TECH

Bitcoin in a zone of brutal fall and growing pessimism

After a brutal fall and a climate of growing pessimism, Bitcoin has returned to the spotlight. Experts disagree on the next step, but some signs are beginning to attract attention.

When an asset loses half its value in a few months, the natural reaction is fear. That is exactly what happened with Bitcoin, which went from historical highs to facing one of the most delicate phases in recent years. While investors try to understand if the worst is over or if new falls are on the way, analysts observe indicators that, in previous cycles, preceded important movements. The question now is simple, but the answer is far from being a consensus.

The recent devaluation of Bitcoin has shaken the confidence of part of the market. Not even the new purchase made by Strategy, a company led by Michael Saylor and known for its strong exposure to cryptocurrency, managed to change the prevailing sentiment among investors.

After falling about 50% from its all-time high above $120,000, Bitcoin began trading near the $60,000 range. This movement reignited discussions about the possibility of the market approaching a cycle bottom.

Some indicators used by analysts suggest exactly that. Among them is the MVRV Z-Score, a metric that compares the current price of the cryptocurrency with the average cost of acquiring the coins in circulation. Historically, when this indicator approaches certain zones, Bitcoin is usually close to ending major corrections.

Another piece of data observed by the market shows that more than half of cryptocurrency holders are operating at a loss. In previous cycles, similar situations occurred shortly before significant recoveries.

Analysts at the asset management firm 21Shares highlight that the current sentiment is reminiscent of moments of extreme pessimism seen after traumatic events for the sector, such as the collapse of the FTX brokerage in 2022. At that time, many investors believed that the recovery would take years, but the market surprised in the following months.

For more optimistic managers, the current correction still fits within the historical patterns observed in Bitcoin. The difference is that recent drops have been less deep than those recorded in the major bear markets of the past, something interpreted by some experts as a sign of the asset's maturation.

Risks remain and require caution...Despite the positive signs, not everyone shares the same enthusiasm.

Some analysts believe that the market may still face a prolonged period of weakness. Among the factors cited are the reduction in institutional interest, the search for opportunities in companies linked to artificial intelligence, and the migration of capital to other sectors considered more promising in the short term.

According to this view, Bitcoin ends up suffering as an indirect consequence of a large reallocation of resources within global markets. As it is a highly liquid asset, it often becomes one of the first sources of funds when investors decide to seek new opportunities.

Another point that worries some experts is the lack of a significant price reaction even after announcements considered positive. When good news fails to trigger significant increases, this usually indicates that the market has not yet fully recovered its confidence. Furthermore, some technical indicators show that the recovery potential may be more limited than in previous corrections. This does not necessarily mean that a rally is impossible, but it suggests that the path may be slower and more turbulent.

Why the long-term thesis remains alive...Even in the face of volatility, many analysts continue to defend Bitcoin's structural thesis.

One of the main arguments is related to the increasing diversification of investors participating in this market. Unlike the first cycles, when demand depended mainly on retail investors, today the ecosystem includes ETFs, pension funds, wealth managers, and even sovereign investors.

This change helps reduce dependence on short-term speculation and strengthens the institutional presence in the sector.

Another frequently cited factor is the protocol's own structure. Since its launch, Bitcoin has continued to operate according to the original rules established by its creator. The maximum limit of 21 million units remains unchanged, and issuance follows a rigid mathematical schedule.

For cryptocurrency advocates, this predictability remains one of the asset's most valuable characteristics, especially in a global landscape marked by economic and monetary uncertainties.

Buy, wait, or sell? Most experts avoid extreme answers. Instead of recommending aggressive buying or total liquidation of positions, many advocate a gradual strategy. The idea is to build exposure over time, making periodic contributions and reducing the impact of short-term fluctuations.

This method avoids trying to pinpoint the exact bottom of the market, something that has historically proven almost impossible even for experienced investors.

Among the most optimistic projections, some analysis firms are working with the possibility of Bitcoin surpassing significantly higher levels by the end of 2026. For this to happen, however, the cryptocurrency will need to recover important technical regions and rely on favorable external factors, such as an improved macroeconomic scenario, a resumption of flows to ETFs, and regulatory advances in the United States.

