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The Concept of 'Mind Children' A few months ago, an AI researcher from Europe attended a dinner party in Silicon Valley. During one of the many courses, the host addressed his guests, all of whom worked in AI. The researcher paraphrased his message like this: “Isn’t it amazing that we are the last generation of humans who will need to think about procreating biologically? We were lucky enough to be born at a time where we can simply upload our consciousnesses instead.” The Book That Sparked the Conversation The book in question was Hans Moravec’s Mind Children: The Future of Robot and Human Intelligence, which was first published in 1988, and which at the time, according to economist and futurist Robin Hanson of George Mason University, caused a big splash in a small pond – the community of robotics and machine-learning experts to which Moravec belonged. The Data Analysis Moravec’s book is more philosophical treatise than technological manual, but the central idea is that cultural evolution has long since taken over from biological evolution as the most powerful force shaping humanity. The logical extrapolation of this is that the information that encodes our future selves would soon be packed into hardware and software rather than DNA. The Impact Analysis Angela Aristidou, who studies the real-life deployment of AI at University College London, is not surprised that Moravec’s book is enjoying a revival. She says that what in 1988 might have read like science fiction – and still might to most of us – looks eminently realisable to those in the know. The Prediction Hanson shares his conviction that the revolution is inevitable, as soon as AI attains something experts agree to call human-level intelligence. “We are going to generate an explosion of things like us in the future, who will be different from us in many ways,” Hanson says. “To the extent that they have minds somewhat like ours, they are our mind children.”

Australia faces a rapidly ageing population and chronic shortages of aged‑care staff, driving an emerging industry of AI‑enabled robots, virtual‑reality experiences and other digital tools aimed at improving resident wellbeing.AI and Robotics in Australian Aged Care: Current LandscapeProf Wendy Moyle, who runs the social‑robotics laboratory at Griffith University, argues that technology should support humans rather than replace them. She points to a Chinese virtual hospital as a sign of rapid progress, while warning that many inventions are built without input from health professionals or end‑users.Demographic Trends Driving Demand for Tech SolutionsAustralia’s population is ageing, increasing demand for residential and home‑based care.Workforce shortages in aged‑care facilities exacerbate challenges of neglect and abuse.Technology is not a magic bullet, but pilots show measurable benefits for mood, cognition and social isolation.How Tech Is Shaping Human Connection in Care HomesAt St Vincent’s Care in Toowoomba, residents board the “St Vincent’s Express” – a replica train station and carriage that combines physical sets with screens showing Alpine scenery. Manager Elzette Lategan says the experience “takes boredom, loneliness and isolation away and brings in hope.”The organisation Aged Care Research and Industry Innovation Australia notes that virtual reality can improve mood, memory, problem‑solving and spatial awareness, and may reduce pain and anxiety.Companion robots such as Abi, produced by Andromeda, use AI and machine‑learning to recognise faces, interpret emotions and remember conversations, speaking in 90 languages to cater to diverse residents.Future Outlook: Integrating AI While Preserving HumanityMoyle cautions that Australia must “think outside the square,” ensuring that tech augments the human touch rather than substituting it. Continued collaboration between engineers, clinicians and residents will be essential to scale innovations that genuinely enhance quality of life for older Australians.

The Inevitable Integration of AI in DevelopmentIn 2026, artificial intelligence has become an inseparable tool for developers, yet this reliance may be masking a critical productivity crisis.Researchers at METR discovered that most developers will not participate in studies without AI assistance.This dependency suggests a psychological shift where AI is no longer viewed as an assistant but a requirement.The "Tokenmaxxing" Crisis and Budget BlowoutsThe trend of measuring productivity by token usage, known as "tokenmaxxing," has led to significant financial waste.Amazon shut down its internal leaderboard, Kirorank, after employees gamed the system to run up costs.Uber reportedly exhausted its 2026 AI budget in just four months without measurable project increases.Self-reported data shows a 2x increase in perceived value, but independent analysis suggests 44% of tokens are spent fixing bugs generated by AI.Code review tools indicate AI produces 1.7x more problems than human code.The Hidden Cost of Speed: Maintenance and QualityWhile AI generates code faster, it introduces long-term maintenance costs that developers are currently ignoring.Programmer James Shore warns that trading a temporary speed boost for permanent indenture is a dangerous strategy.Researchers from Singapore Management University have confirmed that AI-generated code can introduce significant long-term maintenance burdens.The Future of Human-AI CollaborationThe industry is moving toward a model where AI is a junior developer that requires constant oversight.Scott Wu (Cognition) admits his AI agent Devin is currently a junior-to-mid-level programmer.Experts recommend that humans must review AI work as carefully as they would a junior developer's code.Software architecture and security design must remain human-centric tasks.

