Latest / We The People, in Tech or Not: The Debate / The Hidden Cost We Don't Know We Have, and How To Fix It

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The Hidden Cost We Don't Know We Have, and How To Fix It · We The People, in Tech or Not: The Debate

• 0:00
  Welcome to We The People In Tech or Not: The Debate.
• 0:04
  This is an AI-generated podcast based on a post from the We The People In Tech or Not newsletter by Alex Rosa, and its goal is to help listeners learn new ways to explain technical topics from a beginner non-tech understanding up to an expert level.
• 0:17
  Yeah, and we are, uh, really excited to dive into today's topic. Exactly. So imagine snapping your fingers, right? And a fully furnished hundred-story skyscraper just builds itself instantly.
• 0:28
  I mean, the lights are on, the plumbing works, the security system is completely active. A miracle of modern engineering, really. It is.
• 0:36
  But then, at the end of the month, you receive this massive utility bill for, say, 30 underground floors you didn't even know existed, simply because the automated blueprints decided to include them as a best practice.
• 0:48
  Right. You get a bill for floors you didn't even know were there. And that is... Well, that's exactly the reality of the modern cloud, and it's the core tension we're debating today.
• 0:56
  Cloud computing and AI scripting make building digital infrastructure incredibly fast, but that speed masks a massive problem of hidden costs. Yeah. It's a...
• 1:05
  That skyscraper analogy perfectly captures the surreal landscape we're dealing with. We can deploy these vast digital environments in minutes, but the incredible speed completely hides a growing financial black hole.
• 1:18
  We are seeing a proliferation of hidden costs, services running quietly in the background that literally nobody uses, but everybody pays for month after month. Right.
• 1:27
  And the author of our source material proposes a highly rigorous, human-driven five-step routine to find and eliminate these hidden costs.
• 1:34
  And you know, when I look at the sheer speed of AI-generated infrastructure, I argue that this manual human-in-the-loop routine is an absolute necessity. You think it's the only way? I really do.
• 1:45
  Without it, you are guaranteed to experience runaway cloud bills simply because the automation creates resources way faster than any automated guardrail can actually track.
• 1:55
  See, I look at that exact same five-step routine, which, let's be clear, requires inventorying, tagging, reviewing, acting, and preventing every single month, and I see an unrealistic, paralyzing burden.
• 2:07
  I mean, if you are a busy non-technical manager or a director of operations, the human capital required to execute this manual hunt is a massive drain. But the savings.
• 2:18
  We have to seriously ask, though: Does the payroll cost of hunting down these minor idle resources actually exceed the cost of just, well, paying the hidden cloud invoice? Does this really apply to a busy manager?
• 2:30
  Well, I think to answer that, we have to understand that these hidden costs are fundamentally changing.
• 2:35
  Historically, a hidden cost was something you intentionally built, used for a little while, and then simply forgot to turn off.
• 2:41
  But because of AI and deployment templates today, these costs are increasingly things we never even knew we built in the first place. Things that just spawn in the background. Exactly.
• 2:51
  And the author provides a brilliant real-world example of this shift. They used AI to write a deployment script for an Azure SQL Server lab. Standard request, nothing crazy. The script worked perfectly. Server spun up.
• 3:03
  But upon manually reviewing the actual resources generated, the AI had quietly included an advanced networking service called Azure Bastion. Right.
• 3:12
  And for those who aren't familiar, Azure Bastion is essentially a highly secure way to connect to your virtual machines without exposing them directly to the open internet. The AI didn't include it to be malicious.
• 3:23
  It included it because modern AI tools are trained on enterprise best practices. Microsoft prioritizes security over your wallet.
• 3:30
  Which makes sense from a security standpoint, sure, but the financial implications are staggering. To use an analogy, Azure Bastion is like a, a, a high-end, highly secure private lobby for your digital building.
• 3:42
  Complete with the security guards and metal detectors. Exactly. Biometric scanners, the works. It is incredibly secure.
• 3:51
  But the financial catch is that you are billed for this secure lobby continuously by the hour from the very moment it is constructed, whether anyone actually walks through those metal detectors or not. Wow. Yeah.
• 4:01
  And the author notes that this single AI edition was placed in two different geographic regions. At about twenty-nine cents an hour per host, that unused lobby was going to cost four hundred dollars a month.
• 4:11
  That is a four hundred dollar bill generated before a single user even logged into the system. You cannot rely on tools alone to fix this. We need that strict human-driven review.