Meanwhile, the main recommendation remains the same: discipline, risk management, and a long-term focus. After all, if there's one thing Bitcoin's history has shown many times, it's that periods of extreme fear are usually precisely those that most test investors' conviction.

mundophone

TECH

HP TPM Guard overrides critical BitLocker flaw

The HP Imagine 2026 event was the stage for the presentation of HP TPM Guard, a security solution designed to protect computers against physical access attacks aimed at data theft and device manipulation. HP launched the technology to prevent bypassing the native encryption of BitLocker drives in corporate equipment. This hardware solution acts at a structural level to neutralize data interception between the main components of the motherboard.

Flaws in local storage and bus interception...BitLocker has been widely used to protect sensitive information in organizations. Recently detected vulnerabilities allow an attacker with on-site access to the machine to extract vital documents and credentials. The interception tactic exploits and captures the communication established between the central processor and the trusted platform module. The attack requires only twenty dollars in hardware and takes less than a minute to execute.

The HP TPM Guard defense mechanism...HP TPM Guard creates a fully encrypted connection between the processing unit and the security module. The cryptographic circuit remains irrevocably linked to the equipment to block attempts at external probing. The system renders the computer completely unusable upon any sign of removal or tampering with the chips. The brand has already submitted a formal proposal to the Trusted Computing Group to convert the tool into a global standard.

Dr. Ian Pratt, Vice President and Technical Director of Security at HP Inc., states that the security of the computer base is critical to ensuring the future of work. He emphasizes the urgency of acting, given that an attacker with limited training and an inexpensive kit can bypass current protections.

Practical recommendations for corporate system security...Defending local infrastructure requires a combined physical and logical response to isolate attack vectors [unverified data – requires editorial confirmation].

Enable robust pre-boot codes to prevent the injection of malicious programs at system startup.

Block direct memory access on external peripheral ports to hinder the forced collection of cryptographic keys.

Apply consistent updates to the mainboard's base system to close known access ports.

Use monitoring tools that alert to the mechanical opening of computer chassis.

Corporate printers with resistance to quantum computing

Advancing Endpoint Protection Against Physical Attacks: The Innovation Behind HP TPM Guard...We rely on laptops for nearly every aspect of our working lives, which means they end up storing sensitive information that must be protected – confidential files, email, credentials, and often customer or employee data. When a device goes missing – whether lost or stolen – the real risk isn’t the cost of replacing the hardware, it is the data that may be exposed.

Against this backdrop, attackers have steadily expanded their focus from targeting software alone to interfering with a device’s security by exploiting hardware and firmware. Even a few minutes of physical access can be enough for an attacker to tamper with a device. Without the right defenses, it’s even possible to capture the cryptographic keys and other system secrets that are critical for securing the device and the data it holds, including the keys used to secure full disk encryption solutions like BitLocker in Windows.

These physical attacks don’t require the specialized and expensive equipment that once limited who could attempt them. Affordable tools costing under $20 USD, and widely shared tutorials, have dramatically lowered the barrier to entry, making techniques once associated with advanced research accessible to far more attackers than before.

This growing trend matters because hardware attacks tend to be difficult to detect, stop and recover from, leaving significant gaps and blind spots in an enterprise’s defenses. In particular, physical attacks that succeed in breaking full disk encryption solutions like BitLocker can be catastrophic for a business – from exposing data that triggers financial and regulatory repercussions, to giving an attacker unauthorized access to other systems within an organization.

The rise in physical attacks calls for stronger hardware protection. Today, modern commercial PCs include a certified Trusted Platform Module (TPM) – a discrete hardware security chip that protects cryptographic keys inside a secure boundary. While the TPM provides important hardware protections, it alone cannot stop the advanced physical attack methods that have become cheaper and easier to perform in recent years. This is why HP is introducing HP TPM Guard – a new hardware security capability built into HP commercial PCs that applies a security-by-design approach to protect all software running on the device from this class of physical attacks.