The Revolutionary GeneticistCraig Venter, the pioneering geneticist who revolutionized genome sequencing and challenged traditional scientific approaches, has died at age 79. His announcement at the 2001 BioVision conference that humans possess only about 30,000 genes—far fewer than the previously estimated 100,000—shattered scientific assumptions about genetic determinism. "We simply do not have enough genes for this idea of biological determinism to be right," Venter declared, emphasizing that human diversity is shaped primarily by environmental influences rather than hard-wired genetic code.The Breakthrough in Genome SequencingVenter's most significant contribution was developing the revolutionary whole genome shotgun sequencing technique, which allowed for faster, more efficient genome mapping. In 1995, his team achieved the remarkable feat of sequencing the first genome of a living organism, the bacterium Haemophilus influenzae. This breakthrough led to the founding of Celera Genomics in 1998, which aimed to sequence the entire human genome using Venter's innovative methods.The competition between Venter's privately funded Celera and the publicly funded Human Genome Project, spearheaded by the US government and UK's Wellcome Trust, created what scientists described as "testosterone-driven" rivalry. Despite tensions, this competition dramatically accelerated progress in genomics research, culminating in the announcement of the first draft human genome sequence at a White House ceremony in June 2000.The Scientific MaverickVenter was as famous for his bold personality as for his scientific achievements. A brilliant entrepreneur and unapologetic self-promoter, he enjoyed showcasing his success, private plane, yacht, and luxury watches. This flamboyant approach made him both admired and controversial. James Watson, co-discoverer of DNA's double-helix structure, compared Venter to Hitler for attempting to patent human genes, while others nicknamed him "Darth" Venter after the Star Wars villain.His tendency to break scientific protocols became evident when he revealed that much of the DNA used in Celera's human genome sequencing came from his own cells—a decision that annoyed scientists who felt he had subverted standard processes. "I've been accused of that so many times, I've got over it," Venter responded, noting that the analysis revealed he had an abnormal fat metabolism and elevated risk of Alzheimer's disease.A Life Shaped by Science and WarBorn in Salt Lake City, Utah, Venter had an unconventional path to scientific greatness. Growing up in California, he had a poor academic record and initially pursued "pursuits that involved drink, girls and bodysurfing" rather than education. His life took a dramatic turn during the Vietnam War, where he served as a senior corpsman in a naval hospital's intensive care unit in Da Nang."I witnessed several hundred soldiers die, more often than not while I was massaging their hearts – at times with my bare hand – or attempting to breathe life into them," Venter recalled. "Vietnam would teach me more than I ever wanted to know about the fragility of life." This experience sparked his interest in life sciences, leading him to study at the University of California, San Diego, where he earned a PhD in physiology and pharmacology in 1975.The Legacy of a Scientific PioneerAfter being dismissed as head of Celera in 2002, Venter used his substantial payoff to endow the J. Craig Venter Institute with $100 million. There, he pursued ambitious projects including designing energy-producing microbes and synthesizing bacterial genomes. He later founded Human Longevity and Diploid Genomics, companies that aim to combine artificial intelligence with advances in aging research and gene sequencing to extend human lifespans and improve disease diagnosis.While some of Venter's claims about the primacy of environmental influences over genetics have been questioned, his impact on genomics research remains undeniable. His revolutionary sequencing techniques transformed the field, and his competitive approach accelerated what would have otherwise been a much slower process of mapping the human genome. As the scientific community remembers Craig Venter, it acknowledges a complex figure who was simultaneously a brilliant innovator, a controversial competitor, and a transformative force in our understanding of life's fundamental building blocks.

Global Study Quantifies Gender Attractiveness Gap Across AgesThe research team led by Dr Eugen Wassiliwizky at the Max Planck Institute for Empirical Aesthetics compiled the world’s largest dataset on facial attractiveness, drawing from 52 studies across 76 countries.Numbers Behind the Gap: 1.5 Million Ratings Reveal 60% Preference1.5 million attractiveness ratings17,000 distinct faces evaluated30,000 individual ratersAverage female face rated more attractive than 60% of male facesGap strongest in Western cultures, present across all sexual orientationsWhen participants rated themselves, the gender gap vanished, underscoring the role of external perception.Implications for Evolutionary Theory and Social PerceptionThe findings revive debate over Darwinian sexual selection. While Darwin noted male ornamentation in many species, he considered humans an exception where male competition dominated. This study suggests a universal bias toward rounder, more feminine facial structures, which may be linked to infant‑like features rather than purely cultural norms.Historical language—"the fairer sex", "le beau sexe"—reflects a long‑standing perception that the research now quantifies.Future Research Directions and Societal ShiftsAs the attractiveness gap diminishes after age 80, researchers hypothesize that facial structural differences shrink with age, reducing perceived bias. Ongoing work will explore:Neuro‑cognitive responses to facial roundness across agesCross‑cultural variations beyond the current datasetPotential impacts on age‑related social dynamics and media representationThe study, published in Proceedings of the Royal Society B, calls for cautious interpretation but highlights a robust, global pattern that challenges purely cultural explanations.

