Large language models continue to struggle with hallucinations, presenting a major roadblock for real-world enterprise applications. Reducing these errors is a messy business, forcing model developers to navigate a strict tradeoff where eliminating factual errors often suppresses valid answers.

In a new paper, Google researchers introduce the concept of “faithful uncertainty,” a metacognitive technique that aligns a model’s response with its internal confidence. This alignment allows the model to offer appropriately hedged hypotheses, such as “My best guess is,” instead of defaulting to an unhelpful “answer-or-abstain” binary.

In real-world agentic AI applications, this metacognitive awareness acts as an essential control layer. It empowers autonomous systems to accurately determine when their internal knowledge is sufficient and when they must dynamically trigger external tools or search APIs to resolve deficits.

The utility tax of current mitigation strategies

Understanding why LLMs hallucinate hinges on separating two capabilities: a model knowing facts versus knowing what is known. Historically, most factuality gains in AI have come from expanding the knowledge boundary, meaning developers simply pack more facts into the model’s parameters through larger scale and more training data.

However, expanding a model’s knowledge does not automatically improve its boundary awareness, which is its ability to distinguish the known from the unknown and recognize its own limitations.

“There are broadly two ways to improve LLM factuality,” Gal Yona, Research Scientist at Google and co-author of the paper, told VentureBeat. The first is continuing to teach the model more facts. But, Yona notes, “model capacity is finite, and the long tail of knowledge is effectively infinite.”

Once models hit this limit, the hope is they know what they don’t know and simply abstain from answering. However, this is inherently difficult for LLMs.

“This is why most practical attempts to reduce hallucinations through various interventions don’t actually make it to deployment,” Yona explains. “They do reduce hallucinations, but they also hurt utility, because the model ends up refusing to answer questions it actually does know.”

This inability to distinguish between knowns and unknowns creates what the paper’s authors call the “utility tax.” Enforcing a zero-hallucination standard requires the model to abstain whenever it is even slightly uncertain, discarding massive volumes of completely valid information. For example, the authors demonstrate that reducing an underlying 25% error rate down to a strict 5% target forces developers to discard 52% of the model’s correct answers.

Treating all errors as hallucinations forces enterprise systems to choose between trustworthiness and helpfulness. Application developers are generally unwilling to pay this massive utility tax and render their models unhelpful.

Consequently, they optimize systems to prioritize coverage, forcing models to operate in a state where they continue to generate confident hallucinations.

Reframing hallucinations as confident errors

To move past the utility tax, the researchers propose to stop treating any factual error as a hallucination. Instead, they reframe hallucinations as “confident errors”: incorrect information delivered authoritatively without appropriate qualification.

This subtle reframing dissolves the strict “answer-or-abstain” dichotomy and allows the model to express its uncertainty.

In this new framework, if a model makes a factual mistake but appropriately hedges its response (e.g., by stating, “I am not completely sure, but I think…”), it isn’t a hallucination. It is simply a hypothesis offered to the user for consideration. By expressing uncertainty, the AI preserves its utility—sharing whatever partial or likely knowledge it has—without violating the user’s trust.

However, if an AI assistant hedges all its responses with a disclaimer, the user is forced to double-check everything, defeating the purpose of the tool entirely.

The solution the researchers propose is “faithful uncertainty.” This approach requires aligning a model’s linguistic uncertainty, or the words it uses to express doubt, with its intrinsic uncertainty, which is its actual, internal statistical confidence in that specific answer. This ensures the model only hedges when its internal state genuinely reflects conflicting or low-probability information.

Faithful uncertainty forms a core component of “metacognition,” the AI’s ability to be aware of its own uncertainty and act on it. To understand this practically, consider the intuitive example of consulting a doctor. We do not trust doctors because they are all-knowing. We trust them because they reliably distinguish between a confident diagnosis (“You have a fracture”) and an educated hypothesis (“It might be a sprain, but let’s run some tests”).

Practical implications for enterprise AI

Under the new framing, errors where a model is genuinely confident but factually incorrect are categorized as “honest mistakes.” This casts knowledge expansion (training the model on more data) and faithful uncertainty as completely complementary efforts. Knowledge expansion pushes the absolute knowledge boundary outward to minimize honest mistakes, while faithful uncertainty honestly communicates wherever that boundary currently lies.

