AI News Today — Part 6
Jun 22, 2026 · Sourced from 500+ daily AI sources, scored by relevance.
- Is Mistral late or savvy?
Is Mistral late or savvy? InfoWorld
- The unlikely role of Operation Epic Fury in a Mississippi AI data center lawsuit
The lawsuit argues that the data centers are operating illegally and polluting surrounding areas, but the DOJ said they’re needed for national security.
- Sam Altman thinks AI will surpass human intelligence by 2030. His rival AI billionaires say it’ll be even sooner
Sam Altman thinks AI will surpass human intelligence by 2030. His rival AI billionaires say it’ll be even sooner Fortune
Score: 55🌐 MovesJun 22, 2026https://fortune.com/article/sam-altman-ai-superintelligence-stargate-chatgpt-human-intelligence-2030/ - FIFA's Best-Kept Secret: The AI Command Center Powering the 2026 World Cup
FIFA's Best-Kept Secret: The AI Command Center Powering the 2026 World Cup PCMag
Score: 55🌐 MovesJun 22, 2026https://www.pcmag.com/news/fifas-best-kept-secret-the-ai-command-center-powering-the-2026-world-cup - NetworkNews Audio Announces Audio Press Release (APR) Discussing AI Infrastructure Demand Creating Urgent Need for Scalable, Intelligent Automation
NetworkNews Audio Announces Audio Press Release (APR) Discussing AI Infrastructure Demand Creating Urgent Need for Scalable, Intelligent Automation Toronto Star
- Chevron signs power supply deal with Microsoft for Texas data center
Chevron signs power supply deal with Microsoft for Texas data center Reuters
- The Canary In The CDP Mine: Databricks CustomerLake Is The Litmus Test For Agentic Marketing
Databricks announced CustomerLake, a new customer data platform (CDP) offering, at its Data + AI Summit last week. Though widely anticipated, the announcement generated quite a bit of unabashed excitement and clever commentary. The enthusiasm is relatable; it’s not every day the martech industry gets a new entrant. A CDP Announcement That Isn’t Really About […]
- PP-OCRv6 on Hugging Face: 50-Language OCR from 1.5M to 34.5M Parameters
PP-OCRv6 on Hugging Face: 50-Language OCR from 1.5M to 34.5M Parameters
- Elon Musk says AI will probably become smarter than all humans in 4-5 years
Elon Musk says AI will probably become smarter than all humans in 4-5 years YourStory.com
- Bain tests software takeover targets by vibecoding AI replicas
Private equity groups swiftly recreate software products to gauge their competitive advantages
- Chevron moves into power production with Microsoft AI deal
Company signs 20-year agreement to develop data centre in heart of US oil country that could include gas-fired plant
- Andrew Yang: "I cannot believe how pathetic our leadership has been on AI"
Andrew Yang: "I cannot believe how pathetic our leadership has been on AI" Fortune
- AI is Rewriting Rules of MarTech and Solving Core Problems
AI is Rewriting Rules of MarTech and Solving Core Problems india.entrepreneur.com
Score: 55🌐 MovesJun 22, 2026https://india.entrepreneur.com/technology/ai-is-rewriting-rules-of-martech-and-solving-core-problems - Orchestration models 🤖, DeepMind exodus 👋, loop engineering 🔄
Orchestration models 🤖, DeepMind exodus 👋, loop engineering 🔄
- Enable Real-Time AI for High-Speed Data Acquisition with DAQIRI
When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971...
Score: 54🌐 MovesJun 22, 2026https://developer.nvidia.com/blog/enable-real-time-ai-for-high-speed-data-acquisition-with-daqiri/ - Snowflake Postgres Powers Low-Latency ML Feature Serving
Snowflake's ML team chose Snowflake Postgres to power their Online Feature Store — demonstrating 2.5x lower latency and 7x higher QPS than Databricks Lakebase in production benchmarks.
