Tech Disruptions

Dead Before Lunch: Why Edge AI’s Battery Problem is the Industry’s Best-Kept Secret

April 25, 202620:08Tech Disruptions

This episode delves into the true motivations behind the tech industry's push for 'Edge AI' on personal devices, revealing that despite marketing claims of privacy and speed, it's primarily a multi-billion-dollar cost-shifting strategy. Listeners will learn how Big Tech is attempting to offload the astronomical energy and infrastructure expenses of running AI in the cloud onto consumers, whose device batteries and electricity bills will bear the brunt of these computational demands.

Key Takeaways

Detailed Report

The tech industry is aggressively pushing a vision of "Edge AI," where powerful artificial intelligence runs directly on your smartphone or laptop. While marketed with promises of enhanced privacy and low latency, a new report by Cornerstone Communications reveals a far more cynical reality: Edge AI is a multi-billion-dollar cost-shifting maneuver designed to offload expenses from tech giants to consumers.

The True Cost of Cloud AI

Today, many users interact with AI through cloud-based services like ChatGPT. However, running these large language models (LLMs) in the cloud is astronomically expensive. Financial filings indicate that major tech companies like Microsoft, Google, Amazon, and Meta spent over $380 billion on AI infrastructure in 2025 alone, a figure that more than doubled from 2022. This capital expenditure isn't just for data centers; it includes colossal numbers of Nvidia GPUs and, critically, securing the power grid infrastructure to run them.

Serving a single ChatGPT query, for instance, consumes roughly ten times the electricity of a standard Google search. This massive energy demand is pushing municipal grid limits, with some firms even investing in nuclear power plants in 2024 and 2025 to secure enough electricity. The current business model for generative AI is fundamentally unprofitable at scale if tech giants continue to bear the electricity bill for billions of user prompts.

Shifting the Burden to Your Device

Edge AI offers a solution to this problem for Big Tech: by shifting the computational burden of LLM inference from their server farms to the Neural Processing Unit (NPU) on your smartphone or PC, they offload those electricity costs directly onto the consumer. When you use cloud AI, the tech company pays for the power; with Edge AI, your device's battery bears the cost, meaning you pay when you plug it into the wall.

Projections from IDC suggest that GenAI smartphones will account for 70% of the total market by 2028, with AI PCs also seeing massive growth. The industry envisions a future where your device effectively becomes their outsourced data center.

The Physics of Battery Drain

This vision, however, collides with the cold, hard physics of mobile hardware. Real-world testing by Cornerstone Communications found that generating just 1,000 tokens locally – roughly 750 words, or a couple of email drafts – can consume up to 13% of a flagship phone’s total battery charge. This means that if a user attempts to utilize a phone’s AI assistant as marketed, the device could be dead before lunch.

Several factors contribute to this extreme drain:

  • Massive Data Transfer: Running an LLM requires constant, energy-intensive shuttling of vast amounts of data between the device's RAM and its NPU or GPU.
  • Bursty Power Spikes: Local inference creates massive, intense power spikes. While modern chips are efficient at rest, spooling up the NPU for token generation draws intense peak wattage, rapidly depleting the battery.
  • Thermal Throttling: High power draw generates rapid heat. Benchmarking data shows flagship phones losing nearly half their token-generation throughput within just two prompts due to thermal throttling. In some Android devices, the operating system may even terminate AI inference to prevent hardware damage.

This situation represents the "battery wall" for Edge AI, similar to how early mobile tech hit processing and bandwidth limitations in previous decades. Furthermore, keeping AI models loaded in memory creates a constant power draw, known as "phantom drain," preventing the phone from entering deep sleep states even when not actively used.

The Consumer Disconnect and Feature Abandonment

Despite these limitations, tech companies continue to market AI-enabled devices with impressive demonstrations, conspicuously omitting battery life estimates for heavy AI usage. This contrasts sharply with decades of consumer survey data consistently showing that battery life is the undisputed priority for smartphone users, often cited as the primary reason for upgrading.

In stark contrast, consumer enthusiasm for AI features is remarkably low, with many users finding them "not useful" and preferring fewer additions if it compromises basic functionality. This sets up a "catastrophic collision course" leading to "feature abandonment." Consumers, prioritizing battery life, will likely disable on-device AI features to extend their phone's charge, rendering the specialized NPU a "dead silicon" – a useless chunk of hardware.

