The global rise in infertility rates has catalyzed a dramatic surge in the utilization of assisted reproductive technologies (ART), marking a pivotal juncture in reproductive medicine. As conventional ART procedures remain largely manual, labor-intensive, and fraught with subjective decision-making, the quest for heightened precision and consistency in outcomes has become increasingly urgent. Despite advances in laboratory techniques and clinical protocols, many aspects of ART are hindered by a lack of robust, evidence-based tools capable of non-invasively enhancing processes such as gamete evaluation, protocol optimization, and embryo selection. These challenges underscore the necessity for innovative solutions that can transcend the limitations of human assessment and procedural variability.

Artificial intelligence (AI) and automation emerge as transformative forces poised to revolutionize the landscape of ART by driving standardization, accelerating workflows, and improving predictive accuracy. Integrating computer vision, deep learning algorithms, and microfluidic technologies offers a compelling framework to refine every stage of the reproductive journey—from semen processing and oocyte evaluation to embryo culture and transfer. Early successes in clinical deployment underscore the feasibility of such approaches; for instance, AI-powered embryo grading systems are already assisting embryologists in objective assessment, while microfluidic devices are revolutionizing sperm sorting with unprecedented precision and gentleness. Nonetheless, the frontier of AI-enabled ART is still nascent, with vast potential waiting to be unlocked by systems-level integration.

At the core of this technological evolution lies the application of deep learning, a subset of AI that excels in pattern recognition and data-driven decision-making. By training neural networks on vast datasets of clinical and cellular images, researchers have begun to develop models capable of predicting embryo viability with remarkable accuracy, thereby enhancing implantation success rates and reducing the emotional and financial burdens on patients. These AI models leverage an array of features—from morphological characteristics and dynamic developmental patterns to molecular biomarkers—redefining embryo selection as a data-rich, evidence-based process rather than an art reliant on subjective human judgment.

Microfluidics, another cornerstone of automation in ART, offers the ability to manipulate minute volumes of biological fluids with exquisite control. The integration of microfluidic platforms in semen processing exemplifies how automation can enhance both efficiency and effectiveness. Traditional sperm preparation techniques often expose gametes to physical stresses that compromise their quality, but microfluidic systems facilitate gentle, precise sorting based on motility, morphology, and other functional parameters. This advancement translates directly into improved fertilization outcomes and healthier embryos, thereby addressing one of the key bottlenecks in male fertility assessment and treatment.

Beyond gamete processing and embryo selection, AI is influencing the management of the entire embryology laboratory workflow. Automation frameworks, guided by adaptive algorithms, have the potential to create closed-loop systems where feedback from each stage informs real-time adjustments in protocols. Such platforms could continuously learn from clinical outcomes to optimize hormone stimulation regimens, culture conditions, and embryo transfer timing. The vision is a data-driven reproductive ecosystem where human oversight is augmented—not replaced—by intelligent systems, enabling a more personalized and effective approach to fertility care that adapts dynamically to each patient’s unique biology.

Despite these promising advancements, the integration of AI and automation into ART faces notable challenges. One major hurdle is the scarcity of high-quality, standardized datasets critical for training reliable and generalizable AI models. Variability in laboratory techniques, imaging modalities, and patient populations complicates efforts to construct comprehensive databases, slowing algorithm development and validation. Furthermore, ethical and regulatory considerations loom large. The deployment of AI in reproductive medicine raises complex questions about data privacy, algorithmic transparency, and informed consent, necessitating stringent oversight frameworks that balance innovation with patient safety and autonomy.

Clinical adoption also requires robust validation through large-scale, prospective trials to demonstrate that AI-driven interventions translate into meaningful improvements in live birth rates and patient experience. As many current studies rely on retrospective data or surrogate markers of success, the path to widespread acceptance demands rigorous evidence and consensus among reproductive specialists. Additionally, the integration of automated systems within existing laboratory infrastructures must consider workflow compatibility, cost-effectiveness, and user training requirements to ensure seamless transition and maximize clinical impact.