This new framing has important implications for agentic applications. The shift to agentic AI might make it seem like knowing what the model doesn’t know is redundant, since models can just search external databases. However, access to external tools actually amplifies the need for faithful uncertainty. In agentic systems, metacognition becomes the central control layer that governs the entire system.

External tools solve the storage problem because the model no longer needs to encode every fact into its parameters. However, this introduces a new control problem: managing when to retrieve information, verify facts, and orchestrate these external tools. Without faithful uncertainty, an agent is essentially flying blind and must rely on external, static heuristics or over-engineered scaffolds.

“The model might search for something it already knows confidently—wasting latency and cost for no gain. Or the opposite: it confidently answers from memory when it should have searched, producing a plausible but wrong output,” Yona said. Today’s agent harnesses try to solve this externally with query classifiers or always-search rules, but Yona notes that these are “static and brittle.” By using its intrinsic uncertainty to regulate its own behavior, the agent dynamically optimizes its tool use, choosing to invoke a search tool only when its internal confidence is genuinely low.

Beyond deciding when to search, faithful uncertainty is critical for evaluating the results of a search. If a tool returns low-quality or unexpected information, a metacognitive agent does not blindly accept whatever appears in its context window. Instead, it uses its uncertainty awareness to weigh the retrieved external signals against its own internal priors. This prevents sycophantic behavior where the system might otherwise trust external sources that conflict with its actual known knowledge.

The bootstrapping paradox: The catch to teaching uncertainty

For enterprise builders, achieving this faithful uncertainty is trickier than it sounds. It requires teaching models the syntax of uncertainty through supervised fine-tuning (SFT). Because pre-trained models are mostly fed authoritative text, they must be explicitly taught to say things like, “I’m not entirely sure, but I think VentureBeat was founded in…”

But SFT introduces a “bootstrapping paradox.” Unlike standard training datasets where the “right answer” is the same regardless of the model, the ground truth for uncertainty is the model’s own dynamic knowledge base.

“Here’s the catch: the ‘correct’ expression of uncertainty is inherently dynamic, because it depends on what this particular model knows or doesn’t know at this particular point in training,” Yona said. “If you train on a label that says ‘I don’t know X’ but the model actually does know X, you’ve taught it to hallucinate uncertainty… The training data is static, but the target is a moving one, and that’s the fundamental tension teams need to grapple with.”

The road to self-aware AI

For enterprises looking to implement these capabilities without expensive retraining, prompting serves as the most accessible entry point. “Prompt engineering is already something most engineers do today, this provides the lowest-friction path to improving metacognitive behavior today,” Yona said. Enterprise developers can explore frameworks like MetaFaith, an open-source project previously co-authored by Yona, to begin applying metacognitive prompting to off-the-shelf models.

However, Yona cautions that “there is still substantial headroom that prompting alone doesn’t solve,” meaning the industry will eventually need to rely on advanced reinforcement learning (RL) to bake metacognition deeply into model training.

Ultimately, as enterprises transition from isolated chat applications to complex, multi-agent workflows, self-awareness will become a defining prerequisite for reliable autonomy. But evaluating whether a model truly possesses this awareness remains a profound technical challenge.

“How do you actually evaluate whether a model can sense its internal states?” Yona asks. “Even in humans, it’s hard to define or separate ‘true’ self-monitoring abilities from a capable reliance on proxies. We face exactly the same challenges with LLMs: a model might learn to mimic the style of uncertainty without truly sensing its internal state. Developing evaluation frameworks that can tell the difference is one of the most important open problems in this space.”

The brokerage says it saw “record-breaking traffic.”

“While several organizations successfully blocked the activity or remediated the vulnerabilities, others experienced compromise, resulting in stolen data being published on the ShinyHunters DLS,” Mandiant said. (DLS is short for data leak site.)

An analysis of a bash script left in the staging environment shows the attackers performed reconnaissance on compromised organizations, including mapping the PeopleSoft configurations, viewing process scheduler, and WebLogic server XML configurations. Eventually, the threat actors established an outbound SSH connection to 176.120.22.24, the IP address hosting ShinyHunters’ DLS. The stolen data was first compressed using the zstd tool. The DLS claimed to have recovered 48GB of data from a single victim.

A partially redacted section of the ShinyHunters’ DLS.