• 4:20
  Look, I'll admit spending four hundred dollars a month for an unused private lobby is a problem, unquestionably. But consider the alternative the author actually implemented to avoid that cost.
• 4:31
  To bypass Bastion, the author used SSH and a public IP address to reach those virtual machines directly. Right, secure shell. Yeah.
• 4:39
  SSH, which essentially pokes a direct encrypted hole through the internet straight to the server.
• 4:44
  So if Bastion is a high-end secure lobby with guards, using SSH with a public IP is like putting a cheap padlock on a direct side door that leads out to a busy city street.
• 4:54
  A side door that is constantly being rattled by automated bots on the internet looking for a way in. Which is exactly why the author readily admits this workaround requires significant locking down, right?
• 5:05
  They explicitly state they wouldn't ever use this method in a real production environment. So immediately, the alternative to paying the hidden cost introduces a serious security dynamic. That's a fair point.
• 5:16
  But more importantly, if you're a non-technical business manager, you're being asked to implement this five-step manual routine every single month. Inventory, tag, review, act, prevent.
• 5:29
  Hunting down minor idle resources requires highly specialized attention.
• 5:34
  I mean, the cure might actually be worse than the disease when you factor in the sheer amount of time professionals have to spend debating line items.
• 5:41
  But to understand why that time investment is necessary, we really need to look under the hood at how cloud providers physically architect their billing meters.
• 5:49
  It isn't just about flashy, expensive services like Bastion. The hidden waste is baked into the fundamental mechanics of the cloud. Take virtual machines or VMs. The author points out a classic trap.
• 6:00
  If you log into a Windows VM and click Shut Down from the Start menu, Azure still bills you for the underlying compute power.
• 6:06
  Because the operating system is off, but the cloud hardware hasn't been released back to the provider. Right.
• 6:12
  Think of it like turning off your television screen with a remote control, but leaving the massive cable box and the surround sound receiver running at full power in the background.
• 6:20
  The screen is completely dark, so to the user, the system appears off, but the power meter on the side of your house is still spinning furiously. Right. It's still drawing juice. Exactly.
• 6:30
  Azure only stops the billing meter when the machine is formally selected in their portal and moved to a stopped deallocated state, and you know, the mechanics get even deeper than that.
• 6:38
  When you finally do delete a virtual machine, the digital hard disks attached to it often just get left behind.
• 6:44
  They just sit there spinning in the digital void, billing you continuously, even though there is literally no computer attached to them to read the data.
• 6:52
  The television and cable box analogy perfectly illustrates that disconnect between what a user sees and what the cloud provider actually bills.
• 6:59
  But in a real business environment, that silent, invisible nature is the exact reason this manual routine fails the practicality test. Notice a common thread in every single example you just listed. What's that?
• 7:12
  None of those orphan disks or idle VMs actually break the system. Which is why they sit there for months draining the budget. But think about how a director of operations manages a team.
• 7:22
  A, a broken resource triggers alarms. Users complain. The IT ticket system lights up. Engineers are dispatched to fix it. But an idle resource, it just silently generates an invoice at the end of the month.
• 7:33
  If the system isn't broken, you are asking managers to dedicate hours to hunting for ghosts. It's a necessary hunt, though.
• 7:39
  But hunting for those ghosts requires specialized technical knowledge that a standard business manager just does not possess. They don't know the difference between a Windows shutdown and a hypervisor deallocation.
• 7:49
  By mandating this routine, you force non-tech managers to rely on already overworked tech teams, creating a massive operational bottleneck.
• 7:57
  Well, I'd argue the author's five-step routine is designed specifically to prevent that bottleneck. It acts as a bridge between the highly technical reality of the cloud and the necessary business oversight.
• 8:08
  Let's actually walk through the mechanics of it. Step one is inventory. You pull a raw list of everything that exists. Step two is where the magic happens: tag.
• 8:17
  Every single digital resource gets metadata attached to it, an owner, a specific business purpose, and crucially for temporary projects, an end date.
• 8:26
  So metadata is essentially a digital sticky note attached to the resource. Yes, exactly. Think of it like managing a massive office refrigerator.
• 8:34
  Tagging is putting your name and a throwaway by date on your leftover lunch container.
• 8:39
  If there is a random container sitting in the back of the fridge with no tag and no explanation, it automatically goes on the review list to be thrown out. Okay, I see where you're going with this. Right.
• 8:48
  That system makes a massive digital haystack manageable, which logically brings us to step three: review.