The LeadSimon Rogers, Google's data editor, presents a fascinating exploration of human curiosity through the lens of search queries in his book 'What We Ask Google.' The compilation of anonymized search data from 2004 to the present offers a unique mirror into our collective concerns, from parenting questions to existential queries, though the review suggests the book presents a somewhat rose-tinted view of technology's role in our lives.The Book OverviewRogers, a former Guardian journalist who joined Google in 2015, organizes the search data into themed chapters that blend statistics with personal anecdotes. The book traces search trends back to 2004, when internet access was limited to less than half of UK households. Rogers posits that our search queries reveal something 'real and deep and meaningful about who we are as humans,' arguing that even brief searches indicate genuine care and concern.The Data InsightsThe book reveals intriguing patterns in human search behavior. Parenting-related queries like 'Why do babies get hiccups?' and 'How to tell kids about divorce?' appear frequently. Notably, in early 2023, searches for 'take care of parents' surpassed 'take care of kids,' reflecting the demographic pressures on the sandwich generation. The data also highlights geographical peculiarities, such as Austrians, Nigerians, and Canadians most frequently asking about back pain at night, and Americans in Kansas struggling to spell 'chaos' while their Missouri neighbors are stumped by 'unconscious.'The Critical PerspectiveThe review identifies significant limitations in Rogers' approach. As a 'company man' who joined Google from Twitter, the book presents an overly optimistic view of the internet and Google's role in society. There's minimal acknowledgment of the AI revolution's impact on search behavior and its consequences for content creators. The book also avoids addressing darker aspects of human nature reflected in search histories, political influences like Donald Trump, and how big tech may actually amplify parenting anxieties rather than alleviate them.The Cultural ImpactDespite its limitations, the book offers a diverting window into collective curiosity. It demonstrates how our search habits reflect societal concerns, from the practical ('How to fold a burrito') to the profound ('How often can you donate plasma?'). The reviewer notes that Rogers interprets this latter query as evidence of altruism rather than recognizing it as a symptom of US healthcare inequities. The book ultimately serves as an interesting, if selective, cultural artifact that captures our digital age's peculiarities and preoccupations, even if it doesn't fully confront the complexities of our relationship with technology.

Why a Living AI Glossary Matters NowArtificial intelligence is reshaping every industry, but its rapid evolution has spawned a parallel explosion of terminology that can leave even seasoned technologists feeling insecure. TechCrunch’s new glossary aims to provide a single, regularly‑updated reference that translates the most common AI buzzwords into plain language.Key Definitions from AGI to RLHFThe article walks readers through a spectrum of concepts, including:Artificial General Intelligence (AGI) – AI that outperforms humans on most economically valuable tasks, as defined by OpenAI and Google DeepMind.AI Agent – An autonomous tool that can perform multi‑step tasks such as expense filing, ticket booking, or code maintenance.API Endpoints – “Buttons” that let software components interact, enabling agents to automate third‑party services.Chain‑of‑Thought Reasoning – A technique that breaks problems into intermediate steps to improve accuracy.Compute – The hardware (GPUs, CPUs, TPUs) that powers AI model training and inference.Deep Learning – Multi‑layered neural networks that learn features directly from data.Diffusion – The process behind many generative AI models that learns to reverse noise‑added data.Distillation – A teacher‑student method for creating smaller, faster models like GPT‑4 Turbo.Fine‑Tuning – Adding task‑specific data to a pre‑trained model to improve performance.GAN – Generative Adversarial Networks that pit a generator against a discriminator to produce realistic outputs.Hallucination – When models generate inaccurate or fabricated information.Inference – Running a trained model to make predictions, often accelerated by specialized hardware.LLM – Large Language Models that power assistants such as ChatGPT, Claude, Gemini, and Llama.Memory Cache (KV Caching) – An optimization that stores intermediate calculations to speed up inference.Open Source vs. Closed Source – The debate over publicly available model code (e.g., Meta’s Llama) versus proprietary systems (e.g., OpenAI’s GPT).Parallelization – Executing many calculations simultaneously, a cornerstone of modern AI hardware.RAMageddon – The current shortage of memory chips driven by AI data‑center demand.Recursive Self‑Improvement (RSI) – Models that can redesign themselves, a potential step toward singularity.Reinforcement Learning from Human Feedback (RLHF) – Training models with reward signals to improve helpfulness and safety.Tokens & Throughput – The basic units of text processing that determine cost and performance.Quantifying the AI Vocabulary ExplosionThe glossary covers more than 30 distinct terms, each accompanied by concise explanations and links to deeper resources. By cataloguing this breadth, the piece highlights how quickly the AI lexicon has expanded within just a few years of mainstream adoption.Implications for Developers, Investors, and the PublicUnderstanding this terminology is no longer optional. For developers, clear definitions accelerate product building and reduce miscommunication when integrating APIs or deploying agents. Investors gain a sharper lens for evaluating startup pitches that hinge on concepts like fine‑tuning or distillation. Meanwhile, the broader public can better assess claims about “AGI” or “hallucinations,” mitigating hype‑driven misinformation.Future of AI Terminology and Industry AdoptionTechCrunch positions the glossary as a “living document,” promising regular updates as new techniques (e.g., emerging diffusion variants or next‑gen RLHF methods) appear. As AI systems become more autonomous and specialized, the vocabulary will continue to evolve, making ongoing education essential for anyone interacting with the technology.