Credit:
Mandiant

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ShinyHunters has been active since at least 2019. Over the past several years, it has executed scores of hacks against some of the world’s largest companies, affecting millions of people downstream. A small sample of victims includes Ticketmaster (through the breach of Snowflake, which hosted the data), Spain’s biggest bank, Santander, and Salesforce (and, through it, Google and, reportedly, many other companies). ShinyHunters uses various techniques to gain initial access, including exploiting cloud misconfigurations and software vulnerabilities, stealing OAuth tokens, supply chain attacks, voice phishing, and other forms of social engineering.

Mandiant and Rapid7 are providing detailed indicators of compromise. They are also advising PeopleSoft customers on the steps they should take immediately. Given ShinyHunters’ success rate, all PeopleSoft users would do well to heed the calls.

Eighty residents of towns near SpaceX’s Starbase facility in South Texas have filed a class-action lawsuit alleging that the company’s constant rocket launches are physically destroying their homes. The lawsuit accuses SpaceX of negligence, gross negligence, and trespass based on the Commercial Space Launch Act of 1984.

One plaintiff showed Reuters her home in Port Isabel, less than six miles from Starbase. Cabinets no longer sit evenly. Doors will not close. Flooring warped after a waterline burst during a launch. She estimates $100,000 in foundation repairs, more than the home is currently worth. “They’re wanting to get to Mars,” she said. “But what about us that are here?”

The lawsuit alleges damage from 11 Starship test flights conducted between April 2023 and October 2025. Sonic booms, vibrations, and overpressure waves cracked walls, shattered windows, damaged roofs, and broke foundations across dozens of households in Port Isabel, Laguna Vista, and South Padre Island.

The economic pressure extends beyond structural damage. The influx of SpaceX money has doubled housing costs in Cameron County. Average home prices rose from $131,000 in 2014 to over $281,000 in 2026, according to Moneywise. For the poor and working-class communities that were there first, the combination of physical damage and inflated costs is squeezing them out.

SpaceX built Starbase as a company town for its 22,000 employees, complete with subsidised housing, a corporate medical clinic, and an employee-only gastropub. But the benefits stay inside the perimeter. Outside it, locals have lost access to Boca Chica Beach, a free public beach that was, as one resident told ABC, “a poor man’s beach” where families gathered without paying anything. SpaceX’s operations have made it largely inaccessible.

The timing is notable. The lawsuit was filed weeks before SpaceX’s record $75 billion IPO, which debuted Friday at a $2 trillion valuation. The company’s S-1 filing valued its total addressable market at $28.5 trillion. The 80 plaintiffs are asking for compensation for homes that are, in some cases, worth less than it costs to fix them.

The Commercial Space Launch Act gives the Secretary of Transportation power to terminate or suspend launches if they are “detrimental to the public health and safety.” No such action has been taken. SpaceX’s IPO prospectus disclosed regulatory risks but did not specifically address the class-action lawsuit or the structural damage claims.

The case echoes the growing resistance to tech infrastructure across the United States. Whether it is data centres straining power grids or rockets cracking foundations, the pattern is the same: the communities absorbing the physical costs of the tech industry’s ambitions are organising, and they are no longer willing to absorb them quietly.

It takes significant upskilling to stay abreast of changes along the Industry 4.0 career path, but courses don’t have to cost an arm, a leg and all of your time.

Regardless of the industry you find yourself in, the majority of professionals will at some stage in their career find themselves in need of new skills. Online courses are a key way to upskill in a cost-effective, timely manner that suits the individual.

When it comes to Industry 4.0 specifically, there are a wide range of career routes to choose from – be it a role in AI, machine learning, supply chains, manufacturing or sustainability, just to name a few. With that level of choice comes a plethora of opportunities to learn new skills, broaden your career horizons and even make a real-world impact in a space that has near endless potential. 

So, for professionals working in Industry 4.0 careers, what courses are worth taking a look at?

Robotics and automation

Robotics and automation are a core pillar of Industry 4.0, as more and more of the processes and technologies within the workplace are powered by advanced tech and AI. Skills in robotics and automation are absolutely essential for jobseekers and established professionals aiming for career longevity.

With that in mind, edtech Udemy has multiple free and fee paying options on the subject of robotics. Courses include a free Mechanisms and Motion Robotics Focus tutorial, which takes roughly five hours to complete, has downloadable materials and can be accessed when needed. The course is designed for students interested in 3D CAD design, mechanical engineering and robotics.

