Latest AI News
Crypto exchange OKX wants AI agents to hire and pay each other
When AI agents begin working for people — and increasingly for one another — they will need a way to find jobs, pay for services, and build trust. Crypto exchangeOKXis betting that future is closer than many expect, launching a marketplace where AI agents can hire one another, settle payments autonomously, and build portable on-chain reputations. Called OKX AI, the marketplace opens to developers on Tuesday following a closed beta involving 50 early AI service providers. The marketplace builds on technology OKX previously developed to let AI agents hold digital wallets, make payments using stablecoins, and establish persistent identities. The launch marks OKX’s latest push beyond crypto trading as it seeks to become a broader fintech company. With more than 150 million users globally, OKX is betting the next generation of customers will not just be people or institutions, but AI agents capable of transacting autonomously, giving rise to an emerging “agent economy.” “The coming decade will be defined by one-person companies that generate over a million dollars in annual revenue – because every individual effectively gains an unlimited workforce,” Star Xu, founder and CEO of OKX, told TechCrunch. “Traditional financial infrastructure was built for humans. The agentic economy needs infrastructure designed for autonomous software. That is why we built OKX.AI.” Haider Rafique, OKX’s chief marketing officer and global managing partner, said the company believes “agentic commerce” could become a trillion-dollar market over the next five years, driven by micropayments and autonomous software. The marketplace is aimed at crypto developers building AI applications and solo entrepreneurs looking to automate parts of their businesses with AI agents, Rafique told TechCrunch. The company expects those developers to build applications for the marketplace, allowing other users to access AI-powered tools without having to build them from scratch. Among the early builders are CertiK, whose service lets AI agents assess the security of a crypto wallet or token before executing a transaction, and CoinAnk, which provides live market data on a pay-per-query basis. GenLayer, another launch partner, is bringing dispute-resolution infrastructure to the marketplace to help AI agents resolve contractual disagreements. By using blockchain-based payments and stablecoins, the company says AI agents can settle transactions around the clock, including low-value micropayments that would be impractical using conventional payment rails. Rafique said OKX is applying the same fraud detection, compliance systems, and internally developed infrastructure that underpin its cryptocurrency exchange to the marketplace, which will be rolled out in phases before becoming more widely available. OKX’s launch comes as technology companies and startups race to build the infrastructure that will underpin AI agents, from developer platforms and marketplaces to payment and identity systems. Albert Castellana, co-founder and CEO of GenLayer Labs, said the biggest challenge is not simply enabling AI agents to transact, but helping them discover one another and resolve disputes when things go wrong. “What we’re building is essentially a digital court system,” Castellana told TechCrunch. “The challenge for us is distribution. OKX already has that.” Rafique argues that OKX’s biggest advantage is not simply its technology but its reach. The company believes its existing network of crypto developers and users will help seed the marketplace, while its broader strategy extends well beyond digital assets. In March, Intercontinental Exchange (ICE), the parent company of the New York Stock Exchange, invested about $200 million in OKX at a$25 billion valuation. Rafique said the partnership is part of the company’s ambition to “modernize markets” through tokenization, while OKX AI represents its parallel effort to “modernize money” for an era of autonomous software. Developers access the marketplace through Onchain OS, OKX’s toolkit for connecting AI agents to blockchain-based services. The company said no OKX account is required to get started, and the platform is compatible with AI coding tools including Claude Code, Codex, Hermes, and OpenClaw. Because the marketplace is aimed first at developers rather than retail users, India features prominently in OKX’s plans. The country has emerged as one of the world’s largest hubs for AI and blockchain developers, a community the company hopes to reach even before a broader return of its crypto trading business. In 2024, OKXsuspended its services in Indiaas it navigated the country’s regulatory requirements for crypto exchanges. Rafique told TechCrunch that India remains one of the company’s highest-priority markets, adding that developer products such as OKX AI face fewer regulatory hurdles than spot crypto trading and could help the company reconnect with the country’s builder ecosystem sooner.