Score: 53🌐 MovesJun 22, 2026https://www.snowflake.com/content/snowflake-site/global/en/blog/snowflake-postgres-ml-online-feature-store - Optro Accelerates Global Growth with Singapore Hub, Delivering Agentic GRC to APAC Enterprises
Optro Accelerates Global Growth with Singapore Hub, Delivering Agentic GRC to APAC Enterprises The Straits Times
- The next evolution of Enterprise AI: From governance frameworks to runtime accountability
By Prem Brahmandam, The Hartford India Leadership Team Artificial intelligence has moved past the stage of isolated pilots. In large enterprises, it is now becoming part of the operating core. […] The post The next evolution of Enterprise AI: From governance frameworks to runtime accountability appeared first on Express Computer .
- AI’s next compute layer is likely to come from outside Silicon Valley
For years, the assumption around AI infrastructure was easy to accept. Serious compute would be built where hyperscale cloud , developer density, and capital were already concentrated, namely California, Seattle, London, and a small circle of established technology hubs. There was a practical reason for that geography. Training and deploying AI at scale requires datacentres, compute, networking capacity, energy, and advanced infrastructure to work together. OECD analysis notes that this has pushed AI firms toward services operated by the largest cloud computing suppliers . Over time, that dependence hardened into market concentration . In the third quarter of 2025, Synergy Research Group put Amazon, Microsoft, and Google's combined share of global enterprise cloud infrastructure spending at 63%. That logic now looks less durable. Compute is becoming more expensive, more power-intensive, and harder to access outside a small group of dominant providers . Builders are starting to confront questions that hyperscale cloud mostly let them ignore. Where will the power come from? Can chips be shipped to this jurisdiction? Whose laws apply to the data once it moves? Those questions are getting answered in different places now, and most of them are not in Silicon Valley. What scarcity teaches In established cloud markets, the default answer to rising AI demand is to add more capacity through larger cloud contracts, denser datacentre buildout, and deeper dependence on the same centralised stack. That answer is becoming harder to scale. Datacentres consumed about 1.5% of the world's electricity in 2024, which was enough to make energy one of the pressure points in AI infrastructure. The International Energy Agency expects that share to rise to just under 3% by 2030, making compute harder to treat as a hidden layer behind AI products. In much of the developing world, that pressure was already the starting point. Builders there have rarely had the option of treating compute access, power, and distribution as someone else's problem. They have had to design for it. The result is a quieter pattern that does not get much attention in Silicon Valley coverage. Namely that serious AI infrastructure is now being built in places where scarcity is treated as a design problem rather than an afterthought. What this looks like in practice The pattern is most visible across four regions. In India, Yotta Data Services runs Shakti Cloud on more than 16,000 NvidiaH100 GPUs and is on track to roughly double that by the end of 2025. Over half of the compute behind the IndiaAI Mission – the government's push to build indigenous foundation models – sits on Yotta's hardware. In February 2026, the national multilingual platform Bhashini moved off foreign hyperscalers and onto Shakti Cloud, picking up roughly 40% in performance along the way. Bhashini runs real-time translation across 11 Indian languages at population scale and the people running it had decided that infrastructure they could not govern was the wrong place to put it. Across Africa, Cassava Technologies, founded by Zimbabwean entrepreneur Strive Masiyiwa, is deploying 12,000 Nvidia GPUs across datacentres in South Africa, Egypt, Kenya, Morocco, and Nigeria. Cassava is the first Nvidia Cloud Partner on the continent. Before this buildout, Nvidia estimated that roughly 80 of its GPUs were installed across the entire African continent. The constraint was not only compute pricing; it was the basic absence of advanced silicon. Cassava's response is a pan-African network running on its own fibre backbone, designed so that African startups, researchers, and governments do not have to route through Europe or the United States to train and deploy AI. In Brazil, the government's SoberanIA project reserves 500MW for a sovereign AI factory in Piauí, powered entirely by renewable energy, with Scala datacentres as lead infrastructure partner. Meanwhile, Brazil has committed to attracting up to $370 billion in datacentre investment over the next decade, tied to the REDATA program's tax incentives for projects sourcing 100% renewable power. Roughly 65% of Brazilian data is still stored abroad. The wager is that abundant hydroelectric and solar power gives