The future of ART may well be shaped by the emergence of fully integrated AI-enabled laboratories, where a network of automated devices and intelligent software operate in concert to deliver adaptive, personalized reproductive care. Such closed-loop systems could harness continuous data streams from non-invasive monitoring technologies, predictive analytics, and decision support tools to refine every decision point in the embryology pipeline. This paradigm shift would move the field from static, protocol-driven practices to a responsive, learning environment where patient outcomes guide iterative improvements and innovations are rapidly deployed.

This revolution has implications beyond technical enhancements; it also reshapes the ethical landscape of reproductive medicine. The empowerment of AI to influence critical decisions about embryo viability and selection introduces profound questions about agency, consent, and the potential for unintended biases embedded within algorithms. Transparent development processes, interdisciplinary collaboration among clinicians, ethicists, and technologists, and proactive regulatory engagement will be essential to navigate these challenges responsibly while preserving patients’ trust and autonomy.

In summation, the intersection of AI, automation, and ART heralds a new epoch in reproductive medicine, where data-driven insights and precision engineering coalesce to surmount longstanding barriers. Continued investment in research, infrastructure, and ethical frameworks will be critical to unlock the full potential of these technologies, enabling more predictable, efficient, and equitable reproductive care globally. The vision of an AI-integrated, closed-loop in vitro fertilization laboratory exemplifies the tangible future of fertility treatment—one where innovation meets compassionate, personalized medicine to address one of humanity’s most fundamental challenges.

As the global community grapples with escalating infertility, embracing AI and automation represents a beacon of hope, promising not only enhanced clinical outcomes but also democratization of access through scalable, standardized technologies. The path forward invites a collective effort—uniting data scientists, reproductive biologists, clinicians, and policymakers—to realize the transformative impact of intelligent systems that can truly redefine what is possible in assisted reproduction.

This profound shift will ultimately transform the experience of patients, clinicians, and laboratory professionals alike, as the integration of AI and automation reduces variability, mitigates error, and personalizes treatment. By transcending the limitations of subjective assessments and manual procedures, these technologies offer the promise of a more reliable and confident path to parenthood for millions worldwide.

While the journey to fully automated, AI-driven labs continues to unfold, current advancements signal meaningful progress that is already reshaping clinical practice. Continued interdisciplinary collaboration, technological refinement, and comprehensive validation are poised to accelerate innovation and broaden access to cutting-edge fertility care. As the field moves swiftly toward these new horizons, AI and automation stand as pivotal tools in our collective endeavor to overcome infertility’s challenges through science and technology.

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Subject of Research: The application and integration of artificial intelligence (AI) and automation technologies in assisted reproductive technologies (ART), with a focus on improving precision, standardization, and outcomes in embryology laboratories.

Article Title: AI and automation in assisted reproduction

Article References:
Lorimer, J., McLachlan, R., Zander-Fox, D. et al. AI and automation in assisted reproduction. Nat Rev Bioeng (2026). https://doi.org/10.1038/s44222-026-00454-2

Image Credits: AI Generated

Now that attention within the AI revolution has one again firmly turned toward the cost-benefit equation (i..e., ROI) of tokens (see “From Singularity To Tokenomics: The AI Narrative Just Hit A Serious Snag“) in particular, and the trillions behind the AI spending rollout in general, and we say once again because every few months we get some iteration of the following report from Goldman published almost two years ago today…

… we have more bad news: according to a global survey by Bain, cost savings from automation are broadly falling short of projections. Which means that those expecting big savings from their investments in artificial intelligence, which is most companies, will be disappointed.

The missed targets “should be making executives uncomfortable,” since many of them are approving increased spending for artificial intelligence on the basis of expected savings, the consulting firm said in a report shared exclusively with Bloomberg News. The problem is there are little actual savings to speak of. 

The survey, completed in April, was based on responses from executives at 951 companies with more than $100 million in revenue, across nine sectors: retail, technology, advanced manufacturing, healthcare, consumer products, energy, financial services, telecom/media/entertainment and insurance.

It found that among companies measuring their AI cost savings, the largest share (40%) realized reductions of 10% or less. Predictably, most had been expecting to see far more meaningful improvement, especially since they spent far more than that on the new technology.

Here’s the part that Bain found the most troubling: 44% of large companies that are funding their next wave of AI spending are basing those investments on the last round of savings – savings that haven’t yet materialized. 