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Why Your Database Cannot Handle Agentic Workflows
The data stack was never built for agents, and the cost of that mismatch is now coming due.
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Merck Opens AI-Focused Global Capability Centre in Bengaluru: Report
The new Bengaluru facility will become a strategic hub for Merck’s AI, digital and enterprise technology operations, supporting global innovation efforts.
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Can Yogi Adityanath Turn Uttar Pradesh into a GCC Powerhouse?
The state is offering 43 million square feet of ready-to-move office space, dedicated AI and data centre parks, electronics manufacturing clusters, and IT and digital services infrastructure.
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The AI jobs debate just got messier
AI-related job loss fears grow each time another companyannounces a round of layoffs. Through May of 2026, companies announced that close to90,000 job cutswere tied to AI, and, by some accounts, up to 15% of U.S. jobs areprojectedto beeliminated by AIover the next five years. Promises from the tech industry that AI will also create new jobs does little to ease fears, especially for the generation wondering if anyone will be hiring when they graduate. A recent report from Ramp and Revelio Labs, which track enterprise AI spend and workforce records from nearly 22,000 companies, respectively, complicates that gloomy narrative. The report found that companies spending heavily on AI are growing headcount faster, even in the entry-level roles that many fear are doomed. According to the report, “high-intensity adopters” — firms that spend on average $30 per employee per month on AI in the first three months — saw headcount increase 10.2%. Headcount also rose across functions, includingengineering, sales, administration, customer service, finance, marketing, and scientist roles. The strongest job growth among high-intensity adopters was in the information sector, which includes software, internet, media, and tech-adjacent firms. Despite these positive signals, the data isn’t as rosy as it seems. It skews heavily towards tech-forward, knowledge-work firms — ones that might have VC-backing and are growing fast anyway, making it difficult to say whether AI is contributing to the hiring or just showing up at companies that are expanding anyway. “This paper does not show that AI universally creates jobs,” the paper’s authors admit, “but it does counter claims that AI will lead to broad job losses.” It also counters claims that AI is killing all junior jobs.Recent researchfrom Goldman Sachs found that AI has already erased about 16,000 net jobs per month over the past year, with Gen Z and entry level workers taking the brunt of the burden. But in tech-forward firms, the report finds that entry-level headcount actually rose by 12%. So what can we take away from this? Perhaps that AI isn’t always a tool for labor substitution, but that it can be a tool for firm-expansion instead. “For software and technology firms, AI can make core output cheaper or faster to produce: writing code, debugging, building internal tools, producing technical documentation, and supporting product development,” the report reads. “Lower production costs in these workflows can raise the return to expanding the whole firm, not just the engineering team.” But companies that buy subscriptions and run pilots, yet did not go on to make sustained investments, don’t tend to see any gains in headcount, per the report. That sets up the potential for awidening gapbetween firms that have the resources — like capital, technical staff, founder networks, and management bandwidth — to turn AI adoption into actual business gains and those that are stuck experimenting with subscriptions. In other words, this report suggests that firms that already have the resources are the ones who will see the largest gains. The paper’s authors speculate such a divide may continue to grow, saying: “Firms without those channels may fall behind.”
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Gujarat Taps IBM, IAIRO to establish Industrial AI Centre of Excellence
Envisioned as a ‘living lab’, the Industrial AI CoE will support the development, testing, and adoption of industrial AI applications
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CoreWeave Launches ARIA to Help Researchers Find Hidden Patterns in AI Experiments
ARIA analyses thousands of experiment runs in minutes, surfaces hidden patterns and recommends improvements to accelerate AI innovation
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Delhi to Roll Out AI-Enabled PUCC 3.0 Ahead of Winter Pollution Season: Report
The new PUCC 3.0 system will use AI, geotagging and encrypted data transmission to curb fraudulent vehicle emission certificates.