The Jevons Paradox further complicates this: as AI becomes more efficient, increased usage encouraged by software will compound battery drain, negating any efficiency gains.

The Trillion-Dollar Blindspot: Battery Technology

The core problem isn't software; it's the physical limits of current battery technology. Lithium-ion batteries are approaching their theoretical energy density limits, and simply making them larger increases weight, thickness, and safety risks. The industry cannot simply "Moore's Law" its way out of chemistry.

Despite this critical bottleneck, there's a staggering misallocation of capital. While $380 billion flowed into AI infrastructure in 2025, venture capital funding for the *entire* energy storage sector was disproportionately small in comparison. Mainstream Edge AI adoption will stall unless capital pivots to next-gen battery solutions like solid-state or silicon anode technologies, which require massive investment to scale.

Environmental and Regulatory Concerns

The implications extend beyond user experience. If Edge AI forces users to charge their phones twice a day instead of once, the total lifespan of the phone's battery could be cut in half. This effectively creates planned obsolescence, potentially leading to massive consumer backlash, class-action lawsuits, and a significant spike in e-waste, posing a looming environmental crisis.

Ultimately, the AI revolution, as currently conceived for mobile devices, is running on fumes. Until the chemistry of energy storage catches up to the mathematics of neural networks, "Edge AI" remains a marketing slogan facing insurmountable physical and economic realities.

Show Notes

Works Referenced

This episode was based on a research prompt rather than a single source URL. List the most relevant resources discovered during research, starting with the most important.

Then list any other articles, papers, reports, projects, companies, tools, standards, or resources that were mentioned in the episode or discovered during research. Format each as a bullet with a bolded name followed by a short description. Where a URL is known, make the name a clickable Markdown link: Name: one-sentence description. Only include items actually discussed or directly relevant to the episode — do not pad with tangentially related links.

  • Cornerstone Communications Report: A comprehensive report detailing the financial incentives and physical limitations of Edge AI, heavily cited throughout the episode.
  • ChatGPT: An example of a cloud-based AI service that consumes significant energy at the data center level.
  • Microsoft: A major tech company investing heavily in AI infrastructure and pushing Edge AI.
  • Google: A major tech company investing heavily in AI infrastructure and pushing Edge AI.
  • Amazon: A major tech company investing heavily in AI infrastructure and pushing Edge AI.
  • Meta: A major tech company investing heavily in AI infrastructure and pushing Edge AI.
  • Nvidia GPUs: Graphics Processing Units from Nvidia, identified as a critical and costly component of AI infrastructure.
  • IDC: An industry analysis firm whose projections for GenAI smartphones and AI PCs were cited.
  • Apple: A flagship phone manufacturer whose devices and past "Batterygate" incident were referenced.
  • Samsung: A flagship phone manufacturer whose devices were referenced in the context of battery life.
  • Apple M4: A specific mobile chip mentioned for its efficiency and power draw characteristics.
  • Snapdragon 8 Gen 3: A specific mobile chip mentioned for its efficiency and power draw characteristics.

Glossary

List technical terms, acronyms, and concepts from the episode that may be unfamiliar to a general listener. Format each as a bullet: Term: concise, plain-language definition. Only include terms that actually appeared in the episode — do not add general background terms.