“The prior wave underdelivered. The savings pool is smaller than assumed,” Bain warned. “And the investment case for the current wave was sized against projections rather than actuals.” Kinda like the bubble in AI forward earnings: based on projections – which as any intern can tell you can flip on a dime – rather than actuals.

“Self-funding the next wave from past returns sounds like discipline. In reality, it is a circular bet with a structural leak,” the firm cautioned, and concluded that “The technology worked. The value didn’t arrive.”

Whether driven by hope or FOMO or a blend of both, the AI boom is exposing divides between promise and reality. An MIT research report last year showed that 95% of corporate AI pilots fall flat and concluded that the “primary factor keeping organizations on the wrong side of the GenAI Divide is the learning gap, tools that don’t learn, integrate poorly, or match workflows.”

So Bain’s latest survey wasn’t the first evidence of AI underdelivering so far on expectations. And it’s not likely the last either.

But the Bain report isolated a different problem: “Despite a decade of investments in data modernization running well into hundreds of billions of dollars globally, the No. 1 reason AI programs underperform is that companies cannot reliably get access to their own data,” Bain said.

“Companies that don’t validate their reinvestment math against what automation actually returned, rather than what it was supposed to return, are compounding risk rather than managing it” the Bain report concluded, confirming what many have already sensed: virtually nobody has done effective ROI analysis amid a technological rollout that has already soaked up more than $1 trillion in capital, the return on which appears to be modest at best. 

Bain’s prescription: Instead of waiting to structure all of their data to make it ingestible by AI, companies should start with what’s available to feed into the models, and then use AI to help sort out how to structure the rest.

Meanwhile, companies that were meeting their savings targets reported running into barriers with data structure and accessibility at even higher rates than those missing their targets, but they were less likely to report organizational challenges such as insufficient budgets or competing priorities.

Adding fuel to the fire, a comparable report from Gartner found that over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls. 

“Most agentic AI projects right now are early stage experiments or proof of concepts that are mostly driven by hype and are often misapplied,” said Anushree Verma, Senior Director Analyst, Gartner. “This can blind organizations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production. They need to cut through the hype to make careful, strategic decisions about where and how they apply this emerging technology.”

As such, Gartner recommends agentic AI only be pursued where it delivers clear value or ROI, noting that “Integrating agents into legacy systems can be technically complex, often disrupting workflows and requiring costly modifications. In many cases, rethinking workflows with agentic AI from the ground up is the ideal path to successful implementation.“

“To get real value from agentic AI, organizations must focus on enterprise productivity, rather than just individual task augmentation,” said Verma. “They can start by using AI agents when decisions are needed, automation for routine workflows and assistants for simple retrieval. It’s about driving business value through cost, quality, speed and scale.” 

The problem, it now appears, is that virtually nobody has done an actual ROI analysis. But with token costs now soaring…

… the time has finally arrived, and as enterprises pull back in horror from the “great promise” of the agentic black hole, one can easily understand why both OpenAI and Anthropic, both of which are extrapolating their burst in agentic revenue in perpetuity, are rushing to go public before the market once again does the ROI math.

Read full story here…

If Alphabet's record-breaking $85 billion stock sale signals investor appetite for AI-related offerings, we can see that investors are ready to chow.

Dreambeans is a curated list of AI-illustrated "stories" culled from the personal data in your Google account.

O Microsoft Build 2026 foi o palco escolhido pela tecnológica para o lançamento de novos modelos de IA próprios para reduzir a dependência da OpenAI, do novo chip quântico Majorana 2 e de uma plataforma de agentes que vai ao "coração" de como as organizações trabalham.

The post Microsoft aposta em novos modelos próprios, agentes que conhecem as empresas e no chip quântico Majorana 2 appeared first on Tek Notícias.

Jess Asato was portrayed wearing a bikini in Grok-generated images after she criticised creation of such non-consensual pictures

A Labour MP has taken legal action against Elon Musk’s xAI company after saying its Grok tool helped a user produce fake sexualised pictures of her, part of a wave of such images that flooded the social media platform X earlier this year.