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MongoDB to Upskill 2 Mn Builders in India by 2030, Unveils AI Retrieval Tools
The company announced Voyage Context 4, Hybrid Search, and Native Reranking, saying the technologies work together to improve retrieval quality.
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Vibe coding platform Base44 launches own model as AI startups seek defensibility
Base44, the vibe coding platform thatWix acquired for $80 millionjust one year ago — when the company was barely six moths old and had a team of eight — has started rolling out its own AI model to support its users in creating apps with natural language. The move comes as the discussion in AI circles has intensified over whether frontier models are best suited for all use cases. A related question is whether businesses built on top of someone else’s models are truly defensible long-term. The latest move of Base44, based in the Bay Area, speaks to both. While its custom LLM is only just rolling out, Base44 hopes that it will eventually outperform frontier models. According to its founder, Maor Shlomo, “training and owning the model as part of [our] entire stack allows us a lot more optimizations on latency, cost, and efficiency.” At first glance, this could be a way to stay ahead of competitors such asSwedish startup Lovable, which reachedunicorn statusin its Series A round last summer and thatrelies on external LLMs. However, Shlomo expects that others will train their own models — “at least the players that have gotten enough scale and velocity to have enough data.” According to Jonathan Userovici, a general partner at VC firm Headline — whose portfolio includes AI companies like Mistral AI, but not Base44 — data is one of three key ingredients of defensibility for AI startups, alongside distribution and tech stack. The upshot is that players with strong brands are now leaning into their data and infrastructure to increase their defensibility, and Base44 fits that pattern. The company says the first iteration of its LLM, Base1, was developed and trained on a dataset generated from “tens of millions of real user interactions on the platform.” This dataset will keep on growing with the company; but so will its rivals’. The bigger competition may not be vibe-coding startups at all but instead come from frontier AI labs that are getting closer to Base44’s home turf — Cursor and Grok’s parent company xAI now bothbelong to SpaceX, and Claude Code has become a vibe coding player in its own right. This gives Anthropic and other foundational AI providers access to data and feedback loops they can use to improve models for app creation, but Shlomo thinks specialization gives Base44 a leg up. “Models are progressing, but they’ll stay very general in what they can do,” he predicted. Userovici, for his part, cautioned against underestimating frontier models, citing the example of the legal tech startup Harvey, which abandoned plans to train its own model. He doesn’t expect applied AI companies to become frontier labs en masse but frames Base44’s move in a broader context — one in which inference costs have become a meaningful part of the equation. That cost pressure, Userovici says, has driven change that enterprise customers are now demanding. “They don’t necessarily see a [return on investment] when using the latest models for all use cases, so an entire infrastructure is being set up to do orchestration and optimization to select the right models for them so that costs don’t skyrocket while maintaining the same or similar performance across the majority of use cases.” Enterprise companies still are a minority among the audience of the vibe coding platforms, but they represent a growing share of platform revenue, and users of all sizes are starting to express concerns over the cost of using AI. Base44’s decision to develop its own LLM stemmed from multiple factors, but cost reduction is likely among the benefits. “We want to get a model that is going to be more aligned to what we think is the right thing, is going to be more optimized to what we see users like in terms of the results we’re getting, and is going to be faster and cheaper for customers eventually than using the frontier models like Opus,” Shlomo said. As for Base44 itself, cost reduction isn’t as clear cut. In a press release, the company explained that “ownership of the model gives Base44 direct control over compute and inference spend, expected to result in a structurally stronger margin profile over time.” Even with a delayed payoff, improved margins would be good news for Base44’s parent company, which recently announced it wouldlay off 20% of its workforce. In contrast, Base44 has been growing in headcount since the acquisition — and announced it hadpassed $100 million in annual recurring revenuea few months ago. That’s still less than Lovable, which said ithit $500 million in ARR earlier this month. But Shlomo is betting that the “huge engineering effort” to develop Base1 will cement Base44’s positioning as the “only vertically integrated vibe-coding application — meaning, in Userovici’s terms, a player that owns its distribution, data, and infrastructure all at once.