  • Edge AI: Artificial intelligence processing that occurs directly on a user's device (like a smartphone or laptop) rather than in the cloud.
  • Tokens: The basic units of text or code that large language models (LLMs) process and generate, roughly equivalent to words or sub-words.
  • Capital Expenditure (CapEx): Funds used by a company to acquire, upgrade, and maintain physical assets such as property, buildings, industrial plants, technology, or equipment.
  • Nvidia GPUs: Graphics Processing Units manufactured by Nvidia, widely used for their parallel processing capabilities essential for training and running AI models.
  • Generative AI (GenAI): A type of artificial intelligence that can produce various types of content, including text, images, audio, and synthetic data.
  • LLM (Large Language Model): An advanced AI model trained on vast amounts of text data, capable of understanding, generating, and summarizing human language.
  • Inference: The process of using a trained AI model to make predictions or generate new outputs based on new input data.
  • NPU (Neural Processing Unit): A specialized microprocessor designed to accelerate machine learning and artificial intelligence workloads more efficiently than general-purpose CPUs or GPUs.
  • Thermal Throttling: A mechanism where a device automatically reduces its performance (e.g., CPU or GPU clock speed) to prevent overheating and potential damage.
  • Phantom Drain: The phenomenon where a device's battery slowly loses charge even when it is not actively being used, often due to background processes or inefficient software.
  • Feature Abandonment: A situation where users disable or stop using a device's advertised features because they negatively impact core functionality, such as battery life.
  • Jevons Paradox: An economic principle stating that as technological progress increases the efficiency with which a resource is used, the rate of consumption of that resource rises due to increasing demand.
  • Energy Density: The amount of energy stored per unit of mass or volume, a key metric for battery performance.
  • Lithium-ion batteries: A type of rechargeable battery commonly used in portable electronics and electric vehicles, known for their high energy density.
  • Solid-state batteries: A next-generation battery technology that replaces the liquid electrolyte of traditional lithium-ion batteries with a solid material, potentially offering higher energy density and improved safety.
  • Silicon anode technology: An advanced battery technology that uses silicon instead of graphite in the battery's anode, allowing for significantly higher energy storage capacity.
  • E-waste: Discarded electrical or electronic devices, which can contain harmful substances and contribute to environmental pollution if not properly recycled.