• 8:54
  You sit down with the department heads or resource owners and ask one simple question for each item: Does this still need to exist to serve the business? Then step four: act. You delete the orphan disks.
• 9:04
  You deallocate the VMs. And finally, step five: prevent, which involves setting strict financial budgets and manually reviewing those AI-generated deployment scripts before they ever run.
• 9:15
  The office fridge analogy works great if you have, say, 10 employees, but what happens when that fridge is the size of an Amazon warehouse?
• 9:22
  Well- I just cannot accept that step three, the manual review, is practical at an enterprise scale. The author explicitly states to sit down with the owners.
• 9:30
  If you are a large organization, dragging various department heads into a monthly meeting to debate untagged line items is organizational paralysis.
• 9:39
  Let's actually apply the author's own data to the return on investment for this exact meeting. Okay, let's look at the data. The author ran this rigorous routine on their own lab inventory.
• 9:48
  Out of a relatively small environment, they found that eight out of 12 hard disks were still attached to stopped VMs. Which is a staggering ratio of waste. Eight out of 12 disks were silently bleeding money.
• 10:00
  But look at the actual financial impact of that waste. The author's cost tab showed that for the entire month of September, those orphaned disks spread across more than 20 individual line items, added up to about $50.
• 10:12
  Now, put yourself in the shoes of a business leader.
• 10:14
  Are you really gonna schedule a multi-manager review meeting, pulling highly paid technical professionals away from their core engineering work, just to track down the owners of 20 esoteric line items to save $50?
• 10:26
  I mean- The payroll cost of that one-hour meeting is five times the savings. The math on the human labor simply does not justify the strictness of the routine.
• 10:35
  You are isolating a very small personal lab environment's cost and directly applying it to an enterprise scale while entirely ignoring the compounding nature of cloud waste.
• 10:43
  Yes, the immediate savings was $50 for a single month in a tiny lab, but in a corporate environment where deployments happen dozens of times a day, that $50 scales to five thousand or $50,000 incredibly quickly.
• 10:55
  It can scale, yes, but- And more importantly, we have to address why the human element of that review is fundamentally non-negotiable.
• 11:02
  We cannot just automate the cleanup because tools and AI simply cannot understand business context. Well, automated optimization tools are getting highly sophisticated at identifying where resources are underutilized.
• 11:13
  They are sophisticated at identifying usage, sure, but they are completely blind to intent. The author provides a brilliant example regarding Azure Advisor.
• 11:22
  Azure Advisor is Microsoft's built-in automated tool specifically designed to optimize your cloud resources.
• 11:28
  It analyzed the author's environment and confidently generated 185 recommendations, promising a massive savings of $5,718 a year. How? By advising the author to commit to long-term reservations and savings plans.
• 11:42
  Right, and in standard business terms, that means shifting from paying a premium hourly rate to locking in a one or three-year contract for a steep discount. Exactly the dynamic.
• 11:51
  It's like a gym offering you a massive discount if you prepay for a three-year membership in full, simply because their automated turnstile system saw you visit the gym three days in a row.
• 12:00
  Now, if you are running a massive 24/7 production environment, if you are a dedicated athlete who Practically lives at that gym. That is phenomenal financial advice. You should sign that three-year deal. Oh, absolutely.
• 12:12
  But the author's environment was a temporary testing lab explicitly built to be torn down a few weeks later.
• 12:18
  If the author had blindly followed the automated tool's advice, they would have locked themselves into paying for years of capacity they didn't actually need. The automated tool looks at the servers and sees usage.
• 12:28
  It sees that the lights are on right now, but it does not see intent. It has no idea the lab is temporary. Only a human being can provide that intent.
• 12:37
  That is why handing this process off to automated cleanup tools is dangerous, and why the manual review is absolutely essential.
• 12:44
  I will concede that is a brilliant point about intent, but, and this is a big but, if the automated tools are truly that blind to business intent, how can a non-technical manager safely execute step four of the routine, which is act?
• 12:57
  Well, they use the inventory reports. But if the AI is giving bad advice, you're asking managers to delete infrastructure based on severely flawed intelligence.
• 13:05
  Let's look at the inventory gathering tool the author actually recommends to find these hidden costs. It's called Azure Resource Inventory, or ARI.
• 13:13
  It's an open source PowerShell module, PowerShell being essentially a scripting language that interrogates the cloud and asks, "Give me a list of everything built here." Right, and it outputs a great visual.
• 13:23
  It is read-only, and yes, it draws a very nice network diagram in, in a program called draw.io, but the author explicitly highlights a glaring discrepancy.