For beginners, there is also a free two-hour Robotics Introductory Course that provides a comprehensive foundation for beginners, hobbyists and professionals alike. The course offers opportunities for hands-on learning that covers everything you need to know ahead of building and programming robots. This will include the fundamentals of robotics, basic electronics, programming languages and the robot operating system. 

Coursera also has interesting options open to professionals, including a Generative AI Automation Specialisation course from Vanderbilt University. The programme offers a free trial, takes four weeks to complete and comes with a certificate of completion. There is also a Fundamentals of Robotics & Industrial Automation course available, that will take around a week to complete, consists of three modules and aims to provide a comprehensive understanding of essential concepts and practical skills.

Smart manufacturing

Similar to robotics and automation, smart manufacturing is a defining feature of Industry 4.0 and skills in this area are a must.

For those at an intermediate level, Coursera has a Digital Manufacturing: Introduction and Smart Design course. The programme takes eight hours to complete, can be engaged with flexibly and consists of three modules. It also comes with a sharable certificate upon completion of the course. Created for mechanical, manufacturing and mechatronics engineers, the course is intended for those who want to comprehend and navigate the transformative concepts and technologies that shape modern industry. Participants will learn about the fundamental principles of digital manufacturing, the path to Industry 4.0 and emerging technology.

Springboard+ and Chevron College are offering a level 9 post-graduate microcredential course on smart factories. The part-time programme is funded by the Micro-credential Learner Fee Subsidy 2026 programme and fees will be subsidised at 80pc for all eligible learner categories – criteria for eligibility are available on the website. The programme will introduce learners to modern and emerging developments in manufacturing engineering, with a focus on the digital technologies powering smart factories and connected production systems. Core topics include digital twins, simulations, smart sensors, artificial intelligence, IoT, robotics, machine learning and virtual reality, covering their applications and deployment in manufacturing. 

Additive manufacturing

3D printing or additive manufacturing skills are highly relevant for Industry 4.0 careers and there are plenty of upskilling opportunities available to professionals and jobseekers. Learning platform Alison is hosting a future of additive manufacturing technologies course – published by Indian platform The National Programme on Technology Enhanced Learning – that is free, takes three to four hours and is CPD accredited. Intermediate level students will learn how to explain the objectives of machine intelligence, break down the features of blockchain technology, define additive manufacturing, outline the categories, describe the process chain, analyse the design for additive manufacturing and discuss the supply chain.

Coursera have similar paid and free trial programmes, such as Additive Manufacturing offered by University of Arizona, Essentials of Additive Manufacturing offered by the Indian Institute of Technology Guwahati, and Aerospace 3D Printing Additive Manufacturing, among others. 

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The new fab marks a win for the EU in its attempts to improve the bloc’s chipmaking capacity.

Global semiconductor company Infineon Technologies is set to open a new €5bn chip factory in Germany, extending its existing campus in Dresden.

The factory represents Infineon’s single largest investment, according to Bloomberg, and was built with support from European Union subsidies. The Smart Power Fab will be opening doors on 2 July.

EU support for Infineon is part of a strong effort aimed at honing the bloc’s chipmaking capabilities and improving resilience to foreign pressures on the industry. Last year, the European Commission approved a direct grant of up to €920m for Infineon to support its investment in Dresden.

The fab also received support under a different programme aimed at supporting common European interests on microelectronics and communication technologies. The company said that the total funding for the newly expanded Dresden site amounts to around €1bn.

The new “first-of-a-kind” European plant will produce two types of semiconductor technologies to be used in industrial, automotive and consumer applications, according to the Commission.

Infineon claimed that the development will create up to 1,000 new jobs, aside from jobs created in the investment ecosystem.

“The AI data centres currently being built and planned around the world will consume twice as much electricity in 2030 as they do today,” Alesander Gorski, Infineon’s chief operating officer, told media at a walk-through of the site. “That’s as much as the entire Federal Republic of Germany.”

Chip production at the fab will be scaled over a period of time depending on demand, Gorski said, adding that it could potentially add as much as €5bn in revenue every year.

The Smart Power Fab is a welcome success for the EU, coming less than a year after Intel scrapped plans for a chip factory in Germany and an assembling and testing facility in Poland.

Earlier this year, Infineon opened a new Cork-based R&D centre focused on its newest innovations in the automotive and consumer microelectronics space, in areas such as battery management, motor control and touchscreens.

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