Breaking AI & Tech News Analyzed

The Shift to Token-Based Billing GitHub Copilot, a tool developed by Microsoft, is changing its billing system from a flat subscription rate to a token-usage system. This change, effective June 1, has sparked concern among developers who fear significant cost increases. The Impact on Developers The new system will charge users based on the number of tokens they use, rather than a low flat rate based on requests. Some developers have taken to online forums to express their discontent, sharing screenshots of drastic cost increases. One developer reported a potential increase from $29 to $750 per month, while another saw costs jump from $50 to $3,000. The Data Analysis Previous flat rate: $29-$50 per month New token-based rate: potentially $750-$3,000 per month The Impact Analysis The changes could disproportionately affect smaller companies and workers, who may struggle to balance their monthly budgets. Some developers have argued that the new system is unfair, given that Microsoft previously encouraged indiscriminate use of the chatbot. The Prediction As the new billing system takes effect, it's likely that some developers will be forced to reevaluate their use of GitHub Copilot or seek alternative tools. The move may also lead to increased scrutiny of Microsoft's pricing strategies and the economics behind its products.

Meta's Foray into AI Wearables Meta is developing an AI-powered pendant that it plans to start testing in the next year, according to a memo viewed by The Information. This device would presumably build on the work of Limitless, an AI device startup that Meta acquired at the end of 2025. The Acquisition and Its Implications The startup made an AI pendant that users could attach to their shirt or wear as a necklace to record their conversations. At the time, Meta said the acquisition would allow it to "accelerate our work to build AI-enabled wearables." Challenges in AI Wearables Earlier AI wearables have failed to catch on with consumers — perhaps due to privacy concerns and tone-deaf marketing, or perhaps because they just weren’t that useful. But companies like OpenAI aren’t giving up. Meta's Future Plans The memo also reportedly states that the company is planning to expand its lineup of AI glasses and launch a business subscription called Wearables for Work. With all these planned devices, Meta is apparently hoping to reverse the fortunes of its hardware-focused Reality Labs division, which lost $4 billion in the first quarter of this year.

The 24/7 Agentic Assistant Breakthrough Google has introduced Gemini Spark, a 24/7 agentic assistant designed to help users navigate their digital lives autonomously. Unlike traditional chatbots that require local hardware to stay active, Spark runs on virtual machines in the cloud, allowing users to close their laptops while tasks are being completed. The service is deeply integrated into the Google Workspace ecosystem, connecting with Gmail, Calendar, Docs, Sheets, and Slides to handle work-adjacent tasks. Cloud-Native Architecture: Spark operates continuously without the need for the user's device to be awake. Work-Adjacent Focus: It is optimized for tasks that bridge the gap between manual labor and automation, such as summarizing inboxes or organizing spreadsheets. CEO Endorsement: Sundar Pichai positioned Spark as an accessible entry point into agentic AI, contrasting it with more complex systems that require constant user oversight. Real-World Performance Metrics Testing the assistant revealed a mix of high-utility features and frustrating limitations. While Spark excelled at complex research and aggregation, it struggled with specific execution details and integrations. Shopping Research: Spark successfully identified weekly deals and suggested coupon stacking strategies. However, it failed to validate a specific promo code, requiring manual intervention. Packing Lists: The AI provided highly accurate suggestions for a day trip, including weather-appropriate items and event restrictions. However, it failed to export the list to Google Keep, instead offering to create a document or email—a significant usability oversight. Event Discovery: Spark successfully aggregated local events from multiple sources, identifying niche opportunities like the 'Annual Beaver Queen Pageant' that would be missed by manual searching. Newsletter Summaries: The assistant generated summaries with context but missed one requested article and suffered from link redirection issues. The Ecosystem Lock-In Challenge The primary barrier to Spark's adoption is its heavy reliance on the Google ecosystem, creating a 'walled garden' effect that limits its utility outside of Google services. The lack of integration with Google Keep is a major usability gap, as the notetaking app is essential for personal productivity lists. Furthermore, the confusion surrounding its branding—separate from the main Gemini chatbot interface—adds unnecessary cognitive load for users trying to distinguish between 'questions' and 'tasks.' Platform Limitations: The tool cannot be accessed via iPhone hardware buttons, requiring users to manually launch the app. Integration Gaps: Current limitations in MCP (Model Context Protocol) integrations prevent Spark from booking external services like restaurants or flights. Branding Confusion: The industry is saturated with AI names, and Spark's standalone toggle adds to the mental load rather than simplifying it. The Future of Standalone AI Toggles Google's experiment with Spark suggests that standalone AI products may struggle to justify their existence in a crowded market. The future of AI assistants lies in unified interfaces where functionality is integrated seamlessly rather than separated by confusing toggles. For Spark to become a 'must-have,' Google must address the lack of cross-platform accessibility and expand its integration capabilities beyond the Google universe.