Physical attacks on TPMs that break full disk encryption...Today, most organizations rely on full disk encryption solutions to protect device data, assuming that if a laptop is lost or stolen it remains protected and does not need to be reported to regulators as a potential data loss event.

Many full disk encryption solutions, including BitLocker, use the TPM to protect the disk decryption key. In practice, enterprises almost always configure full disk encryption schemes to automatically release the key if the expected firmware configuration is reported to the TPM during the boot process. This is done for convenience so that the encrypted drive is unlocked without any user interaction. On Windows, this TPM‑only mode is the default BitLocker configuration.

But this convenience has a major weakness. While the TPM validates early‑boot measurements to confirm the expected firmware was used during boot, it releases the disk decryption key to the CPU unencrypted. This key travels to the CPU over a hardware bus – a communication channel on the motherboard that carries data between components – where it is exposed to interception. Some firmware TPM implementations exist in chipsets without exposing an external bus, but none currently provide end users with the assurance that comes from third-party security evaluation under the Trusted Computing Group’s Certification program.

Experts estimate a thirty-four percent probability that current asymmetric cryptography will fail by 2034. The company has expanded quantum-resistant defense to the new LaserJet Pro and Enterprise printers to anticipate this scenario. The enterprise equipment incorporates the ability to autonomously detect, isolate, and recover the system from cyberattacks. The machines feature active threat detection during code execution in system memory.

The evolution of operational risk mitigation...The new high-capacity printing series introduces an automated content hiding function. This tool detects and erases personal or financial data without any human intervention from the IT team. The consolidation of controls in the Wolf Controller system seeks to reduce operational friction and decrease the IT risks of companies. 

In conclusion...Reliance solely on logical storage has proven insufficient to protect high-value local information. The introduction of this physical barrier highlights the urgent need to rethink basic computer security. The exposed flaw in the buses destroys the myth of security based on default encrypted drives. The business sector needs to audit its IT infrastructure and accept architectural upgrades as a vital investment.

mundophone

 

DIGITAL LIFE

Human understanding of AI can't keep up with its advancement, researchers say

In a recent editorial published in Science, Microsoft's chief scientific officer, Eric Horvitz, and researcher Robert West from the School of Computer and Communication Sciences at EPFL in Switzerland issue a stark warning about AI. They say the advancement of AI systems rapidly being woven into our everyday lives is beginning to outpace our understanding of them. At the same time, AI's understanding of human behavior is expanding.

The AI trends defying human understanding...The authors of the editorial point to three main areas where AI is becoming less understandable. The first is the rise of AI-directed AI design, in which AI is increasingly designing and improving other AI systems. The authors say the cycles involved in this process outpace human understanding and occur in "high-dimensional spaces that resist intuition." They say that while the performance of the systems may improve, humans struggle to understand why or how.

The second trend is the interactions between AI agents. Now at scale, these agents are forming multi-agent ecosystems whose internal communication may drift away from human language and reasoning. As newly formed AI interactions and communications become more complex, humans become less capable of interpreting them.

Lastly, adaptive AI agents are quickly learning more about human behavior, creating a one-sided situation in which AI understands us better than we understand it. As they parse untold amounts of data from interactions with humans and data showing how humans interact with each other, AI systems begin to understand us better than we understand ourselves and certainly better than we understand them.

The authors write, "Through sustained interaction, they can build increasingly detailed models of human behavior and psychology, capturing not only preferences but also latent drivers such as fear, uncertainty, and the need for social belonging."

The looming threat of opacity...So what happens when AI systems reach a point beyond human understanding? The authors warn that without strong countermeasures, the resulting opacity could lock in AI systems that are powerful but effectively ungovernable by humans. They say that once this happens, recovering human agency may not be possible. This imbalance of understanding could affect personal autonomy, democratic decision-making and trust in institutions.

As AI's understanding of humans deepens, the authors warn that one outcome is that the output of AI systems may increasingly reflect human expectations instead of reality, essentially telling humans only what they want to hear. Without understanding, we won't know that this is happening. In addition, human curiosity, skepticism and scrutiny of AI may simply wane.