Full Transcript

HostSo, imagine this: you're casually chatting with your phone's super-smart AI assistant, maybe asking it to summarize a quick email or whip up a draft for you. Harmless, right?
ExpertHarmless to your cognitive load, perhaps. But according to a new report, it could be devastating for your phone's battery. That means 13% of a flagship iPhone's charge… gone. For just 1,000 tokens.
HostThirteen percent? For what, like, a couple of paragraphs? For many users, that would not even be enough to get through a morning coffee. What exactly *are* these tech giants trying to sell consumers?
ExpertThey're trying to sell consumers a vision of "Edge AI," where all this incredible intelligence lives on your device. But the reality, according to Cornerstone Communications, is that the physics simply doesn't add up. And the financial incentives behind this push are far more cynical than they appear.
HostOkay, let's unpack that cynicism. Because the marketing around "Edge AI" – AI running directly on your phone or laptop – it's all about privacy, right? "Your data stays on your device," "low latency," "personalized AI." Sounds great.
ExpertIt sounds fantastic. And those benefits are real, in theory. Keeping data local *does* enhance privacy, and on-device processing *is* faster than round-tripping to the cloud. But framing it as an altruistic gift to the consumer completely misses the real story.
HostWhich is?
ExpertMoney. Pure and simple. This pivot to Edge AI is a multi-billion-dollar cost-shifting maneuver.
HostCost-shifting from whom to whom?
ExpertFrom Big Tech – Microsoft, Google, Amazon, Meta – to you, the consumer. To understand why, you have to look at the staggering cost of running AI in the cloud.
HostThe cloud, where many users interact with AI today, through services like ChatGPT or various online assistants.
ExpertExactly. And that's where the capital expenditure explosion comes in. The Cornerstone report, corroborated by financial filings, shows that these major tech companies spent over $380 billion on AI infrastructure in 2025 *alone*.
HostJust 2025? $380 billion? That's an astronomical figure.
ExpertIt is. And it's a massive acceleration. Back in 2022, their combined capital expenditure was about $162 billion. It more than doubled in just three years. This isn't just about building new data centers; it's about acquiring colossal numbers of Nvidia GPUs, and critically, securing the power grid infrastructure to run them.
HostSo, these companies are essentially building an entirely new, incredibly expensive, electricity-hungry infrastructure just to handle AI requests in the cloud.
ExpertPrecisely. This indicates an energy crisis at the data center level. Serving a single ChatGPT query, for instance, consumes roughly ten times the electricity of a standard Google search. When you multiply that by billions of users and trillions of queries, you start hitting municipal grid limits. The report even notes that in 2024 and 2025, some of these firms were investing in nuclear power plants just to secure enough electricity.
HostWait, nuclear power plants? Just to run their AI models? That's not a small footnote; that's a headline.
ExpertIt underscores the scale of the problem. The current business model for generative AI is fundamentally unprofitable at scale if these tech giants have to keep footing the electricity bill for every single prompt generated by billions of users.
HostSo, the "Edge AI" push isn't about giving consumers a better experience, it's about getting *out* of that electricity bill themselves.
ExpertThat's the ultimate cost-shifting maneuver. By shifting the computational burden of LLM inference from their server farms – their infrastructure, their electricity costs – to the Neural Processing Unit, or NPU, on your smartphone or PC, they achieve a massive financial victory. They offload those electricity costs directly onto the consumer.
HostThis implies that when a user utilizes cloud AI, Microsoft or Google covers the power costs. However, when "Edge AI" is used on a device, that device's battery bears the cost, meaning the user ultimately pays when plugging it into the wall.
ExpertThat's the practical implication. Silicon Valley isn't giving you a personal AI; they are making you subsidize their server costs. The industry is banking on this transition, with projections from IDC suggesting GenAI smartphones will account for 70% of the total market by 2028 – that's 912 million units sold annually. AI PCs are projected to hit 205 million units. They want a future where your device is their outsourced data center.
HostIt's like they're building a network of miniature, personal servers, but you're paying for the power and maintenance.
ExpertAnd this is where the cold, hard physics kicks in, because hardware does not care about software hype or financial maneuvering.
HostThat shocking 13% figure was mentioned right at the top. Let's delve into that. What does 1,000 tokens really mean in practical terms, and why does it drain so much?
ExpertThe Cornerstone Communications report conducted real-world testing. They found that generating just 1,000 tokens locally can consume up to 13% of a flagship phone’s total battery charge. To contextualize that, 1,000 tokens is roughly 750 words.
HostSeven hundred and fifty words. That's not a novel. That's like, two well-developed paragraphs, maybe a summary of a short article.
ExpertPrecisely. It’s the equivalent of generating a couple of email drafts, summarizing a moderately long PDF, or transcribing and summarizing a 15-minute morning meeting. Not an intensive, all-day workload for an AI.
HostSo if a user attempts to utilize a phone’s AI assistant the way the marketing materials suggest – as a constant companion that reads screens, summarizes notifications, drafts messages, and edits photos – that phone could be dead before lunch.
ExpertThat's the report's conclusion. It would be entirely depleted before a late lunch. This isn't just about inconvenient recharging; it's about making the device fundamentally unusable for its core purpose as a communication tool.
HostSo, *why* does it happen? Where does all that energy go? This isn't a huge server farm; it's a tiny chip in a user's pocket.
ExpertIt comes down to the physical constraints of silicon and energy storage. First, running an LLM requires moving massive amounts of data from the device's RAM to the NPU or GPU. This constant shuttling of data is highly energy-intensive. Think of it like trying to move a library full of books across town, one book at a time, very quickly. That's a lot of trips, a lot of fuel.
