Yes, I’m still writing about my AI builder journey, the most out of me in YEARS… I get it, I keep saying I would blog when I had something to say… I do now. Coding agents, multiple LLMs running in parallel, vibe-prompting entire features — it’s exhilarating. Search feels prehistoric now; why hunt for links when an LLM reasons through context and synthesizes faster? I open several chat sessions for the same project, treating them like a distributed team of specialists. One for architecture, one for debugging, one for research, one iterating on UI. Output is flying. Tools are built in days, not weeks.

And when I run a capable model locally on my desktop rig? The machine itself comes alive. The GPU spins up under heavy inference load, fans ramping into a deep, resonant hum. But the real star is the VRM buzz — the voltage regulator modules and their components straining to deliver hundreds of amps at precise low voltages. Inductors (coils) vibrate from rapid electromagnetic switching, while the multilayer ceramic capacitors “sing” via the piezoelectric effect as voltage ripples through them. The whole PCB acts like a speaker cone, turning electrical stress into audible whine and physical vibration you can feel through the desk. It throbs in rhythm with token generation, like the system is breathing hard and alive.

I even searched what this sentient-like buzzing and vibration was. Turns out it’s the VRMs and capacitors being heavily taxed under sustained high-wattage load — completely normal physics, not a failing part, but the hardware literally singing under stress as it powers matrix multiplications for hours on end. The rig pushes silicon to its limits right alongside me.

I love it. But it’s costing me. That same low-level buzzing has invaded my own head. Mental exhaustion hits harder and faster. There’s a constant mental static — a vibrating fog that mirrors the GPU’s VRM whine and the case’s resonant throb under load. Focus fragments. And recently, it’s spilled into real insomnia — lying awake replaying prompts and agent outputs — and straight-up skipping meals because I’m “in the flow” until my body protests. This isn’t sustainable. I’m not alone, and emerging research confirms it. This is showing the limit of how fast I can go given so much resource.

The Phenomenon Researchers Call “AI Brain Fry”

A March 2026 Harvard Business Review study from Boston Consulting Group (surveying 1,488 U.S. workers) named this exact experience: “AI brain fry” — mental fatigue from excessive use or oversight of AI tools beyond one’s cognitive capacity.

Participants described a “buzzing” feeling or mental fog, with difficulty focusing, slower decision-making, and headaches. High-oversight users (constantly reviewing, verifying, and orchestrating AI outputs) reported +14% mental effort, +12% mental fatigue, and +19% information overload.

That internal buzz feels eerily synchronized with my local rig’s physical vibration. The hardware’s coils and capacitors strain audibly and tactilely; my brain does the same — silently vibrating from the cognitive load of managing what feels like a team of tireless digital collaborators.

Managing multiple AI agents or chat sessions — exactly my workflow — amplifies it. The study highlights how juggling autonomous tools turns you into a full-time supervisor, not a creator. What was supposed to free time instead intensifies work through constant context-switching and evaluation.

About 14% of AI users experienced brain fry, with higher rates in creative/tech roles. Costs are real: +33% decision fatigue, +11% minor errors, +39% major errors, and 39% higher intent to quit.

The Human Cost: Insomnia, Skipped Meals, and Burnout Spillover — Echoes of Pi

My insomnia and meal-skipping aren’t unique. Earlier research linked frequent AI interaction to disrupted sleep and boundary-blurring. The always-on nature of these tools keeps the mental GPU spinning long after the physical fans wind down.

UC Berkeley/Yale ethnographic work showed AI adoption encourages multitasking, voluntary overwork, and blurred work-life lines. Productivity rises short-term; burnout follows, especially for power users running local models or parallel sessions.

This whole experience is starting to feel disturbingly like the movie Pi (1998) by Darren Aronofsky. If you haven’t watched it, stop reading and go do it — black-and-white, shot on 16mm, scored with a pounding industrial soundtrack that drills into your skull. It follows Max Cohen, a brilliant but tormented mathematician obsessed with finding hidden patterns in π and the stock market. He runs relentless computations on a straining home supercomputer setup while suffering brutal cluster headaches, paranoia, hallucinations, and social isolation.