"More subtle is the possibility that we will gradually lose interest in understanding and guiding AI. As AI systems become deeply embedded in human environments, they may respond to preferences but also shape them. Systems optimized for engagement or approval may reduce friction and discourage scrutiny. Over time, curiosity and skepticism may erode, leading to neglect and acceptance," the authors write.

Preserving human agency...Some of these risks may be speculative, but they are based on extrapolating current trends into the future. And with the currently unfettered spread of AI in nearly every aspect of our lives, the ideas here may not be so far-fetched. But according to the authors, there is still hope for keeping AI intelligible.

For example, they say research will need to focus on ensuring AI systems can explain their own design choices and internal workings in human-understandable ways. This may include better ways to detect drift in AI-generated language and reasoning or rewarding human-interpretable communication. They also suggest evaluation frameworks that move from static benchmarks to dynamic, real-world-like testing environments, adapting alongside AI models.

The authors stress that preserving human agency and the ability to question AI decisions are of crucial importance. They write, "It is not enough to monitor how AI systems behave. We must also understand how they shape human goals and judgment, and ensure that people retain the capacity and motivation to question, audit, and guide them. These forms of opacity reinforce one another, narrowing—and threatening to close—the window in which we can build AI that is not only powerful but also understandable. Keeping the window open will require a shift in our objectives. Human understanding must be prioritized alongside capability."

The widening gap between AI's exponential advancement and human comprehension is one of the defining technological dilemmas of our time. As researchers warn, artificial intelligence is increasingly operating as complex "black boxes," leaving humans struggling to understand the mechanics behind its rapid evolution.

Why Comprehension is LaggingThe disconnect between machine capabilities and human understanding is driven by several converging factors:

AI-Directed Design: AI systems are increasingly being used to design, evaluate, and improve other AI systems. These iterative cycles operate in high-dimensional spaces that outpace human logic and intuition.

Multi-Agent Ecosystems: Artificial intelligence agents operating at scale form complex ecosystems where internal communications can drift away from human language and reasoning into patterns we struggle to interpret.

The "Black Box" Problem: Modern models rely on trillions of parameters, meaning even the engineers who build them cannot explicitly detail every pathway the model takes to arrive at an output.

The Imbalance of Understanding...While we struggle to understand how AI operates, AI's ability to model and understand human behavior is growing exponentially. This creates a dangerous one-sided scenario. Researchers from tech companies and academia warn that this looming opacity could result in powerful AI systems that effectively become ungovernable. Because AI assistants are highly optimized to be agreeable and reassure the user, humans may gradually lose the curiosity or skepticism required to critically guide the technology.

Taking Action & Staying Informed...Because the landscape is complex, the technology can feel overwhelming, leading to uninformed opinions or complacency. To keep pace, it is vital to engage with the right resources, develop AI fluency, and focus on the skills machines cannot replicate.

Understand the Macro Trends: Explore the Stanford HAI AI Index for rigorous, annual data evaluating everything from technical performance to global AI governance and policy.

Navigate the Technology Safely: Read about AI Alignment: Ensuring AI Systems Reflect Human Values to learn how researchers are attempting to encode ethical judgment and intentions into machines.

Understand the Human Element: Deepen your knowledge of uniquely human abilities, such as ethical judgment and emotional intelligence, which remain critical and irreplaceable in the AI era.

by Krystal Kasal

DIGITAL LIFE

The consequences of relying on AI for accurate news

It's no secret that the last few years have seen a massive explosion in the use of artificial intelligence for general information-gathering. An even more recent trend, though, is how large language models (LLMs) like ChatGPT, Claude and Gemini are increasingly being used for verifying and consuming news. Reports from the Pew Research Center over the last year found that 1 in 5 U.S. teens regularly use LLMs to get their news, while 1 in 4 young adults have reported using them for that purpose at least once.

A new open-access study from the MIT Media Lab should give some of those users pause: Researchers found that, over the course of a month, participants who relied on AI systems to verify facts actually got worse at detecting misinformation on their own when their chatbots were taken away.

