The AI Hardware Tax: How AI Affects Your Computers

Your employees' computers are slowing to a crawl, and some of them are doing something about it. They're turning off their security software. It's a quiet crisis unfolding inside businesses of every size, driven by the explosive resource demands of AI tools and a global hardware shortage that's making it worse by the day. Call it what it is: the AI Hardware Tax.

Businesses are seeing a 54% increase in bandwidth requirements due to AI adoption. While users expect AI to speed up work, running these local models or complex integrated programs on outdated hardware is actually causing users to complain their computers are too slow. In fact, Google Trends shows a massive spike in searches such as the following:

• speed test
• my computer is running slow
• how to fix slow computer
• internet speed
• why is my computer slow all of a sudden

AI Hardware Tax

RAMageddon Behind Slowdowns

There's a reason new computers cost significantly more than they did a year ago, and it has nothing to do with what's sitting on your employees' desks. The world's three dominant memory chip manufacturers, Samsung, SK Hynix, and Micron, together control roughly 95% of global DRAM (Dynamic Random Access Memory) production. They've quietly redirected a growing share of that capacity toward high-bandwidth memory chips that power AI data centers, leaving far less supply for the consumer and business PC market.

Industry insiders refer to this as "RAMageddon." A 32 gigabyte Double Data Rate 5 (DDR5) memory kit that sold for less than $100 in 2025 now starts at $350, if it's even available. PC Gamer reports that CyberPowerPC cited a 500% surge in RAM prices when it raised system prices in December 2025. Solid state drive (SSD) storage has followed the same trajectory. SK Hynix has told investors the shortage is expected to persist through 2028.

Major PC makers are passing those costs along. Dell, Lenovo, HP, and Microsoft have all raised prices on business hardware, with some models jumping 15% to 50% in a matter of months. HP's Q1 2026 earnings call revealed that memory now accounts for 35% of a PC's build materials, up from 15-18% the prior quarter. That's the hardware environment companies are navigating when they try to outfit employees with AI-capable machines. In short, businesses are paying more for computers that still can't keep up.

Pack Patience

The problem isn't just the cost of new hardware. It's what happens when AI applications run on equipment that wasn't designed for them. Large language models (LLMs), AI-assisted productivity platforms, and real-time analytics tools are computationally expensive. They were built for data centers with substantial processing power, not for standard-issue business laptops already several years into a typical refresh cycle. Our AI basics primer explains how these models work and why they demand so much from the hardware running them.

Endpoint security tools are resource-intensive by design. Endpoint detection and response (EDR) platforms continuously monitor system behavior, scan files in real time, and send telemetry to cloud analysis engines. When an AI productivity app is competing for the same CPU cycles and RAM, users notice. Machines freeze. Tasks that once took seconds now take minutes. Frustration sets in fast.

"When a machine slows to a crawl, the first thing many people do is open the task manager and start shutting down whatever's using the most resources," said Rich Miller, founder and CEO of STACK Cybersecurity. "Security tools are visible, they consume resources, and most users don't understand what they're actually protecting against. That makes them an easy target.

"I recently discussed this with a CEO who called me to complain her machine was too slow. She blamed our cybersecurity tools. After I explained what was really slowing down her machine, she told me we're all going to have to relearn how to be patient. I couldn't agree with her more."

Slow Computers Vulnerable

Disabling endpoint protection, even temporarily, removes a critical layer of defense at the worst possible time. According to ReliaQuest's 2025 Annual Cyber-Threat Report, the median time for an attacker to move from initial access to lateral movement inside a compromised network is just 29 minutes. That's not a window, it's a blink. An endpoint running without active security monitoring may already be compromised before anyone notices something is wrong.

The risk doesn't stop at the individual machine. Once an attacker is in, they typically use that foothold to move laterally across the network, targeting servers, shared storage, and systems that may otherwise be well protected. The weakest machine becomes the entry point for a much broader breach. The Stryker cyberattack is a sobering example of how quickly threat actors can escalate once they're inside.

When RAM is maxed out, the operating system prioritizes active user tasks over background processes. Security scans, behavioral monitoring, and telemetry reporting are all background processes. That means a machine under heavy AI workload isn't just slow, it's actively deprioritizing the tools designed to catch threats. As we've covered in our analysis of cybersecurity investment costs, the price of prevention is almost always lower than the cost of recovery.

In companies where IT and security teams aren't tightly integrated, these self-inflicted configuration changes can go undetected for weeks. Employees with local administrator rights can pause firewall rules, disable security agents, or whitelist applications to improve performance. Security dashboards may show nothing alarming until an incident surfaces what was actually running, or not running, on affected endpoints.