The Lead This week at TechCrunch’s StrictlyVC event in Athens, I sat down with three top VCs to discuss the current state of venture investing, the wave of mega-IPOs, and where they see opportunities in AI. VC Insights on AI and Mega-IPOs The conversation featured Niko Bonatsos of Verdict Capital, Andreas Stavropoulos of Threshold Ventures, and Ben Blume of Atomico. They discussed the potential impact of SpaceX's reported $1.75 trillion valuation at IPO, as well as the opportunities and challenges in the AI space. The Data Analysis SpaceX's potential $1.75 trillion valuation at IPO OpenAI and Anthropic potentially not far behind in terms of valuation Three-quarters of all venture capital raised over the last year went into five companies $500 million fund looking at the same opportunities as people investing from a $10 billion or $15 billion fund The Impact Analysis The VCs discussed how the current flood of capital into AI may be justified by future earnings, but also acknowledged the risk of extreme FOMO (fear of missing out). They also touched on the challenges of pricing deals when things are moving fast and the importance of looking beyond age as a proxy for entrepreneurial potential. The Prediction The VCs see opportunities in areas such as consumer fintech, AI interacting with the physical world, and robotics. They predict that the next generation of companies will be able to go after much larger markets and that immigrant founders will continue to play a significant role in driving innovation.

The Browser Wars: An Overview The browser market is dominated by Google Chrome and Apple Safari, but users seeking alternatives have a variety of options. These alternative browsers aim to challenge the industry giants with innovative features, AI integration, and a focus on user well-being. AI-Powered Browsers Several startups have launched AI-powered browsers, including: Perplexity's Comet: A chatbot-based search engine that can perform actions like summarizing emails and browsing web pages. Currently available only to users with Perplexity's $200/month Max plan. The Browser Company's Dia: An AI-centric browser that helps users navigate the web more easily. Currently available as an invite-only beta. Opera's Neon: A browser with contextual awareness that can perform tasks like researching and shopping. Expected to be a subscription product, but pricing has not been announced. OpenAI's Atlas: An AI-powered web browser that allows users to ask ChatGPT about search results and browse websites within the chatbot. Currently available on macOS, with plans for Windows, iOS, and Android. Privacy-First Browsers Some browsers prioritize user privacy, including: Brave: A well-known privacy-first browser with built-in ad and tracker blocking capabilities. It also features a gamified approach to browsing and rewards users with its own cryptocurrency, Basic Attention Token (BAT). DuckDuckGo: A browser that blocks trackers and ads, and doesn't track user data. It has also introduced generative AI features, such as a chatbot. Ladybird: An open-source browser that aims to build an entirely new browser from scratch, without relying on existing code. It will offer features to minimize data collection, such as a built-in ad blocker. Productivity-Focused Browsers Some browsers focus on productivity and user well-being, including: SigmaOS: A Mac-only browser with a workspace-style interface that emphasizes productivity. It displays tabs vertically and allows users to create workspaces to better organize different activities. Zen Browser: An open-source browser that aims to create a "calmer internet" with features like tab organization and community-made plug-ins and themes. Opera Air: A mindfulness-themed browser that includes features designed to support mental well-being, such as break reminders and breathing exercises. Vivaldi: A Chromium-based browser with a customizable user interface and features like ad blocking and a password manager. The Future of Browsers The browser wars are expected to continue, with more innovative features and AI integration on the horizon. As users become increasingly concerned about privacy and productivity, browsers that prioritize these aspects are likely to gain popularity.

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.

Why Fans Are the Smart Summer Cooling ChoiceThe Guardian’s award‑winning tech journalist measured 16 fans and several evaporative coolers to see how they perform against a typical portable air‑conditioner that draws 1,000W (about 26p per hour). Fans in the test consumed between 8W and 60W, delivering a far lower electricity bill and carbon footprint while still moving enough air to make a noticeable temperature drop.Power Consumption Numbers Show Fans Beat Air‑ConditionersAirCraft Lume – 18W on top setting; could run 56 hours for the cost of one hour of air‑con.Dreo TurboCool misting fan 765S – 22W, best overall cooling performance.Devola desk fan – 12W, cheapest at £64.99.Shark FlexBreeze Pro Mist – 30W, premium misting option at £249.99.Swan Nordic evaporative cooler – 15W, lowest‑energy water‑based cooler at £69.Cooling Comfort Meets Carbon Savings for UK HouseholdsRunning a fan instead of an air‑conditioner can cut summer electricity use by up to 95 %, translating into lower bills and reduced greenhouse‑gas emissions. For a typical UK home, swapping a 1,000W air‑con for an 18W fan saves roughly £23 per month and avoids about 0.12 tCO₂ of emissions.What’s Next for Home Cooling in a Warming Climate?As heatwaves become more frequent, manufacturers are likely to focus on quieter, smarter fans with integrated sensors that adjust speed automatically. Expect more hybrid designs that combine low‑energy misting with airflow optimisation, giving consumers a wider menu of carbon‑friendly cooling solutions.

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.