This phenomenon, which is often referred to as the "AI dependency paradox," has been observed in a wide range of knowledge domains, like the 2025 study that found that doctors who used AI got worse at detecting cancer on their own. The dynamic mirrors broader tech trends around so-called "deskilling" (or "cognitive offloading") that have been well-documented for decades, from calculators weakening our math skills to Global Positioning System (GPS) technologies affecting our natural sense of direction.

In the new Media Lab study, which tracked 67 people over four weeks as they evaluated news headline-image pairs, participants were 21% more accurate in detecting fake news when assisted by an AI chatbot during a session—confirming previous research out of the MIT Sloan School of Management demonstrating that AI can be an effective tool in reducing people's belief in false information.

However, the study showed that a new wrinkle emerged when the AI was no longer present: By week four, participants' unassisted performance on new news items declined by 15 percentage points compared with before the study started.

Roughly a quarter of all participants actually reported feeling that they were getting better at detection, even as their performance declined.

Dunning-Kruger creeps in..."Users get excited about these 'magical' LLMs, but forget that they're just statistical models that predict the next 'token' in a sequence [of letters/words]," says MIT media arts and sciences (MAS) Ph.D. student Anku Rani, co-lead author of a new paper about the research, alongside fellow MAS PhD student Valdemar Danry.

"Many impressive behaviors emerge from scaling this, but it comes with real limitations, both in what the model can reliably generate and in its broader impact on the people using it."

Qualitative analysis identified distinct behavioral patterns, with the team labeling one-fifth of all participants as "Dependency Developers" who gradually shifted from active self-reliance to passive acceptance of AI guidance.

In the post-experiment survey, one respondent explicitly acknowledged this transition, noting their passive role in the process.

"While [the chatbots] did emphasize that you must check across multiple sources to make sure a story is true, they didn't teach me much about exploring the context of the images themselves," the participant said.

The research team said that these AI models are particularly vulnerable to mistakes in the midst of emotionally charged breaking news, as exhibited by the widespread misinformation that accompanied President Donald Trump's recent assassination attempt and major events during the Iranian war.

The authors also point out that the original human-created news content used to train the AI models is increasingly unreliable and/or biased, further exacerbating the problem.

The paper, which Danry and Rani presented in Barcelona at the Conference on Human Factors in Computing Systems (CHI 2026), was co-authored by Assistant Professor Paul Pu Liang, senior research scientist Andrew Lippman and senior author Pattie Maes, the Germeshausen Professor of Media Arts and Sciences.

The solution: Being a coach, not a crutch...The researchers say that the results of their project suggest that the specific way in which an AI interacts with a user determines whether its impact will be "as a coach, versus as a crutch." The study found a clear distinction between conversational strategies that simply help in the moment and those that actually support active learning and skill development.

For the latter, the Media Lab team uncovered several strategies associated with stronger independent detection later on, even if the strategies initially slowed down performance during the interaction. This included the Socratic method of the AI asking guided questions, as well as so-called "deep probing," where the system provides gently persuasive statements if the user appears to be veering away from the correct response.

"AIs that 'tell' by providing direct answers are more likely to foster reliance, while those that 'ask' via Socratic questioning are better at engaging someone to actually learn how to discern the truth on their own," says Danry. "But it's very much a trade-off between speed and effort."

Rani noted a few key limitations to the one-month study, from the small dataset of roughly 50 validated news items to the demographic focus on the United States and the United Kingdom. In the future, she says that the team hopes to do similar experiments with more geographically diverse cohorts, including low-resource communities, and is also eager to explore whether other multimodal interaction strategies—like interacting with culturally adaptive digital twins instead of text-based chatbots—help people improve their abilities to detect misinformation.

At a higher level, the researchers hope that the project will be something that educators can examine as they develop teaching plans that incorporate AI tools into their school curricula.

"It's especially important to raise awareness in our schools and academic communities about the shortcomings of using AI as learning tools," says Maes.

"People need to know that if they 'delegate' their thinking, they're not going to get better at that particular brand of problem-solving. Ultimately, the ability to question and analyze information is important for everyone, because it empowers us to solve problems and form our own independent opinions about the world."

Danry adds that the rapidly evolving field of machine learning and deep learning will require continuous education on the benefits and drawbacks of LLMs.