Brazil the kind of compute the US and China have to work harder to build – clean by default, cheap by geography. The United Arab Emirates is taking the most expensive route. Core42, part of the G42 group, sells inference capacity on a mix of Nvidia and Qualcomm chips out of Abu Dhabi, and the country has committed jointly with the United States to a 10-square-mile, 5GW AI campus that should be partially operational by the end of the decade. The Emirati pitch is straightforward. Countries that want sovereign AI but cannot build the underlying stack themselves can rent one from a friendly government. The Middle East Institute describes it as a deliberate strategy of vertical integration – owning the chips, the power, the datacentres, and the foreign relationships in one piece. These projects do not share a politics or an ownership model. What they share is a starting assumption that compute access, power, land, and chip supply are first-order design problems rather than externalities. That assumption produces different infrastructure. Why inference changes the map Training large models still rewards dense clusters, large capital budgets, and access to advanced chips. That work is unlikely to leave the largest hyperscale facilities soon. Inference is a different problem. Models are used continuously, by customers, devices, agents, and enterprise systems. McKinsey expects inference to overtake training in AI datacentres by 2030, to account for more than half of AI compute and roughly 30% to 40% of total datacentre demand. Inference asks different questions than training does. Rather than where the largest cluster can be built, the questions become where compute should sit, how fast it can respond, how reliably workloads can be routed, and whose laws govern the data while it does so. Those questions have geographic answers that hyperscale concentration does not handle well, especially for the billions of people who do not live within easy latency of a US or European datacentre. The compute fabric that inference demand requires is broader than hyperscale cloud alone can provide. Distributed GPU capacity, regional inference clusters, sovereign clouds, and emerging neoclouds in places such as Mumbai, Nairobi, São Paulo, and Abu Dhabi are not substitutes for hyperscale. They are the layer hyperscale cannot serve on its own. What this means for the map The old map of AI infrastructure was drawn around places where cloud capacity was already concentrated. That map made sense when compute was treated as cheap and abundant. The next map will look different. It will be drawn around places that learned to build when compute was costly and strategic, and where the question of who controls the stack was never theoretical. The companies and governments doing that work are not catching up with Silicon Valley. They arrived at the problem first, because they had to. Ilman Shazhaev is founder and CEO of Dizzaract, an AI infrastructure company headquartered in Abu Dhabi. He serves as a UN/UNODC expert panel member advising on AI applications in developing economies and has authored 46 scientific articles and 10 registered invention patents. Read more about datacentre pipeline Data dive: A new American Century in the datacentre pipeline? Looking at datacentre development internationally, we see how the UK faces apparent relative decline, how countries are responding to the AI age, and what MW vs GDP can tell us Hit the north! UK datacentre focus shifts to M62 and points north . Barbour ABI data shows 8GW of total datacentre pipeline with most big projects in the north and Scotland, while London and the M4 corridor are about 25% of projected capacity
Score: 52🌐 MovesJun 22, 2026https://www.computerweekly.com/opinion/AIs-next-compute-layer-is-likely-to-come-from-outside-Silicon-Valley - Every Developer Thinks AI Makes Them 20% Faster. Measured, They’re 19% Slower.
Why senior engineers are the slowest with AI, and the team pays twice. Continue reading on Towards AI »
- As AI adoption accelerates, new SRI report examines what makes AI trustworthy
As AI adoption accelerates, new SRI report examines what makes AI trustworthy EurekAlert!
- What are your organization’s AI ethical nightmares?
Instead of discussing values and policy for AI, Reid Blackman writes that companies need to focus on worst-case scenarios, the things they absolutely do not want to happen
- China lets AI do your shopping. SEA ain’t sold
Southeast Asia's ecommerce players are betting that AI agents will stay behind the scenes for now. The reason comes down to trust.
- Combining AI and physics to sharpen measurements of dark matter
Combining AI and physics to sharpen measurements of dark matter Cambridge | Faculty of Mathematics
Score: 51🌐 MovesJun 22, 2026https://www.maths.cam.ac.uk/features/combining-ai-and-physics-sharpen-measurements-dark-matter - 10 things migration policymakers need to know about AI
How to ensure using AI in migration makes its governance more inclusive, rights-based and effective, rather than more unequal, opaque and exclusionary.