Jess Asato, the MP for Lowestoft, said in January that seeing herself portrayed by the AI tool as wearing a bikini without her consent was “violating”.

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© Photograph: PA Images/Alamy

© Photograph: PA Images/Alamy

© Photograph: PA Images/Alamy

This week we've got tandem hands-ons with Google's new Gemini AI agent - Spark - from my colleagues David Pierce and Jay Peters. Their takeaways are similar: It's so effective that it's scary. Spark knew that David's dog is named Frida and knew the first name of Jay's wife, even though neither of them explicitly provided this information to Google. But what's scary to me is how all of this stuff seems geared toward a future of "productivity" that completely misses what needs to be fixed in our world.

"Productivity" is often pitched as a panacea for what befalls us in our personal lives, even going so far as to implicate our moral worthiness …

Read the full story at The Verge.

Meta is scaling back parts of its employee tracking initiative after staff objected to software that collected mouse movements, clicks, keystrokes, and other actions for AI training data. According to Reuters, the company will now let workers pause collection for up to 30 minutes and request exemptions. Reuters reports: [Stephane Kasriel, a vice president in Meta's AI model-building Superintelligence Labs unit] said the team behind the software had also introduced "several optimizations" to reduce its impact on computer battery life, after employees complained it was consuming so much data it was causing their home internet usage to spike. "While we remain confident in the privacy protections we put in place at launch, which went through several layers of risk review, we have heard your concerns about personal data on work devices, battery life, and wanting more control over when capturing happens," Kasriel said in the memo.

Read more of this story at Slashdot.

Christi Hill and male officer misidentified in Vickrum Digwa murder case on AI platforms including Grok

A former police officer has been forced to flee to a safe space after she was falsely accused online of being involved in the arrest of Henry Nowak.

Christi Hill, who served as a police constable for 12 years, has criticised social media and AI platforms, including Elon Musk’s Grok, for spreading the false claim that she was one of the officers who arrested Nowak as he lay dying after being stabbed by Vickrum Digwa.

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© Photograph: Lab Mo/SOPA Images/Shutterstock

© Photograph: Lab Mo/SOPA Images/Shutterstock

© Photograph: Lab Mo/SOPA Images/Shutterstock

Hackers appeared to take over victims’ accounts even after Meta said it fixed its AI-powered support chatbot, which granted hackers access to victims’ accounts.

Amazon's updated search bar will now show you AI-generated images of products as you describe them. For now, the in-app feature only surfaces AI images of clothing and home goods, allowing you to tap on the image that best matches what you're looking for and search for similar-looking items.

In a blog post, Amazon positions the feature as a way to help you search for items if you can't remember the name of a specific texture or style, like describing a "shirt with a draped collar" if you can't think of "cowl neck." The feature seems like it might come in handy in these kinds of scenarios, but it doesn't really add much if you're just searc …

Read the full story at The Verge.

Amazon will use visual search and AI to show AI-generated product images that match your search queries. The retailer says it will help guide users to products.

The startup's own stack for Africa and Middle East is now handling more than 17,000 calls per day.

U.K. regulators are requiring Google offer a tool allowing website publishers to opt-out of generative AI search features. The option will be tested in the U.K. then rolled out globally.

A groundbreaking report from the United Nations University Institute for Water, Environment and Health (UNU-INWEH) unveils the extensive environmental footprint underpinning artificial intelligence (AI) across carbon emissions, water usage, and land occupation, exposing complexities beyond the often-cited surge in electricity consumption. This comprehensive study paints a sobering picture of the physical infrastructure, resource demands, and environmental justice implications accompanying the explosive growth of AI technologies worldwide.

At the heart of this investigation lies the understanding that AI’s environmental impact extends well beyond energy consumption and carbon footprints. The report emphasizes the intricate supply chains and physical systems supporting AI: sprawling data centers, semiconductor fabrication, cooling mechanisms, and resources extracted for critical minerals. These components introduce significant water withdrawals, land use for energy infrastructure, and the escalating challenge of electronic waste management. In doing so, the report marks a crucial shift from the conventional carbon-centric discussions toward a holistic environmental perspective.