The film is filled with motifs of buzzing, drilling, and static in Max’s head — auditory representations of his fracturing mind under obsessive load. Migraines come with auras, twitching, and a white-void escape, culminating in desperate self-surgery to “release” the pressure. The parallels hit too close: me chasing emergent patterns and breakthroughs through straining silicon (VRMs whining, GPU throttling), while my own neurons buzz with overload, replaying sessions at 3 a.m., skipping meals, feeling the isolation of deep flow states. I genuinely hope this isn’t foreshadowing my fate. The movie’s warning about the cost of pursuing ultimate truth through machines feels prophetic in 2026.

Why This Hits So Hard with Coding Agents, Multi-Chat, and Local Rigs

• Search is obsolete for reasoning tasks → LLMs win on synthesis speed.
• Multiple sessions = team management → Each chat has its own context and hallucinations to catch.
• Local inference → The tangible VRM vibration, coil whine, and capacitor singing make the acceleration feel visceral and alive — until your own nervous system starts humming in sympathy.

This is the productivity paradox of 2026: AI amplifies output while intensifying the mental (and sometimes physical) work of oversight.

What Now? Sustainable AI Use (Lessons I’m Applying)

I’m not quitting AI — or my local setup. But I’m redesigning and adding guard rails:

• Batch sessions and set hard cutoffs (including GPU cooldown periods).
• Protect deep-focus blocks without any models running.
• Prioritize AI for toil, reserve core judgment for me.
• Build in recovery: walks, meals, no screens before bed.
• Track personal metrics (energy, sleep, output quality and GPU/VRM temps).

This topic builds on my recent cantechit posts about vibe coding, agents, and the realities of AI adoption. The acceleration is real. So is the need for guardrails — before the buzzing becomes permanent.

What’s your experience? Local models making your desk (and brain) vibrate with VRM whine? Multiple agents burning you out, or found a sweet spot? Drop comments or links to your workflows.

Sources linked inline. Key reads: HBR on AI Brain Fry and related coverage.

Free Samples, Then the Hook (And Yeah, I’m in Full Withdrawal)

Look, I wrote this right after a stupid billing glitch with my main AI provider nuked my agentic coding access for two full days. No deep reasoning chains. No autonomous agents refactoring my messy repos. No vibe-coding flow where I spin up tools, iterate like a madman, and ship in hours what used to take weeks.

I felt actual withdrawal. Irritable. Slow. Like my brain was running on dial-up while the rest of me knew what god-mode felt like. That’s when it hit me: these AI companies aren’t just selling tools. They’re dealers. And we’re all hooked.

The Classic Dealer Playbook – Free Samples to Get You Dependent

First they flood the zone with insane value. Generous free tiers, cheap Pro plans, agentic capabilities that feel like having 1-2 full-time devs in your pocket for under $50/month. You get hooked on the productivity superpowers. You start building faster, thinking bigger, shipping stuff you’d never attempt solo. Your workflow changes. Your expectations reset.

Then comes the squeeze.

Everyone’s raising prices. Or doing the sneaky version: shrinkflation on tokens. Same monthly fee, but the model “thinks” less, outputs dumber results, or burns through your quota faster. I’ve seen reports of Claude Opus variants suddenly using 67% fewer thinking tokens for the same tasks. Same price, worse output, more tokens consumed. Classic move.

They’re quietly shifting from flat subscriptions to consumption models – per-call, per-token, usage-based billing (UBB). GitHub Copilot just did it. Anthropic is pushing enterprise users toward metered API rates on top of seats. OpenAI and others are following the same pattern. They pretend the subscription is still the core, but the real work (agentic sessions, heavy reasoning, long contexts) now eats credits that vanish fast. No rollover. Burn mid-project and you’re stuck.

I also see coding agents getting stuck in a loop, when it makes a mistake – wait a second – I’m paying for this tool, and it got stuck in a loop and burned MY credits…. That doesn’t seem fair.

This isn’t random. It’s the plan.

The Humanity Angle: This Technology Must Stay Accessible

Here’s where I get pissed off at the dealer playbook. Let’s stop for just a second.

This shouldn’t just be for those who can afford the new premium rates. AI needs to stay cheap — or get cheap again — because humanity desperately needs broad access to this knowledge and capability.

Think about the 20-year-old kid who couldn’t afford college, grinding in a basement somewhere with a killer idea but no formal credentials. With affordable AI, that kid can prototype, research, iterate, and ship at a level that used to require a full dev team and venture backing. One good prompt chain and they’re competing with people who have more resources.