The Rise of AI Psychosis in Corporate Decision MakingBox founder Aaron Levie has identified a troubling trend in corporate America: what he calls "AI psychosis," where executives and decision-makers become so enamored with artificial intelligence that they believe it can replace human jobs without understanding what those roles truly entail. This overenthusiasm for AI is leading to significant workforce reductions and a growing backlash from both employees and users.Workforce Reductions Fueled by AI AmbitionThe consequences of this AI psychosis are already becoming apparent in the tech industry. Productivity software company ClickUp recently cut 22% of its workforce, citing a shift toward AI agents. This move is part of a larger trend where tech layoffs in 2026 are already nearly matching the total number of layoffs seen throughout all of 2025. These cuts suggest that companies are prioritizing AI implementation over human talent, often without fully understanding the implications.User Backlash Against Forced AI IntegrationWhile companies push AI solutions, users are increasingly resisting. DuckDuckGo has seen a surge in installations from users who want Google to stop forcing AI into search results and simply provide traditional links. This user backlash highlights a disconnect between corporate AI strategies and actual consumer preferences, suggesting that not all AI implementations are welcome or beneficial.The Duality of AI AdoptionAs TechCrunch's Equity podcast hosts discuss, both the AI-pilled (those enthusiastically embracing AI) and the AI-skeptical (those questioning its implementation) may have valid points. The challenge lies in finding a balance where AI augments human capabilities rather than replacing them entirely, and where technology serves actual needs rather than being implemented for its own sake.Future of Work in an AI-Driven EconomyAs AI continues to evolve, companies must develop more nuanced approaches to workforce planning and technology implementation. The current trend of replacing human workers with AI agents may prove shortsighted if it leads to decreased product quality, poor user experience, and loss of institutional knowledge. The future likely lies in hybrid models where AI and humans collaborate, each bringing their unique strengths to the workplace.

The Vision for AI Coding Agents Cognition CEO Scott Wu made headlines again this week when his two-year-old AI coding agent startup raised $1 billion at a $26 billion valuation. Cognition is the maker of Devin, one of the first and, arguably, most successful AI coding agents. Devin, the CEO says, “naturally owns tasks end to end.” The Future of Software Development In fact, in the blog post announcing that raise, Cognition laid out a vision where “we are shifting to a world of self-driving software development.” So, could Devin replace, say, a mid-level L4 programmer? Yes, and no, Wu told TechCrunch. “We’ve never thought about it as replacing humans. I know it’s like a scenario, folks have said these things. It has never been our view.” Preserving the Joy of Programming Wu emphasizes that the goal is not to make human programmers obsolete. “We are all programmers ourselves,” he explained. “I started coding when I was nine.” He views agents as another layer of abstraction between envisioning a software product and producing it, similar to how visual development environments abstracted software creation away from machine instructions. The Role of Devin in Cognition Cognition says that Devin’s role in its own company is to ship nearly all the software. The company says that 89% of code committed by its engineers was committed by Devin, and the rest by local agents. Wu explains that his agent’s role is largely to do the kinds of long-tail maintenance tasks that many programmers don’t like to do anyway: bringing old software up to date; moving applications off one platform and onto another. The Future of AI Agents Wu predicts that agents will enter other fields where they will learn tasks, from customer service to medicine, but hopes the goal will be to augment human workers in those areas, too. “Code and software has been the first to move, but we’ll see this happen in all these other industries,” he predicts. “One thing that’s been clear to us since the beginning is, it should always be up to the human what to do … you really see this in software engineering, but I think it’s true in all these other professions too.”