific foundational architectures can vanish based on foreign policy decisions.

Fugu functions as a hedge against these sudden supply chain disruptions. The platform relies on a completely swappable agent pool. Fugu dynamically routes traffic around any restricted or degraded provider to maintain service continuity. Sakana AI states this capability provides the resilient architecture required for AI sovereignty.

Fugu deployment tiers

Two tiers are available to accommodate different operational latency requirements.

The standard Fugu model prioritises low latency for daily tasks, integrating into standard developer tools like Codex for live coding and code review. Organisations subject to strict data governance or privacy mandates can manually opt specific underlying models out of the standard Fugu routing pool.

Fugu Ultra targets complex, multi-step analytical problems that demand maximum accuracy. The Ultra variant coordinates a deeper pool of expert agents for intensive tasks such as academic paper reproduction, literature investigations, and patent analysis.

Sakana AI reports that Fugu Ultra performs competitively against leading closed models like Fable 5 and Mythos Preview across scientific, engineering, and reasoning benchmarks:

The orchestration method ensures companies can access top-tier computing capabilities without carrying the vendor concentration risk or export control exposure inherent to those closed models.

Implementation in cybersecurity

Almost 500 early users tested the system during an extended beta program focused on lengthy, multi-step computational workflows. With cybersecurity such a focus for models like Claude Mythos, engineering teams deployed Fugu Ultra to automate complete security assessment cycles.

Human operators issued one scoped instruction, and the orchestration engine executed the entire reconnaissance phase. The model successfully conducted cross-site scripting and SQL injection checks alongside thorough authentication reviews.

A participating cybersecurity engineer confirmed the model stayed strictly within its operational parameters and avoided initiating destructive actions against the target infrastructure. Fugu concluded the automated engagement by generating a clean vulnerability report complete with verifying evidence and exact retest steps for human remediation teams.

The implementation demonstrated that multi-agent routing maintains strict compliance boundaries while executing complex penetration testing sequences.

Software development teams also integrated Fugu Ultra into their primary code review pipelines to compare defect detection rates against established monolithic tools. The orchestration engine consistently outperformed baseline models in identifying logic flaws and security vulnerabilities within complex enterprise codebases.

“For code review, Fugu Ultra is significantly better than GPT-5.5. It gives comprehensive answers and finds the bugs others miss,” reported a software engineer involved in the beta deployment. “Where other tools flag about three issues, Fugu surfaced more than twenty. It’s become the model I run all my reviews through.”

Automated research and persona stability

Data science units deployed the system in an almost fully-automated research mode. Fugu Ultra successfully explored mathematical hypotheses, executed experimental code runs, interpreted failure states, and revised its own approaches to sustain progress over extended periods with minimal human intervention. This capability directly addresses the operational limitations of single-call models that require constant human prompting to recover from logic errors.

Leadership at an unnamed enterprise platform company identified long-term persona stability as a primary advantage during these extended sessions. Conventional monolithic architectures often suffer from context degradation and identity drift when processing extensive conversational histories.

“Raw output quality is on par with top frontier models, but Fugu showed unusually strong persona stability across long sessions, holding its identity where other models drift,” the executive stated. “For agent products, that may matter more than raw benchmark scores.”

Extended benchmark validation

Sakana AI built the internal routing logic upon extensive research into learned model orchestration. The technical foundation for the product stems from findings published in the company’s ICLR 2026 papers, specifically the Trinity and Conductor frameworks.

These academic foundations allow Fugu to process requests by understanding precisely when a task requires delegation versus direct resolution. The internal language model dictates communication protocols between the individual agents and structures the final synthesis of their separate computational outputs.

Validation testing against frontier AI competitors covered complex, open-ended disciplines ranging from financial time series prediction to mechanical design. Fugu also demonstrated high proficiency in niche physical logic tests and visual interpretation tasks, including solving the Rubik’s Cube and performing Japanese handwriting analysis. The capacity to excel in both quantitative financial modelling and qualitative image processing confirms the efficacy of the multi-agent orchestration approach.

Sakana AI designed the system to scale organically as the broader AI hardware and software market matures. Because the product relies entirely on learned orchestration logic rather than fixed operational rulesets, it automatically benefits from third-party innovations. Sakana AI plans to continuously expand the available pool of expert agents.