Or zoom out to emerging markets — places where even $20/month is a real barrier. A developer in Lagos, Jakarta, or rural India with limited local opportunities suddenly has world-class reasoning, coding help, and research at their fingertips. The talent and ideas bubbling up from those places could be the next massive breakthroughs. We’re talking exponential global innovation in emerging markets, if we don’t gatekeep this behind usage-based pricing that prices out the ambitious but broke.

Everyone should have access, not just Western professionals with corporate cards. The more minds hooked into these tools, the faster we solve hard problems in climate, biotech, energy, education — you name it. Imagine the compound effect: millions of new builders creating tools, businesses, and scientific insights we can’t even picture yet. That’s the real moonshot. Locking it behind ever-rising costs kills that potential.

I remember listening to Jake Hirsch-Allen talk about democratizing AI, building a coalition of open democratic economies. Investing in Public AI “The CBC of Compute” he calls it. I recall that line in “Anti-Trust” (A horrible 2* tech film from the 2000’s filmed in British Columbia) “Human knowledge belongs to the world” – Ryan Phillippe said on screen.

Why This Was Always Coming (And Why It’s Not Pure Evil)

Let’s be real for a second – I’m not some conspiracy nut. The economics make sense.

Frontier models are insanely expensive to run. Inference costs, HBM memory shortages, power bills, massive CapEx – the whole industry is bleeding cash even as they raise billions. Demand exploded. They loss-led hard to build market share and moats while compute caught up. The “basically free god-mode AI” era was the sample pack. Now they’ve got us dependent, the bills have to get paid.

Agentic workflows aren’t cheap chats. A multi-hour autonomous coding session that loops, reasons, tools, and iterates eats serious compute. Companies can’t subsidize that forever while memory prices go parabolic and everyone wants Opus-level reasoning.

So yeah – prices up, or shrinkflation, or straight usage-based. Pick your poison. GitHub’s recent Copilot changes (credits instead of unlimited premium requests, Opus multipliers jumping hard) are just the visible tip. More will follow.

We’ve been underpaying for this superpower. Shipping in days what used to need teams? Turning weekend experiments into real tools? That value is still ridiculous compared to hiring humans. But the golden age of predictable flat-rate unlimited was always temporary.

What This Means for You (Practical Moves)

Diversify like your workflow depends on it (it does).

• Stay flexible: Tools like Continue.dev (bring your own keys) or OpenRouter let you pick the best model without middleman markups. Consider holding accounts with more than one provider, another subscription might be cheaper than overage charges (think 2 bags on the aircraft is cheaper than one overweight one)
• Watch the credits: Set hard budgets. Test heavy sessions in advance so you don’t get stranded mid-project. Don’t let flows run endlessly.
• Hybrid stack: Keep one smooth native tool (Cursor, Copilot, Windsurf) for daily flow, but route deep agentic work to raw APIs where you control cost.
• Build habits: Use AI to multiply your thinking, not replace it. The real moat is knowing when to guide, when to verify, and when to ship.
• Prompt engineer: Better prompts = better output = more context = less tokens. Learn to plan projects, and use quality prompts, this will save both time, money and of course – Tokens.

I’m still all-in on AI. Sitting with my kid building stuff is next-level. The superpowers haven’t disappeared – the pricing is just normalizing to reality. How can we make this accessible, don’t gatekeep it behind rich corporations. Those who have this power will become superpowers.

The dealer’s got us. Time to use the high productively while we figure out the new economics.

What do you think? Hooked yet?

Perplexity Computer: $200 a Month Feels Like a Steal… But We’ve Been Getting AI Dirt Cheap

If you haven’t watched NetworkChuck’s video on Perplexity Computer yet, stop everything and check it: https://www.youtube.com/watch?v=G3jvn7n-68Y

Chuck and his kiddos built a full gaming website with it. Pure dad-tech gold. His reviews always cut through the noise and save me (and probably you) hours of digging. Thanks, Chuck — you rock.

That video got me thinking about sitting down with my own kid to design, engineer, and build real stuff with AI. Not just prompts — actual projects. Perplexity Computer feels like the perfect playground. But then the price hit: $200/month for the Max tier that unlocks the full agentic power.

And that’s the spark for this post.

Perplexity Computer Is Next-Level (But Is $200/Month Crazy?)