HostSo it's not just the computation, it's the data transfer itself.
ExpertExactly. And then there's the nature of the power draw. Real-world benchmarking shows that local inference creates massive, bursty power spikes. While an Apple M4 or a Snapdragon 8 Gen 3 chip is highly efficient at rest, spooling up that NPU to generate tokens draws intense peak wattage. It's like flooring the accelerator in your car for short, intense bursts. That consumes a lot of gas very quickly.
HostAnd phones don't have fans like laptops or desktops, right? So where does all that heat go?
ExpertThey don't. And that leads to the third major issue: thermal throttling. When an NPU draws high power for AI inference, it generates rapid heat. Benchmarking data indicates that flagship phones lose nearly half their token-generation throughput within just two iterations of a prompt due to thermal throttling.
HostHalf their speed? After just two prompts?
ExpertYes. In some Android devices, the operating system even enforces a hard frequency floor that terminates the AI inference entirely to prevent the phone from literally melting or damaging its internal components.
HostSo, the phone protects itself by just shutting down the very feature it's supposed to be highlighting.
ExpertThat's the reality. The Cornerstone Communications report offers a really useful historical analogy for this. It says that in the 2000s, early mobile tech was constrained by processing power. The chips were too slow. Then in the 2010s, the smartphone era hit a wall with bandwidth – we couldn't stream high-quality video until 4G and 5G networks caught up.
HostAnd now?
ExpertNow, in the 2020s, the Edge AI era has slammed into the "battery wall." The processing power and bandwidth exist. However, the energy density to sustain these massive AI workloads on a mobile device is fundamentally lacking.
HostSo, considering that 13% for a meeting summary… would a user trade 13% of a phone's battery to have an AI write a three-paragraph email that still requires proofreading?
ExpertFor 99% of consumers, the answer is a resounding no. And it highlights the absurdity of what's being offered versus what's actually being sacrificed. There's also the "idle tax" to consider. It’s not just active generation that kills the battery. Keeping the model loaded in the device's memory, or RAM, creates a constant power draw. It prevents the phone from entering deep sleep states, leading to what's called "phantom drain." So even if you're not actively using the AI, it's still eating your battery in your pocket.
HostSo, if the tech companies know this, if they're seeing these thermal throttles and massive battery drains in their labs, how are they still marketing this as the future?
ExpertThat's where the "marketing sleight of hand" comes in. If you watch any of the keynotes for the latest AI-enabled devices, you'll see endless demonstrations of photo generation, live translation, text summarization – all these impressive AI capabilities.
HostBut what you *don't* see?
ExpertWhat you absolutely will *not* see are battery life estimates that factor in heavy AI usage. Historically, Apple and Samsung have boasted about "all-day battery life" or "up to 20 hours of video playback." But there is no metric for "up to 4 hours of AI token generation." The industry is conspicuously omitting the energy cost of their flagship features.
HostIt's like selling a sports car and only talking about its top speed, never mentioning the fuel economy or the price of premium gas.
ExpertExactly. And this points to a massive, quantifiable disconnect between what Silicon Valley is building and what everyday consumers actually want. Decades of survey data confirm that battery life is the undisputed priority for consumers. Numerous surveys consistently show that a significant majority of consumers prioritize longer battery life as their primary reason for upgrading.
HostThat's a huge majority. So, what about AI features? Where do they rank?
ExpertIn stark contrast, consumer enthusiasm for AI is remarkably low. Surveys indicate that only a small percentage of consumers consider AI features a motivating factor for upgrading their device, with many stating that AI on phones is "not useful" and that they would prefer fewer additions if it compromised basic functionality.
HostSo, the feature they're pushing hardest is the one consumers care least about. And not only that, it actively *detracts* from the feature consumers care about *most*. This appears to be a recipe for disaster.
ExpertIt sets up what the Cornerstone report calls a "catastrophic collision course" for the tech industry, leading to a phenomenon known as "feature abandonment."
Host"Feature abandonment." What does that look like in practice?
ExpertIt goes like this: a consumer buys a new phone, perhaps drawn by promises of better cameras or a faster chip. This phone comes loaded with background AI processes. The user might activate the heavily marketed AI assistant, try it out. The phone's battery plummets, dying at 1:00 PM. The consumer, who values battery life above all else, then immediately searches for a way to turn those AI features off.
HostSo, the NPU, the specialized AI chip they spent billions developing and integrating, becomes dead silicon. Just a useless chunk of hardware taking up valuable space on the motherboard.
ExpertPrecisely. If users systematically disable on-device AI to save battery, the tech industry's entire $380 billion hardware pivot becomes useless. It’s an absurd market dynamic. Tech companies are spending hundreds of billions to force a feature onto phones that consumers actively rank at the bottom of their priority list, and which actively destroys the feature consumers rank at the top.
HostThis exemplifies an industry building what it *thinks* people want, rather than what people actually need or prioritize.
ExpertAnd it’s exacerbated by something called the Jevons Paradox. It states that as technological progress increases the efficiency with which a resource is used, the rate of consumption of that resource rises due to increasing demand. Tech giants hope making AI cheaper and more efficient will make people use it constantly. But if consumers use Edge AI constantly, the battery drain compounds exponentially. Efficiency gains in NPUs are immediately wiped out by the sheer volume of AI requests the software encourages.