Score: 50🌐 MovesJun 22, 2026https://www.weforum.org/stories/2026/06/migration-policy-need-to-know-ai/ - pgEdge Announces ColdFront for PostgreSQL, Seamlessly Uniting AI, Analytical and OLTP Workloads
ALEXANDRIA, Va. — pgEdge, the leading open source enterprise Postgres company, has announced pgEdge ColdFront, a transparent data tiering solution for PostgreSQL. Unlike other alternatives, ColdFront’s cold tier is fully writable: UPDATE and DELETE work on archived rows through the same SQL the application already uses, with no code changes and no rehydration required. Older … continue reading The post pgEdge Announces ColdFront for PostgreSQL, Seamlessly Uniting AI, Analytical and OLTP Workloads appeared first on SD Times .
- Why AI Agents Need to be Held to a Code of Conduct
Companies set expectations for their employees. It’s time to do the same for AI agents.
Score: 50🌐 MovesJun 22, 2026https://www.inc.com/bob-lemmond/why-ai-agents-need-to-be-held-to-a-code-of-conduct/91363070 - The Bear Case for AI Data Centers
The more I dig into the economics, the harder it is to see AI data centers as a good business, and they’re now my leading candidate for what pops the AI bubble in the next 6 to 12 months. The concern isn’t that AI stops improving or that demand vanishes. It’s that spending has raced Continue reading "The Bear Case for AI Data Centers" The post The Bear Case for AI Data Centers appeared first on Gradient Flow .
- Japan's JERA to build large gas-fired plant for US data center for $3bn
Japan's JERA to build large gas-fired plant for US data center for $3bn Nikkei Asia
Score: 50🌐 MovesJun 22, 2026https://asia.nikkei.com/business/energy/japan-s-jera-to-build-large-gas-fired-plant-for-us-data-center-for-3bn - The AI divide in the Philippines began long before AI
Picture a ten-year-old in a public elementary school somewhere in the Philippines. Her classroom holds more children than it was built for. She has sat through four years of lessons. And if she is typical of her cohort, she cannot read a short, age-appropriate paragraph and tell you what it means. That is not a […] The post The AI divide in the Philippines began long before AI appeared first on e27 .
Score: 50🌐 MovesJun 22, 2026https://e27.co/the-ai-divide-in-the-philippines-began-long-before-ai-20260615/ - Why open infrastructure will define the AI era
Why open infrastructure will define the AI era InfoWorld
Score: 50🌐 MovesJun 22, 2026https://www.infoworld.com/article/4186382/why-open-infrastructure-will-define-the-ai-era.html - Oxford’s top maths professor: ‘The devil could use AI to destroy the world’
Oxford’s top maths professor: ‘The devil could use AI to destroy the world’ The Telegraph
Score: 50🌐 MovesJun 22, 2026https://www.telegraph.co.uk/books/non-fiction/interview-oxford-professor-john-lennox/ - Why Canva Doesn’t See ChatGPT and Claude As a Threat
Why Canva Doesn’t See ChatGPT and Claude As a Threat The Information
Score: 50🌐 MovesJun 22, 2026https://www.theinformation.com/newsletters/ai-agenda/canva-see-chatgpt-claude-threat - Drowning in AI: Companies are launching hundreds of projects, and that’s a problem
Drowning in AI: Companies are launching hundreds of projects, and that’s a problem Fortune
- CFOs Are Coming For The Enterprise AI Budget
Enterprise AI vendors know the next sale will be won not only on model quality and capability, but also on control and cost.
Score: 50🌐 MovesJun 22, 2026https://www.forbes.com/sites/ronschmelzer/2026/06/22/cfos-are-coming-for-the-enterprise-ai-budget/ - Why Are AI Bills Exploding? What CEOs And CIOs Should Know
Why AI bills are exploding, and what CEOs and CIOs can do about it. The good, bad, and ugly of AI overuse, and steps to tie AI use to ROI and business outcome metrics.