The scale of AI’s operational energy demands is staggering. Projections estimate that data centers, the backbone of AI computing, will consume 448 terawatt-hours of electricity in 2025—an amount equivalent to the national consumption of France, ranking them as the 11th largest global electricity user if considered a country. Notably, AI workloads account for roughly 20% of this power use, a share predicted to rise to 40% by 2030. Should current growth trajectories persist, the energy consumption attributed to AI could nearly triple by 2030, corresponding to around 945 terawatt-hours annually and equating to nearly 3% of worldwide electricity usage. This prodigious demand alone could sustain the energy needs of 1.3 billion people living in Sub-Saharan Africa for over five years—a demographic particularly vulnerable to energy scarcity.

Beyond energy, the water footprint of AI infrastructure poses an underappreciated risk to global freshwater resources. Data centers currently utilize an estimated 9.3 trillion liters of water, sufficing for the drinking requirements of the global population for approximately 1.6 years. The report underscores that water withdrawals, especially in arid or depleted regions, can severely stress aquatic ecosystems and groundwater reserves, even when some of this water is eventually returned. Moreover, land requirements for electricity generation related to AI’s growth are poised to surpass 14,000 square kilometers by 2030, roughly the size of Northern Ireland, presenting additional challenges for land management and biodiversity conservation.

Training state-of-the-art AI models such as ChatGPT-5 demands colossal energy inputs, consuming around 100 gigawatt-hours of electricity—comparable to the annual residential energy consumption of 770,000 individuals in Sub-Saharan Africa. The corresponding water and land footprints—1 billion liters and 1.5 square kilometers respectively—highlight the significant spatial and resource components embedded within AI’s developmental phase. However, the report pivots attention toward the AI’s ubiquitous daily use, which far exceeds the energy footprint of training alone. For instance, ChatGPT processes roughly 2.5 billion prompts daily, translating into annual electricity use of about 383 gigawatt-hours and water consumption sufficient for half a million people’s domestic needs annually, reflecting the enormous cumulative resource drain of AI services.

The environmental cost per AI interaction varies significantly by technology and usage context. For example, Google handles approximately 5 trillion search queries each year, where a traditional search requires around 0.3 watt-hours, but AI-enhanced generative searches inflate this figure to up to 3 watt-hours—a tenfold increase. Additionally, AI-generated video content emerges as a looming environmental crisis. A single high-resolution video clip may demand more than 415 watt-hours of energy, outstripping the energy required for producing hundreds of static AI-generated images. Given that energy requirements rise quadratically with resolution and frame count, the burgeoning prevalence of AI video generation could rapidly escalate infrastructure strain.

Crucially, the report explores the intricate trade-offs between carbon, water, and land footprints in AI energy sourcing. Transitioning from coal to bioenergy production can reduce carbon emissions by an average of 72%, yet simultaneously inflates water consumption more than thirtyfold and enlarges land use by a factor of one hundred. This nuance dismantles simplistic narratives around “green” or “renewable-powered” data centers and compels stakeholders to weigh multifaceted environmental impacts in energy procurement and infrastructure siting. The geographic variance in electricity supply further complicates the notion of universal sustainability metrics.

The environmental and social implications extend deeply into the realm of mineral extraction and electronic waste. AI infrastructure relies on minerals often mined under conditions that disproportionately harm communities in the Global South, exacerbating environmental degradation and social injustices. By 2030, AI-related hardware waste could reach 2.5 million metric tons annually—equivalent to discarding a quarter of a million Eiffel Towers—posing severe challenges for hazardous material management and pollution control. The report calls for robust lifecycle governance spanning from resource acquisition through responsible disposal to mitigate these burdens on vulnerable populations.

Disparities in AI infrastructure distribution exacerbate global inequalities. Currently, 90% of specialized AI cloud infrastructure capacity is concentrated in just two countries—the United States and China—with only 32 nations worldwide hosting such facilities at all. The vast majority of over 150 countries remain dependent consumers of AI services, bearing metal extraction and e-waste costs disproportionately while reaping scant strategic benefits. This digital divide manifests not only as an economic disparity but as an environmental justice concern demanding urgent attention and coordinated global action.