Launched Feb 25, 2026, Perplexity Computer isn’t another chatbot. It’s a digital worker that orchestrates 19 frontier models in parallel, spins up sub-agents, hooks into 400+ apps, runs background tasks for hours or days, and just gets shit done. Full details here: https://www.perplexity.ai/hub/blog/introducing-perplexity-computer

Chuck’s “crew” shipped a game site. Others are building dashboards, prototypes, and entire workflows that used to take teams weeks. One enterprise user reportedly compressed years of work into weeks.

The problem with all of these services is – “tokens” nobody can tell you how many tokens it costs to do X, you only find out once you start doing things, but as I have found out with services like GenSpark and Perplexity – credits go fast, and when you are 3/4 done and run out — they have you.

Yeah, $200/month stings at first glance, others are spending thousands per month with the extra tokens…. but.

We’ve Been Underpaying for God-Tier AI This Whole Time

Look at what we’ve built lately with Claude, GPT, Cursor, and early agents. I’ve personally shipped internal tools and automations that would’ve needed multiple full-time devs, designers, and PMs just two years ago. The human hours I didn’t spend hiring? Massive. The “too expensive to try” ideas that became weekend wins? Priceless. It seems almost too good to be true — but is it.

There is this natural disparity in my mind, which was why I am writing this….. If it cost me say $100K in engineering time to contract hire a bunch of people to build some form of application – but I can use AI to do nearly all of it for $1000 in AI tokens, that’s 1/100th the price… However those engineers are making money.

Meanwhile, the AI companies are bleeding cash. OpenAI has been posting multi-billion-dollar losses. Perplexity has spent more than its revenue on models and infra at times. The compute bill is insane.

Naturally – AI tokens and services are going to go up in price – they just have to.

Then OpenAI shut down Sora in March 2026 — a money pit they needed to kill so they could refocus compute on higher-priority stuff like coding agents and robotics. Details: https://www.nytimes.com/2026/03/24/technology/openai-shutting-down-sora.html

We got hooked on ridiculously capable AI at bargain prices. Perplexity Computer feels like the first big “okay, time to pay what it actually costs” moment – but I will argue they are still not charging enough. Same vibe with Claude — Anthropic has tightened free-tier access for heavy agent use (like third-party OpenClaw setups) and pushed people toward paid Pro/Max/API tokens. Classic addiction cycle: get everyone dependent, then the price catches up to reality. Still arguing it’s not reality but they know they can’t 10X the price overnight.

The Newcomer Worry — And Why Competition + Reality Still Makes Me Hopeful

How do junior engineers learn the fundamentals when AI handles so much of the grunt + magic? Valid fear. We rely on the critical thinking of engineers from their experiences, and AI does a better job when you guide it – but – how do you get that experience to guide it?

But here’s the flip: the barrier to creating has never been lower. My daughter (and Chuck’s kids) can now experiment with real design and engineering at a level that used to require expensive teams or years of school. Passion and curiosity suddenly matter more than raw syntax.

Competition is fierce — OpenAI, Anthropic, Google, xAI, and more. Competition is very good news: it should depress prices a bit and keep innovation humming.

But here’s the steelman reality check: How sustainable is that when infrastructure costs are exploding? AI demand has driven memory prices (especially HBM/DRAM) sky-high — 50%+ jumps quarter-over-quarter in early 2026, with supply locked into 2027. GPUs, data centers, power — everything is getting more expensive fast. The infra bill isn’t going down; it’s accelerating.

So prices will probably ratchet up over time as the companies stop subsidizing our addiction. But the value we’re getting is still absurdly high compared to the old world of human-only teams.

Bottom Line: We’re Not Overpaying — We’re (Starting To) Finally Paying Fair

Perplexity Computer isn’t overpriced. It’s the wake-up that the “basically free god-mode AI” golden age was always temporary. The companies subsidized it to hook us and build moats. Now the bill is coming — but the superpowers we’re getting in return are still a steal.

I’m buying in. Not just for the productivity, but because I want to sit next to my kid and say, “Let’s build something cool together.” The future isn’t replacing humans — it’s giving every human (kids included) superpowers. This is bringing incredible higher value work to the human race. During the industrial revolution everyone worried about jobs, when 80%+ of humans made food so we could eat – often by hand. Now with industrial manufacturing and technology, we have more time for better innovation. This is no different. Will we see job disruption – of course, but this means a whole new era of innovation.

What do you think? Is $200/month worth it for Perplexity Computer? Felt the “addiction then price hike” yet? Drop your takes below.