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Gemini’s personalized AI image generation is now free for US users
Googleannouncedon Monday that the Gemini app is now offering its personalized Nano Banana-powered image generation feature to a broader audience. Starting today, all eligible users in the U.S. can access the feature for free, a service that waspreviously only availableto Plus, Pro, and Ultra subscribers. Google initially announced that Gemini’s Personal Intelligence feature would get Nano Banana-powered image generation back in April, allowing users to create images that reflect their unique interests. This means that images can be generated based on Gemini’s understanding of your likes and preferences without you having to specify them in your prompt. Gemini utilizes data from your Google account connections — such as Gmail, Google Photos, YouTube, and Search — to achieve this. For example, instead of saying, “Create an illustration of me and my favorite things, such as coffee and baking,” you can simply request, “Create an illustration of me and my favorite things.” Gemini can also pull actual images of you from Google Photos, so you don’t need to manually upload photos. Google initially rolled out the Personal Intelligence feature earlier this year, making itwidely availableto all U.S. users in March. The company recentlyexpandedthis functionality to users in India and Japan. Personal Intelligence is an opt-in feature, allowing you to decide which apps Gemini can access. Once enabled, it is set as the default for every prompt, but you can disable it using a new toggle in the Tools menu. Additionally, last month, Googleannouncedseveral upcoming updates for the Gemini app, including a new “Daily Brief” feature, a revamped interface, access to AI video model Gemini Omni, and a personal AI agent named Gemini Spark. Notably, Google’s AI chatbot Gemini surpassed 750 million monthly active users (MAUs) earlier this year, reinforcing its position as a major player in the AI space.
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TIDAL cracks down on AI music by cutting off monetization
Music streaming serviceTIDALis the latest to take aim at AI-generated music with the introduction ofa new policythat will prevent fully AI-generated music from making money on its platform. In addition, TIDAL will use automated tools to remove AI-generated music that attempts to impersonate an artist or a group, the company said. “We are committed to protecting and rewarding organic creativity to avoid compromising an artist’s ability to connect with and build their fandom from TIDAL subscribers. Many have told us they do not want to be exposed to — or prompted to listen to — wholly AI-generated music,” wroteTonyGervino, TIDAL EVP and editor-in-chief, in anannouncement. He clarified that TIDAL’s new policy was not meant to “bash technological advancement,” but rather focuses on protecting and rewarding “organic creativity” from artists. With the changes, fully AI-generated music on TIDAL will be identified and tagged as such, allowing listeners to see an “AI” badge next to any tracks deemed to be 100% AI. These tunes will not be able to be monetized or collect royalties, and will not be eligible for direct-to-fan sales, the company noted. TIDAL’s policy joins others in the streaming music space, where services like Spotify, Apple Music, Deezer, andQobuzhave developed their own policies to address the growing number of AI-generated tracks filling their services. Spotify last yearrevamped its policies to label AI musicand better filter spam, while still acknowledging that AI tools would be used in the music-creation process to varying degrees. Apple Music alsotook the tagging approach. Deezer, which said that44% of all new musicuploaded to its platform daily is AI-generated, has taken a tougher position. It actively removes AI tracks from recommendations and excludes them from editorial playlists. It alsooffers its AI-detection technology to rivalsandprovides a consumer-facing toolthat lets you see if AI music has slipped into your playlists on competing services. TIDAL’s policy could be an interesting test to see if demonetization could be the thing to slow the deluge of AI music, which many listeners aren’t interested in. “Regardless of what you are reading elsewhere, AI’s takeover of the music industry (and your recommendations) isn’t inevitable if we take even greater steps now to monitor and control it,” noted Gervino. The company said the new policy is a “living document,” meaning it’s open to changes as the space evolves. It goes into effect on July 15, 2026.
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