Score: 50🌐 MovesJun 22, 2026https://www.forbes.com/sites/nishatalagala/2026/06/22/why-are-ai-bills-exploding-what-ceos-and-cios-should-know/ - Interactions API: our primary interface for Gemini models and agents
Interactions API
Score: 50🌐 MovesJun 22, 2026https://blog.google/innovation-and-ai/technology/developers-tools/interactions-api-general-availability/ - The AI revolution comes with a hidden tax
It was the best of trends, it was the worst of trends. It was the epoch of artificial intelligence, it was the epoch of artificial inflation. I’m truly excited to be alive at a time when AI, formerly the stuff of science fiction, is now an everyday reality and promising so many benefits to humankind. AI is accelerating drug discovery, slashing the cost of protein folding research, diagnosing cancers earlier than human radiologists can, automating the drudgery out of nearly every white-collar job, translating speech across hundreds of languages in real time, and giving the blind a way to see the world through a camera. AI is giving us all this and so much more. But no amount of techno-optimism can hide the fact that AI is making just about everything more expensive. While the AI trend is making a tiny number of rich people even richer, the public at large is paying the price through rapidly rising prices; it represents a transfer of wealth from the have-nots to the haves. AI is a machine that eats resources. It eats chips. It eats electricity. It eats water, land, labor, and building materials. AI’s gluttony creates scarcity, and scarcity creates inflation. Here are all the ways AI is driving up costs for you and me. Your gadgets cost more AI runs on memory chips and other computing hardware. AI companies buy so many of them that they’ve created a shortage. NAND prices shot up around 246% from the start of 2025 through last December, according to Kingston. Hard disk drive prices in Europe rose 46% in just four months. The chip shortage pushed smartphone prices to all-time highs — one analyst called it a “tsunami-like shock” to the industry. Beyond that, computer and device prices will climb by 20% by the end of 2026 , according to one estimate. Apple CEO Tim Cook told the Wall Street Journal this week that price increases on Apple products are “unavoidable,” citing the surging costs of memory and storage chips driven by AI data-center demand. He described the supply shock as a “hundred-year flood.” Software costs more Greedflation has hit the software industry. Salesforce, ServiceNow, and others have increased subscription prices, blaming AI for the hikes. IT spending globally is forecast to grow 13.5% in 2026 over the previous year, reaching $6.31 trillion, according to Gartner, with the bulk of the increase driven by AI infrastructure investments. The pricing models themselves are getting more complex and more expensive: seat licenses plus API usage plus GPU compute plus data storage plus compliance layers. What used to be one subscription is now five line items. Enterprise software spending is growing 13.3% , with much of it being price increases on existing contracts rather than new purchases. Services cost more The services you buy cost more because companies are burning money on AI and passing on the costs to you. While tokens are getting cheaper, the new reasoning models can use anywhere from several times to tens of times more tokens than traditional models for comparable tasks. SaaS inflation now runs at 13.2%, which is nearly five times the consumer inflation rate, and a majority of that increase is due to AI costs. Goldman Sachs forecasts that agentic AI could drive a 24-fold increase in token consumption by 2030, and that applies to the companies that provide enterprise services. Electricity costs more AI data centers draw power the way a city does, pushing US residential electricity prices up roughly 5% on an annual average basis in 2025. That’s nearly double the general inflation rate of 2.7%. Wholesale electricity prices near data center clusters have more than doubled since 2020. Goldman Sachs says electricity inflation will hover around 6% through 2027. The utilities build new power plants and transmission lines to serve these data centers, then hand the bill to everyone on the grid. You pay for AI’s appetite, whether you use AI or not. Your heating bill goes up, too, because the same natural gas that warms your home is being burned to generate power for data centers. Your car costs more Modern cars are computers on wheels. And a new automotive chip shortage is now underway . Prices for the memory chips that go into cars are expected to rise 70% to 100% in 2026 adding as much as $400 to the price of a car. Your house costs more Data centers need land near power lines and water. They buy it at record prices, and they outbid the people who would have built homes on it. In Texas, data centers compete directly with homebuilders for utility-ready lots. In Northern Virginia, the data center buildout is squeezing the housing supply in a market that is already short. In Columbus, Reno, and Salt Lake City, data center land deals are pushing up land values beyond anything those markets have ever seen for industrial property. And the houses that do get built cost more, because data center construction has driven up wages for workers by 25% to 30%. Data centers even compete with public infrastructure projects