Ireland stands as a cautionary exemplar of the perils of unregulated AI infrastructure growth. Data centers now consume 21% of the country’s total metered electricity—a sharp rise from 5% in 2015—exceeding the energy used by all urban households combined. The national grid operator’s decision to pause new data center approvals until 2028 encapsulates the critical need for integrative energy planning and sustainable infrastructure development, highlighting the risks that other nations might encounter without proactive governance.

The report presents a compelling call to action and a roadmap for responsible AI governance framed around six foundational principles: transparency in environmental impact reporting; efficiency engineered at the design phase; equity and environmental justice considerations; lifecycle accountability; international collaboration; and sustainable use practices. It addresses varied stakeholders—from governments integrating AI into energy and land-use policy, to industry prioritizing footprint-aware model development, to users selecting appropriate computational scales—emphasizing governance as a collective, multilevel imperative.

Finally, the report recognizes user interface design and behavioral choices as potent instruments for environmental stewardship. For instance, adopting a “concise mode” in AI interactions, which avoids unnecessary politeness or verbosity, can reduce token output by 30%, saving significant electricity—estimated at 87 to 98 gigawatt-hours annually. This reduction parallels the residential energy usage of 760,000 individuals in Sub-Saharan Africa, illustrating how seemingly small efficiency gains in user interactions and product defaults can cascade into substantial sustainability dividends.

In its starkest summary, UNU-INWEH’s report declares that AI’s environmental footprint is neither fixed nor inevitable; it is the product of cumulative engineering, usage, and policy decisions rooted in physical realities. Confronting AI’s rapid expansion with holistic, transparent, and just frameworks offers the only viable path to ensuring that technological progress advances human well-being within planetary boundaries. Without systemic and cooperative stewardship, the opportunity for AI to be a force for sustainable innovation risks being eclipsed by escalating environmental costs and intensifying inequalities.

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Subject of Research: Environmental impacts of AI infrastructure and usage, including energy, carbon, water, land footprints, and associated social justice concerns.

Article Title: Environmental Cost of AI’s Energy Use: Carbon, Water and Land Footprints

News Publication Date: 2026

Web References:
https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints

References:
Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., Madani, K. (2026). Environmental Cost of AI’s Energy Use: Carbon, Water and Land Footprints. United Nations University Institute for Water, Environment and Health (UNU-INWEH), Richmond Hill, Ontario, Canada. doi: 10.53328/INR26RMA002

Image Credits: United Nations University Institute for Water, Environment and Health (UNU-INWEH)

Keywords

Artificial intelligence, AI energy consumption, carbon emissions, water footprint, land footprint, environmental justice, data centers, AI infrastructure, e-waste, sustainable AI, mineral extraction, global digital divide

At Microsoft's annual Build conference on Tuesday, the company announced a slew of new or expanded AI initiatives, including a super app, in-house reasoning models, a cybersecurity tool, and OpenClaw-esque AI agents. All this news added up to a clear message: Microsoft is positioned to be one of the biggest players in AI, and it's finally acting like it.

For years, Microsoft's AI business leaned hard on its early and exclusive partnership with OpenAI. But the drama-filled marriage slowly devolved into a situationship, and the pair effectively separated in late April (though Microsoft is still OpenAI's primary cloud partner - for now). This …

Read the full story at The Verge.

Asking ChatGPT to think like a curious child can be an unexpectedly effective way to spot gaps in logic.

A year after Mark Zuckerberg installed Alexandr Wang to jolt Meta’s artificial intelligence efforts into wartime mode, the $1.5 trillion company has produced Muse Spark, its most credible AI model yet.

By handing responsibility for Meta’s AI revival to a then-28-year-old start-up founder rather than a veteran researcher, Zuckerberg bet that an outsider’s urgency and ambition could succeed where the company’s established AI organisation had struggled.

According to interviews with current and former Meta employees, and associates of Wang, the billionaire wunderkind has now begun to eke out results, while navigating criticism over his experience, early research challenges and the esoteric internal politics of working at a Big Tech behemoth.

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Making sense of what you get from Claude AI at the free tier, especially as limits are vague and keep shifting.

Making sense of what you get from Claude AI at the free tier, especially as limits are vague and keep shifting.