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.
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.”
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.
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.
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.
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.
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.
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.
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.
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.
“The future of AI should be accessible, available, and open to people and builders everywhere, and it should not require an absurd amount of resources only available to a handful of cloud providers,” Paolo Ardoino, CEO, Tether.
About 700 million people use generative AIs like Gemini and ChatGPT weekly, but adoption is far from uniform. McKinsey’s 2025 State of AI survey found that nearly half of respondents from companies with more than $5 billion in revenue have reached the AI scaling phase, compared with just 29 percent of those from companies with less than $100 million in revenue, a gap that only widens further down the chain, locking out smaller businesses, developers, and everyday users.
Retail and small businesses are limited to basic AI utilities that their facilities can power, such as text-based inference and multimedia generation, using base models. That is billions of end users, and developers locked out of full utilization and development of intelligent software due to high infrastructure demands.
Tether’s edge-first LoRA fine-tuning framework for Microsoft’s Bitnet LLM is an important step towards developing an infrastructure system that supports billions of AI agents and intelligent machines. By reducing the computational overhead of machine learning and enabling consumer-grade devices to perform advanced operations, Tether’s edge-first approach ensures greater leverage for the larger population.
Imagine a 13-billion-parameter model being fine-tuned on everyday handheld devices like Samsung S25 and iPhone 16, as well as on regular personal computers. The breakthrough combines resource-efficiency and platform-agnostic techniques to develop a fine-tuning framework for the ternary-quantized LLM.
Bitnet LLM was born out of the vision of an intelligent AI model that doesn’t consume outrageous computing resources even at full precision. Earlier attempts at resource-efficient AI relied on trade-offs, such as running small-parameter models at higher precision or larger-parameter models at lower precision, but neither approach fully solved the problem.
Bitnet takes a more fundamental approach. The result is a model that achieves linear efficiency while consuming only a fraction of the computing resources traditionally required.
The challenge, however, is that contemporary GPUs are optimized for the very floating-point operations Bitnet eliminates, creating a hardware compatibility gap. Compounding this, Bitnet was originally confined to its own Bitnet.cpp inference engine, limiting its broader utility. Tether’s breakthrough addresses both constraints at once by integrating a Vulkan and Metal GPU backend that unlocks true cross-platform capabilities for BitNet inference and LoRA fine-tuning on heterogeneous consumer GPUs, including mobile GPUs. Bitnet can now run on more mature, widely supported inference engines without sacrificing its efficiency advantages.
Vulkan’s cross-platform nature is key here. Unlike CUDA, which ties developers to NVIDIA hardware, Vulkan runs across a broad range of GPUs and operating systems, opening Bitnet to genuinely multi-platform deployment. Tether’s Bitnet fine-tuning framework implements a dynamic tiling technique to mitigate limitations in Vulkan driver buffer allocation on mobile GPUs.
The dynamic tiling algorithm technique was first applied in the fine-tuning framework for QVAC Fabric LLM, the AI model that powers Tether’s QVAC Workbench application.
This implementation demonstrates the efficiency of this approach: fine-tuning a 13-billion-parameter model across a range of consumer devices with varying GPU configurations.
The Bitnet LLM Fine-tuning framework is Tether’s latest achievement and part of a broader expansion into open-source AI and communication technologies that challenge current, slow, fragile, and controlled systems. These developments are open-sourced and packaged as modules in the QVAC SDK for easy deployment and to help developers build edge-first AI applications without needing anyone’s permission.
Tether envisions superintelligence as a foundational element possessed by its owner and is enforcing this through:
Synonymous with decentralized AI, “Local-first” AI aims to create sovereign AI solutions that do not rely on centralized infrastructure, such as data centers, to operate. They are considered cost-effective, relatively more sustainable, and unarguably more private than centralized AI. Tether is building AI applications that rely entirely on the device’s resources. These applications store data in device memory and use its processors for advanced operations, such as fine-tuning and inference.
Tether’s AI applications are built on the Pear runtime. Pear is a tooling platform for fully P2P applications that can operate without servers. Pear leverages the Holepunch tech stack. Holepunch is purpose-built for stable, direct communication between devices. Pear enables delegated inference for AI applications such as QVAC Workbench. Delegated inference enables a unified, dynamic workstation architecture where compute tasks are fluidly distributed between mobile and desktop environments, allowing either device to offload high-intensity processing to the most capable system. That is, you can start a task on your mobile device and delegate it to your desktop or laptop for completion.
The only way to scale intelligence to the needs of a ten-billion-strong society is to push it to the edge. This, in turn, depends on the progress made by experiments aimed at cost-effectively localizing AI computation.
Billions of AI agents and countless AI applications deployed by developers in every region of the world, running effectively on user-owned resources, is the only way we can democratize superintelligence and avoid creating another ‘luxury’ cutting-edge technology controlled by unicorns and fully accessible only to elites.
Tether is pioneering limitless superintelligence for an ever-growing society and applications. Follow the journey to truly local and edge-first AI solutions
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