Insurance Trap

There's a compounding risk most businesses don't consider until it's too late: a disabled security tool can void a cyber insurance claim. Insurers don't just expect companies to have cybersecurity tools. They expect proof those tools were active and functioning at the time of a breach. If your policy requires endpoint detection and response and an attack occurs while that protection was paused or uninstalled, your carrier may deny the claim outright.

This isn't theoretical. The City of Hamilton, Ontario, had its $18.3 million ransomware recovery claim denied in 2025 because its multi-factor authentication deployment was incomplete at the time of the attack. The insurer cited the gap as a policy violation. Industry data shows that between 25% and 40% of cyber insurance claims are now rejected due to gaps in required security controls. Cyber insurance only works when the controls on your policy application match what's actually running on your machines, documented and provable.

An employee disabling EDR to speed up their laptop isn't just a security problem. It's a coverage problem. If that machine is the entry point for a breach, the company may find itself paying the full cost of recovery without any insurance relief.

Shadow AI Makes It Worse

The performance problem compounds when employees install AI tools outside of official IT channels. Shadow AI, the unauthorized use of AI applications without IT or security review, has accelerated significantly. According to the National Cybersecurity Alliance's 2025 "Oh Behave!" report, 65% of workers now use AI tools, up from 35% the prior year. Most haven't received any training on the security and privacy risks involved.

Employees who find a productivity tool that gives them an edge are often more motivated to keep it running smoothly than maintain their security posture. When that tool strains their hardware, they start making tradeoffs. Security software frequently loses.

Phishing attacks, which remain one of the most common initial access vectors, are also increasingly AI-assisted. Threat actors now craft convincing, personalized lures at scale, including messages that mimic colleagues' writing styles or replicate familiar internal communications. Deepfakes extend the threat further, enabling attackers to impersonate executives in audio and video. A user who's disabled email security scanning to reduce slowdowns is far more exposed to those tactics. Our post on how AI is changing hacker tactics covers the full scope of what businesses are up against.

Policy Problem

Faster hardware helps, but it doesn't solve the underlying issue. Many companies lack clear policies governing which AI tools employees can use, what the minimum device requirements are, and what happens when a new application conflicts with security configurations. Without that structure, purchasing decisions and software installs happen informally, and security teams learn about them after the fact. Our AI security best practices guide outlines the governance steps companies should already have in place.

"The companies that handle this well aren't necessarily the ones with the fastest machines," Miller said. "They're the ones that know what AI tools are approved, what devices those tools are allowed on, and what security requirements have to be in place before anything gets deployed. That structure doesn't slow them down. It keeps them from having to clean up a mess later."

A starting point is a formal AI tool inventory. Know what's running, on which machines, and whether those machines can support the workload alongside endpoint security. Where gaps exist, the answer may be hardware upgrades, cloud-based AI alternatives that reduce local processing demands, or a different tool altogether. What it shouldn't be is disabling the security layer to make room. STACK's AI readiness checklist walks through exactly what to assess before deploying any new AI platform.

New Hardware Reality

Security and IT leaders should treat AI-driven performance strain as an active risk. Audit endpoint security configurations regularly and don't assume tools installed six months ago are still running. Check agent health dashboards, review telemetry for reporting gaps, and investigate any machine that's gone quiet. A device that stops sending security data is a warning sign, not a routine anomaly. STACK's cyber dashboard gives leadership real-time visibility into the security status of endpoints across the organization.

Review endpoint protection settings with performance in mind. Most EDR platforms let administrators tune scan schedules, adjust resource usage limits, and deprioritize background tasks during peak workloads. Working with your security vendor to optimize those configurations can reduce the friction that pushes users to start disabling things.

Build a formal AI approval process. Require employees to submit applications for IT review before installation, giving security teams the chance to assess resource demands, evaluate data privacy implications, and confirm the tool meets the company's security baseline. That step costs little time. Skipping it can cost considerably more. STACK's AI Hub includes tools and resources designed to help businesses assess AI risk, including our AI Risk Evaluator (AIRE) for auditing tools against your security posture.

Finally, address the hardware reality directly. If AI tools are part of your business's productivity strategy, the devices employees use must support them without compromising security. With PC prices climbing and the memory shortage projected to continue well into the decade, deferring hardware refresh cycles while adding AI workloads is a risk decision. If it leads to a breach, it may also be an insurance decision.