HostSo, the more efficient the AI becomes, the more people use it, and the more battery it consumes, creating a never-ending cycle of drain.
ExpertThat's the trap. There's also a slight generational divide to consider. While most consumers reject AI in favor of battery life, some younger consumers do find mobile AI features helpful. But here's the catch: these younger consumers are also the heaviest smartphone users, with the highest daily screen time. They are the demographic most likely to hit the "battery wall" first and experience the frustration of a dead device mid-day.
HostSo, they might be more enthusiastic, but they'll also be the first to suffer the consequences and potentially abandon the features.
ExpertThis all brings the discussion to the fundamental problem that isn't going to be solved by next year's iPhone, or the one after that: the physical limits of current battery technology.
HostWhat are those limits? Can't we just make batteries better? Improvements have been seen over the years.
ExpertThat is true, but those improvements are starting to scrape against hard physical ceilings. The theoretical energy density limit for lithium-ion batteries is being approached, with today's highest-end commercial batteries already operating close to that maximum.
HostSo, the industry is not far from the theoretical maximum. It cannot simply "Moore's Law" its way out of chemistry, as the report puts it.
ExpertExactly. You can't shrink atoms. Making the battery physically larger makes the phone too heavy and thick, and packing the cells tighter dramatically increases the risk of thermal runaway – essentially, explosions. So, we're running out of room to maneuver with existing chemistry.
HostThis highlights a staggering misallocation of capital in the tech sector today. If battery tech is the bottleneck, why isn't more money going into solving *that* problem?
ExpertThat's the trillion-dollar blindspot highlighted in the Cornerstone report. Venture capital and massive corporate capex are flowing almost exclusively into software models and data center infrastructure.
HostThe $380 billion mentioned earlier, going into cloud AI.
ExpertPrecisely. Meanwhile, the physical bottleneck – energy storage – is being relatively ignored. In 2025, while Big Tech spent that $380 billion on AI infrastructure, venture capital funding for the *entire* Energy Storage sector was disproportionately small in comparison.
HostFive billion dollars versus almost four hundred billion. That's not just a disparity; that's an abyss.
ExpertIt is. The report issues a stark warning: mainstream edge AI adoption will completely stall unless capital pivots to the intersection of AI and battery tech. We desperately need breakthroughs in next-gen solutions like solid-state batteries, which replace the liquid electrolyte with a solid, allowing for higher density and safety. Or silicon anode technology, which can store more lithium ions than traditional graphite.
HostVarious companies are working on these technologies, but they require massive investment to scale.
ExpertAbsolutely. To scale manufacturing to the billions of units required by the smartphone market is an enormous undertaking. The journalistic verdict here is clear: the next massive wealth generation in tech isn't necessarily going to come from the company that builds a slightly smarter LLM. Software is becoming commoditized.
HostSo, where will it come from?
ExpertThe true trillion-dollar breakthrough will come from the unglamorous hardware and chemistry companies that figure out how to power a trillion-parameter model on a six-inch slab of glass without melting the device or killing the battery in two hours. There's also the environmental and regulatory angle here. If Edge AI forces users to charge their phones twice a day instead of once, the total lifespan of the phone's battery will be cut in half, from maybe 2-3 years down to 1-1.5 years.
HostThat’s effectively planned obsolescence, engineered by the very features being promoted.
ExpertWhich could lead to massive consumer backlash, potential class-action lawsuits similar to Apple's "Batterygate" over battery degradation, and a huge spike in e-waste. It's an environmental time bomb.
HostSo, it's not just a technical problem, it's a looming consumer and ecological crisis. That's quite the outlook.
ExpertThat's the potential outcome if this trajectory continues unchecked.
HostSo, to pull all of this together, what are the key insights listeners should really walk away with about this push for Edge AI?
ExpertFirst, the current "Edge AI" narrative is not primarily about user privacy or speed, despite the marketing. It's fundamentally a multi-billion-dollar cost-shifting strategy by tech giants to offload their massive cloud AI infrastructure expenses – particularly electricity costs – onto consumers.
HostSecond, the physics of current mobile hardware simply cannot support the promised capabilities. Generating even a modest amount of AI content on-device can drain a significant portion of a phone's battery, leading to thermal throttling and ultimately unusable devices.
ExpertThird, there's a profound disconnect between what Silicon Valley is building and what consumers actually want. Battery life remains the top priority for smartphone users, while enthusiasm for on-device AI features is remarkably low. This sets up a scenario for widespread "feature abandonment."
HostAnd finally, the underlying bottleneck isn't software; it's the chemistry of energy storage. Despite massive investments in AI software and cloud infrastructure, there's a critical lack of capital flowing into next-generation battery technologies needed to make sustained, powerful on-device AI feasible.
ExpertEssentially, the AI revolution, as it's currently conceived for mobile, is running on fumes.
HostThis certainly appears to be the case. Until the chemistry of energy storage catches up to the mathematics of neural networks, "Edge AI" is looking like nothing more than a marketing slogan waiting to be killed by a low-battery notification.
ExpertIt begs the question: how long can this discrepancy between marketing hype and physical reality truly persist before consumers demand a course correction?
HostAnd will the industry ever truly prioritize what users want over what saves them money?