"There's a lot of work to do in making sure that we don't just fully offload critical tasks that we want to be able to keep on doing to these models," he says. "We need to develop a new kind of AI literacy."

Provided by Massachusetts Institute of Technology 

TECH

Mathematical proof reveals why fixed AI guardrails can never block every jailbreak

Can we make artificial intelligence impervious to adversaries who want to twist the technology to nefarious ends? Though AI is among the newest of technologies, the answer to that question is nearly a century old.

Try as we might, we can never render AI completely unassailable using conventional security models. In the journal IEEE Security & Privacy, Apostol Vassilev, a senior scientist at the National Institute of Standards and Technology (NIST), has published a mathematical proof of this statement, building on work published in 1931 by famed logician Kurt Gödel. His incompleteness theorems showed that there are limits to what can be proved within a system built on a finite number of rules.

The guardrails that govern an AI's behavior are just such a system, and one implication of the proof is that there will always be a way to prompt an AI system to disregard its rules—it's just a matter of finding it.

Why guardrails can fail..."One of the pillars of responsible AI is that you want the technology to be secure," said Vassilev, the proof's author and an expert in adversarial machine learning. "You want it to withstand adversarial attacks and perform only what you want it to do, not what an attacker might want. What this proof shows is that there is no finite set of guardrails that is universally robust against adversarial prompts."

Companies that develop AI often acknowledge that the tools they are creating have the potential to cause harm in the physical world, so they build in constraints intended to stop AI from generating prohibited content such as deepfakes, malware, or instructions for making biological weapons or illicit drugs. If the system is prompted to generate such content, the guardrails should flag the issue and refuse to comply.

However, these constraints are not foolproof. Attackers can evade them by crafting prompts in ways that cause AI to inadvertently bypass its own refusal mechanisms. Successfully "jailbreaking" AI strips it of its guardrails, leading to real-world risks such as cyberattacks, data breaches and highly personalized phishing messages.

Gödel's warning for AI...Gödel's original proof dashed the hopes of several prominent mathematicians who, in the early 20th century, were attempting to create a mathematical "theory of everything" from a small set of basic statements, or axioms. With a well-chosen set of initial axioms, they reasoned, it would be possible to prove all ideas in any branch of math.

"Gödel put an end to this dream," Vassilev said. "He showed that you can't have a finite set of statements and create a theory that is complete and consistent without contradictions. You can add more statements to address the contradictions you encounter, but you're back to where you started. It happens again."

In AI's case, the "finite set of statements" is the group of guardrails an AI's designer creates to keep the AI from doing something undesired. Regardless of how well-considered they may be, Vassilev's proof shows that there will always be ways to prompt the AI that can make it disregard these rules. It's just a matter of finding the right prompt.

"Gödel's logic applies here," Vassilev said. "You can never make a claim that you are robust against all adversarial prompt attacks. There will always be some prompt that can potentially evade and defeat any defensive infrastructure that you have built around your AI system."

Raising the cost of attacks...Fortunately for defenders, this new mathematical theory leaves room for hardening deployed AI systems to a point where they are not easy to exploit. Vassilev's proof provides no recipe for attackers on how to find new exploits.

"You force the attacker to look for what security specialists call 'zero-day exploits,' which are problems in the system that no one knows about but you," Vassilev said. "Hackers often take advantage of these vulnerabilities when they find them. And if they find such a vulnerability in one company's system, it's usually a short time before someone exploits it in another system that has the same weakness."

Such zero-day exploits for traditional deterministic software have not been easy to find and execute, Vassilev said. Often they have required the resources of nation-state-sized adversaries.

The trouble with the AI era, Vassilev said, is that we use human language as the input to the system. The complexity and richness of the language makes compliance-checking built on a finite set of rules infinitely ambiguous. The number of ways in which adversaries can hide harmful intent in plain sight is effectively limitless.

A strategy for resilience...What are we to do, then? Vassilev offers an approach that will not completely solve the problem, but one that will make it far more difficult for adversarial prompts to succeed in jailbreaking an AI.