for the same crews and the same concrete, copper, and steel — driving up the cost of roads and public works. Everything costs more AI is helping all kinds of companies fleece customers. Research from Carnegie Mellon in 2025 found that AI-driven ranking and pricing systems raise prices. AI-powered pricing algorithms now set the price of your rideshare, your flight, your hotel room, even your concert ticket. They use AI to estimate the maximum amount you’re willing to pay, then charge you that amount. Called surge pricing or dynamic pricing, the bottom line is that it affects your bottom line. A 2020 study in the American Economic Review showed that AI algorithms create a poverty premium: They learn that people with fewer alternatives are less sensitive to price, so they charge poor people more. Here’s another weird phenomenon hardly anyone talks about: When competing companies all use similar AI pricing systems, they can arrive at higher prices together without ever talking to each other. It adds up to a kind of accidental price-fixing. Food costs more Higher electricity prices flow through the entire economy. Farms, food processors, trucking companies, and stores all pay more for power because of AI consumption, and they pass those costs down the chain until they’re ultimately paid by food consumers. Data centers are also consuming land that was once used for farming, forcing some farms to locate further away from population centers. Taxes cost more Data centers receive enormous tax incentives and subsidies from state and local governments . At least 38 states now offer such incentives to data centers, which means funding shortfalls have to be made up by families paying their taxes. Texas is projected to lose $3.3 billion by 2029. Meta’s got a 20-year sales tax exemption from the state of Louisiana on data center equipment worth an estimated $3.3 billion. Pennsylvania will probably give around $2 billion in data center tax breaks. AI may one day change the world. But for now, it’s mainly just changing the cost of living. AI disclosures : I don’t use AI for writing. The words you see here are mine. I used a few AI tools via Kagi Assistant (disclosure: my son works at Kagi) as well as both Kagi Search and Google Search as one part of my fact-checking for this column. I used a word processing product called Lex, which has AI tools, and after writing the column, I used Lex’s grammar checking tools to hunt for typos and errors and suggest word changes. Why I disclose my AI use and encourage you to do the same.
Score: 49🌐 MovesJun 22, 2026https://www.computerworld.com/article/4186923/the-ai-revolution-comes-with-a-hidden-tax.html - A 40% market crash is lurking in the IPO pipeline. SpaceX and OpenAI could trigger it.
Prior records for U.S. equity issuance came in 1929 and 2000 — and we all know what happened next.
- 👨🏿🚀TechCabal Daily – MTN’s AI cables
In today's edition: The DRC is building a stock exchange || WapiPay is in Canada || Zimbabwe eases crypto restrictions || MTN is turning its cables into ears
- NotebookLM is transforming student success at FSU
Google NotebookLM at Florida State University: Transforming Student Success with AI YouTube video
Score: 49🌐 MovesJun 22, 2026https://blog.google/products-and-platforms/products/education/florida-state-university-notebooklm/ - Tata Motors uses AI to cut defects and predict what customers want
Tata Motors uses AI to cut defects and predict what customers want YourStory.com
Score: 48🌐 MovesJun 22, 2026https://yourstory.com/ai-story/tata-motors-ai-manufacturing-defects-demand-forecasting - Heidrick & Struggles Strengthens Board with Appointment of AI and Talent Leaders Aseem Datar and Leanne Wood
Heidrick & Struggles Strengthens Board with Appointment of AI and Talent Leaders Aseem Datar and Leanne Wood The Straits Times
- AI was supposed to replace executive assistants. It promoted them instead
AI was supposed to replace executive assistants. It promoted them instead Fortune
Score: 48🌐 MovesJun 22, 2026https://fortune.com/2026/06/22/executive-assistant-ai-era-more-responsibilities-proxy-human/ - Generation Z Aren’t Sold On AI, And It’s Limiting Enterprise Adoption
Many generation Z graduates and workers aren't sold on AI, which presents significant problems for enterprise leaders.
- How AI Is Reshaping Retail Discounts
Upside Co-Founder and CEO Alex Kinnier joins Bloomberg Open Interest to explain why American consumers are feeling squeezed despite falling gas prices, reveals what transaction data says about spending habits. He also discusses how AI is transforming retail with hyper-personalized offers that save consumers money while helping retailers grow profits. (Source: Bloomberg)
Score: 48🌐 MovesJun 22, 2026https://www.bloomberg.com/news/videos/2026-06-22/how-ai-is-reshaping-retail-discounts-video - The company behind the Ninja Creami paused work to figure out how AI could reshape its business
The company behind the Ninja Creami paused work to figure out how AI could reshape its business Business Insider
Score: 48🌐 MovesJun 22, 2026https://www.businessinsider.com/sharkninjas-big-bet-4-day-ai-hackathon-2026-6 - OpenAI alumnus Shyamal Anadkat returns to India, teases AI venture
Calling the current AI wave a "once in a generation opportunity", former OpenAI executive Shyamal Hitesh Anadkat has returned to India, and hinted that he plans to start a new AI venture.