The engineering team will fold newly-released open-source tools and proprietary Sakana AI models into the routing pool as they become available. Both the standard Fugu and Fugu Ultra models are available to enterprise clients today.

See also: SAP and Google Cloud deploy agentic commerce architecture

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Mitigating vendor lock-in with Sakana AI Fugu multi-agent models appeared first on AI News.

June 22, 2026
How C3 AI agents will automate predictive maintenance for Shell

Shell will use agents from C3 AI to shift from basic anomaly detection towards fully-automated predictive maintenance.

The global energy giant is building on their current use of the C3 AI Reliability Suite, which already keeps tabs on more than 30,000 crucial pieces of equipment across upstream and downstream operations. Shell now intends to lean heavily into autonomous AI agents, putting them in charge of the entire maintenance lifecycle.

Going from that first warning sign all the way to a completed repair, this level of automation strips away the need for constant human oversight and makes sure the company’s resources are pointed exactly where they are needed most.

“This expanded partnership with Shell proves what’s possible when enterprise AI is fully operationalised at global scale for predictive maintenance—reducing unplanned downtime and delivering hundreds of millions of dollars in economic value,” said Stephen Ehikian, President of C3 AI.

“Shell has built mature AI predictive maintenance programs on our platform, and together we’re now pushing into agentic AI, advancing how this technology can further transform reliability, safety, efficiency, and operational performance.”

C3’s AI agents help Shell move past basic anomaly detection

In the beginning, Shell used machine learning simply to spot odd patterns in sensor data, giving engineers an early heads-up before things broke. To pull this off, the system ingests a massive amount of real-time operational technology (OT) data and mixes it with business context from ERP platforms such as SAP.

The next step introduces AI agents built for actual reasoning and independent action. While older systems stopped at pinging an engineer when things looked unusual, this next-generation framework independently investigates why an alert fired in the first place.

Once it pinpoints the root cause, the agent steps up to draft precise work orders, confirm part availability in the inventory, and generate procurement requests.

C3 AI’s platform handles the heavy lifting, providing a model-driven space to easily integrate high-frequency sensor feeds with structured financial and maintenance logs. These AI capabilities are trained to learn the normal operating baselines for specific gear, like pumps, turbines, and compressors.

The agentic layer sits on top of this foundation. Operators configure an individual agent for a given piece of equipment by defining its objectives and permitted responses. If the core machine learning models detect a deviation from normal operations, this agent activates, gathering extensive contextual data to build a complete picture of the situation. This context usually includes recent maintenance history, environmental conditions, and upstream process variables.

Using all that information, it suggests a fix backed by solid evidence. Human operators can then easily approve or override the plan. As the system proves itself over time, Shell can fully automate its responses to certain types of alerts. Connecting straight into systems like SAP is critical here, allowing the agent to work inside the exact same workflows that human planners already use.

The real impact of agentic AI for predictive maintenance

Putting agentic AI to work at this scale tackles the classic “last mile” headache in predictive maintenance. Many industrial companies can predict failures just fine, but turning those insights into fast, efficient action remains a challenge. Usually, engineers still have to manually dig through alerts, investigate the causes, and write up the work orders themselves.

Shell wants to shrink that timeline. By letting AI handle root cause analysis and work orders, the delay between a predicted failure and the actual fix drops. That directly improves equipment uptime and protects production.

Moving to a model where repairs only happen when the equipment condition actually demands it naturally saves money, simply because nobody is wasting time tinkering with perfectly fine machinery. Leaving healthy hardware alone also means it lasts much longer.

On top of the cost savings, stepping in before a catastrophe hits makes the whole operation much safer and cuts down on environmental risks, which is always top of mind in the energy sector.

“What Shell and C3 AI have built on Azure over the past several years is exactly what enterprise AI should look like—real applications, running in production, delivering measurable value at global scale,” commented Sandy Gupta, VP GISV, Software Development Companies at Microsoft.

This expanded rollout shows that we are finally talking about practical industrial AI production workflows instead of just algorithms. Rather than just the prediction itself, the real value comes from the system’s ability to act on it with barely any human oversight.

See also: Meta Business Agent drives AI-powered conversational commerce

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post How C3 AI agents will automate predictive maintenance for Shell appeared first on AI News.

June 9, 2026