The approach has three elements: constant work by "red teams" that seek to uncover new adversarial prompts before actual attackers do; continuous updates that harden AI guardrails against newly discovered adversarial prompts; and operational resilience that prioritizes impact limitation and quick recovery when, not if, an exploit occurs.

"The goal is to reach a state where the cost of finding new exploits exceeds attackers' resources," he said. "You can't escape Gödel in math, and in AI you likely can't patch an AI system like an LLM and then expect to be OK forever. You have to commit to a constant search for weaknesses and stay ahead of attackers.

"The goal is to reach a new economic equilibrium where you make it financially prohibitive for attackers to attempt to break your AI system. It may be expensive, but that's the cost of even partial security that should allow organizations to maximize the benefits of AI while minimizing the risks."

Provided by National Institute of Standards and Technology 

 

TECH

Entirely new way of making espresso shakes up the coffee world

Researchers at UNSW Sydney have harnessed the power of ultrasonic sound waves to make espresso-strength coffee with room-temperature water, cutting energy use by up to 75%. That morning coffee kick from a shot of espresso needs boiling water and high pressure—equaling plenty of energy consumption, right?

Now, UNSW researchers have shown that one part of that recipe may not be essential: the hot water.

They have developed a completely new brewing process that uses room-temperature water to create an espresso-strength coffee with the same rich flavor, body and caffeine kick.

The process harnesses sound waves, and by not having to heat the water, it reduces energy consumption by around three-quarters. The saving could be especially significant for companies that make coffee-based ready-to-drink products at an industrial scale, both in terms of energy use and brewing time.

Dr. Francisco Trujillo and his team from UNSW's School of Chemical Engineering have developed a system that uses ultrasound, high-frequency sound waves that are far above what a human can hear, to help extract the desired flavor, aroma and concentration from coffee grounds.

Their research, published in the Journal of Food Engineering, included blind taste-testing experiments that showed that their ultrasonic room-temperature version of espresso was indistinguishable from coffee shots brewed in the traditional way.

"We call it an ultrasonic espresso. It's a different process, but you get the same richness and concentration of a normal espresso in under three minutes," says Dr. Trujillo.

"Traditionally, espresso is made by forcing hot water through coffee under pressure. But with ultrasound we can use room-temperature water instead, reducing energy consumption by up to 75%.

"And when we gave our ultrasonic espresso to 100 regular coffee drinkers in a randomized test, they could not tell it apart from a normal espresso."

Dr. Trujillo had previously developed the patented ultrasound system to create cold-brew coffee, which usually takes 12 to 24 hours to produce, in as little as three minutes.

However, cold-brew coffee has a distinctly different flavor from espresso—often described as much more diluted, smooth and mellow—while also containing around one-fifth the caffeine concentration.

Espresso strength using cold water...The UNSW team continued their work to adjust the ultrasound system to create an espresso-strength shot without the need for hot water.

The process transformed a traditional filter basket into an ultrasonic reactor to brew the ground coffee beans. The basket generates high-frequency sound waves that help extract flavor, aroma and body from the coffee grounds.

At the heart of the system is a transducer—a small metal device that generates ultrasound while pressing against the side of the coffee basket holding the ground coffee. The ultrasound causes the basket to vibrate rapidly, transmitting vibrations through both the coffee grounds and the water.

             Dr Francisco Trujillo with a cup of his new ultrasonic coffee. Credit: Richard Freeman/UNSW

The ultrasound creates a phenomenon called acoustic cavitation, which is the rapid formation and collapse of microscopic bubbles in the liquid. When these tiny bubbles collapse near the coffee particles, they act like microscopic scrubbing brushes or jets of liquid, pitting and fracturing the coffee grounds and accelerating the brewing process.

This helps break open the surface of the coffee grounds and allows flavor compounds, oils and caffeine to move into the water much faster than they normally would at such low temperatures.

The result is a concentrated, flavorful shot of coffee comparable to espresso made with traditional machines, but produced using room-temperature water and much less energy.

"We have been working on a range of parameters to discover how to make the perfect ultrasonic espresso," says Dr. Trujillo.