- Too good to be true? Avoid free AI token offers — or risk vendor lock-in
Tech industry experts are urging IT decision-makers to be wary of AI vendor gimmicks such as free tokens, and to adopt a multi-vendor and multi-model strategy to avoid vendor lock-in. “Don’t be afraid to adopt a multi-vendor approach to get value from different AI tools rather than risk lock-in with a single one,” said Max Goss , senior director analyst at Gartner. It is unlikely one AI vendor or model will meet an organization’s requirements, Goss said. The advice comes as more AI vendors are offering cheap tokens subsidized by venture capital in a land grab for customers. The companies are also hiring forward-deployed engineers (FDEs) to push their models to enterprises. Once companies start developing business processes around specific AI models, they get locked into their ecosystem. “People are adopting hybrid strategies…to cut token costs, and adopting more token-efficient models,” said Jack Gold , principal analyst at J. Gold Associates. Free and low-cost tokens from AI vendors could incentivize companies to build processes and workflows around proprietary LLMs and agents, said Max Leaming , head of data science and AI solutions at ManpowerGroup. But as the AI landscape evolves, it’s difficult to predict whether a multi-model or multi-vendor landscape will emerge, said Logan Wolfe , partner of global AI strategy and sovereign transformation at IT consulting firm Kyndryl. “I think it could be multi-model, yes. It really comes down to the use case and the type of implementation that you’re having,” Wolfe said. Enterprises are still in the midst of moving blue-sky experimentation to a mindset where they see AI as a powerful tool that needs to make sense from a business perspective. With that in mind, IT leaders should ground their AI strategies on use cases as opposed to vendors, Wolfe said. “If it’s a highly regulated space, if it’s a financial sector, a healthcare sector, then you will be placing a lot more emphasis on safety, privacy, maintaining certain regulations, and so that could prevent you from rapid model switching based on cost,” Wolfe said. For low-stakes use cases, it would be prudent to have a model-switching approach that doesn’t break the bank. “For a low-hanging fruit use case with varying volume, like a customer support data center, during heavy load times you could switch to the more capable model, then optimize that on evenings and weekends,” Wolfe said. ServiceNow Chief Digital Information Officer Kellie Romack , who’s worked in IT for 25 years, said companies need to understand how their AI is built. “You can’t have AI built in such a way that you don’t have human beings understanding how it was built…, how to debug, back up, and retrace,” she said. Romack has also long resisted ripping out one vendor’s platform to replace it with another. “I say, ‘Let’s talk about the technology you already have…, now let’s see the best of breed,’” she said. After studying what customers already own and where their contracts and plans are headed, Romack looks at options based on architectural principles and the problem being solved, then runs multiple models in-house, such as Anthropic’s Claude and Microsoft’s Copilot, through one LLM gateway. “We have a lot of different things in-house that people can put their fingers on,” Romack said. For example, Claude might be better for reading a long Word document, while Copilot might be better for a quick summary. She is sensitive about internal AI spending. “Every day we look at token spend. I’ll look at an engineer that’s got the same job as another engineer, and I’m like, ‘OK, you spent $10, you spent $10,000. Why?’” Avoiding vendor lock-in is important for continuity of service. Outages hit AI services from OpenAI and Claude in recent months, and a multi-model approach provides fallback options, Gartner’s Goss said. “If you are relying on a single provider with a single model, there’s risk there. You can mitigate that risk with a multi-model approach,” he said.
- Lloyds to recruit 300 AI specialists
Lloyds to recruit 300 AI specialists Computing UK
Score: 48🌐 MovesJun 22, 2026https://www.computing.co.uk/news/2026/lloyds-banking-group-recruit-300-ai-specialists