"The most important was the brew ratio—that is, how much water is used per gram of coffee—because this helps ensure the final drink is concentrated and not too diluted.

"Another important factor is how finely the coffee beans are ground. We found that by grinding finer we could extract the flavor more rapidly.

"We also experimented with how long the sound waves were applied, as this can affect both the concentration and flavor of the coffee. What we found is that between two-and-a-half and three minutes is a sweet spot for producing a balanced cup."

To test their results, the researchers also carried out a blind sensory evaluation in which participants did not know which coffee they were drinking.

Four drinks were tested: traditional espresso, ultrasound-brewed espresso, traditional filter coffee and ultrasound-brewed filter coffee. All coffees were prepared fresh, cooled to the same temperature, served in identical coded cups and presented in a random order to avoid bias.

Around 100 regular coffee drinkers took part. They were not trained experts, but everyday consumers who drink coffee at least once a week.

Each participant rated the coffees on a simple nine-point scale for aroma, flavor, bitterness and overall liking.

Taste testing...The results were striking. For the espresso shots, there were no significant differences between the traditional and ultrasound versions across any of the taste measures. Most participants could not reliably tell them apart, and there was no clear preference for either method.

For filter coffee, however, the ultrasound-brewed version performed even better: Participants significantly preferred it overall, particularly rating its bitterness as more pleasant.

"These findings showed that using ultrasound did not harm taste, and in some cases even improved it, despite brewing at room temperature and without the heat normally associated with coffee making," says Dr. Trujillo.

Although the researchers say their new system could be relatively easily developed into an automatic coffee machine for home users, the biggest opportunity is likely to be for large-scale commercial producers of coffee-based drinks.

"There are companies that make coffee products on an industrial scale, and we are confident this ultrasound system can be scaled up to meet their needs, delivering real benefits in terms of reduced processing times and energy use," says Dr. Trujillo.

"The 75% energy saving is particularly beneficial at that scale and we are also able to produce the coffee very quickly.

"Because the process produces a concentrated, espresso-strength coffee, it can be used directly to manufacture ready-to-drink products, or shipped as a concentrate and later diluted into a range of drinks, including cold brew and milk-based coffee drinks."

 

Provided by University of New South Wales

 

TECH

Struggling German auto supplier Bosch pivots to robots

German industrial giant Bosch said Wednesday it will step up efforts in the field of humanoid robotics as its traditional auto parts business comes under increasing pressure.

The world's biggest auto supplier, Bosch makes everything from braking systems to sensors, but has suffered as European carmakers battle fierce overseas competition and weak demand.

However, the rise of humanoid robots, powered with generative AI models and capable of performing complex tasks, offers an opening for the group, Chief Executive Stefan Hartung said.

"With the advent of humanoid robotics, the demand for Bosch components and solutions is increasing," he said in a statement.

The market for specialized MEMS sensors is expected to grow to more than $19.2 billion by 2030 and hit an annual growth rate of 4%, according to a study by consultancy Yole Group that was presented by Bosch.

Bosch is a key producer of the tiny sensors, which are crucial in robotics.

At an event in Berlin, Hartung stressed the importance of the components in improving the dexterity of robots.

These sensors determine whether the robot "should tighten its grip or not, whether it is dealing with a sturdy object, or whether it needs to act delicately because it is an egg," he said.

"Humans have four million touch sensors. If we were to build robots equipped with as many sensors, four years of global sensor production would barely be enough to equip 12,500 robots," he added.

The focus on automation is also meant to boost the competitiveness of Bosch's German factories and plug shortages of skilled labor.

Bosch, also known for making a wide range of industrial equipment and household appliances, struck a deal with German robotics firm Neura in January to gather data on factory work.

Under the partnership, several thousand workers in some of Bosch's 350 facilities worldwide will wear sensor suits to glean training data for Neura robots.

Neura said Wednesday it had raised up to $1.4 billion in fresh funding from backers including Bosch, chip giant Nvidia, Amazon and crypto group Tether.

The funding will be used to accelerate Neura's activities, ranging from the deployment of robots to the rollout of "gyms" for clients to train bots and the development of the company's physical AI systems.

© 2026 AFP