• 13:32
  The Azure portal counted sixty-four resources in the environment, but the ARI tool only reported fifty-seven.
• 13:39
  Well, the author openly acknowledges that no third-party tool sees absolutely everything in the cloud architecture. But consider the massive operational risk that gap creates.
• 13:48
  If the inventory tools are dropping resources and the advisory tools fundamentally misunderstand intent, how can a non-tech person safely hit the delete button? They don't do it blindly.
• 13:59
  The author explicitly warns the reader that a generated report can easily tell you an orphan disk has no VM attached to it, but that report cannot tell you if that orphan disk happens to hold the only backup copy of a critical company database.
• 14:13
  As the text wisely notes, figuring that out is a conversation, not a query.
• 14:17
  I argue that asking managers to aggressively delete infrastructure based on incomplete tool reports just to save fifty bucks a month is a terrifying operational risk.
• 14:26
  Deleting one disk that holds critical client data will cost the company infinitely more than the idle cloud fees.
• 14:32
  And that risk profile is the exact reason step three, the conversation with owners, must occur before step four, the action. The entire routine is deliberately designed to mitigate that specific risk.
• 14:44
  You do not just blindly delete resources based on a spreadsheet, you use the report to force the conversation.
• 14:50
  And the ARI tool, despite missing a few minor background resources, is incredibly effective at surfacing those critical blind spots. It's helpful, sure. I mean, it runs in about a minute.
• 15:01
  It flags resources that are missing tags, and it highlights those tricky VMs that are stopped but not deallocated.
• 15:07
  Most importantly, it found six operating system disks in the author's lab that were running on standard HDD. Standard HDD being traditional spinning magnetic hard drives rather than modern solid-state flash drives.
• 15:18
  Exactly. And the critical context the author adds is that Azure is officially retiring those standard HDD configurations in September 2028.
• 15:27
  Now, to your earlier point, an idle disk running on old hardware isn't breaking anything today. It isn't generating an IT support ticket.
• 15:34
  But if nobody notices it now, that silently aging infrastructure becomes a massive, expensive emergency migration project in 2028. The manual routine surfaces the technical debt before it becomes a crisis.
• 15:46
  I certainly do not disagree that infrastructure visibility is vital to a healthy business. My contention remains with the rigidity of the proposed solution.
• 15:55
  The modern cloud was fundamentally designed for speed and agility. AI templates allow engineering teams to stand up entire architectures in minutes to test new products or respond to market demands.
• 16:06
  If we drag every single automated deployment into a bureaucratic monthly review meeting, forcing managers to interrogate every untagged IP address, we destroy the very agility we moved to the cloud to achieve in the first place.
• 16:19
  But agility without accountability isn't agility at all, is it? It's just operational chaos. Yes, AI makes deployment faster than ever.
• 16:27
  A script writes the code, a wizard runs the deployment, and suddenly you have sixty-four resources you barely understand.
• 16:33
  Because they are created faster than any human can review them, this new category of hidden costs, the things we didn't even know we deployed, like that four hundred dollar Bastion Lobby, is growing exponentially.
• 16:45
  You cannot rely on a broken resource support ticket to save you because these hidden deployments don't break anything. They just silently drain the quarterly budget.
• 16:53
  The human in the loop review is the only effective defense against the silent compounding bloat of AI-generated infrastructure. Okay.
• 17:00
  Let's synthesize where we actually align on this issue, because beneath all the mechanics, I think we share a fundamental realization about the technology itself.
• 17:08
  We both completely agree on the core premise of the author's experience.
• 17:11
  AI and deployment templates make building digital infrastructure incredibly fast, and that exact speed has created a massive new category of financial waste. We are no longer just paying for things we forgot to turn off.
• 17:22
  We are actively paying for things we never even knew were turned on. Right. The four hundred dollar secure lobby that nobody walked through was the perfect emblem of that new reality.
• 17:31
  And I think we also converge on the realization that automated tools, while incredibly helpful for gathering raw data, fundamentally lack the human context needed to safely optimize an environment.
• 17:41
  They see the usage, but they are entirely blind to the business intent.
• 17:45
  Where we diverge, and what ultimately depends heavily on the listener's specific operational context, is how strictly one applies this five-step routine.
• 17:54
  For a short-lived temporary lab environment, like the one the author built, aggressive tagging, careful manual review, and shutting things down completely makes total sense.
• 18:03
  You want to prevent permanent ongoing billing for a temporary two-week experiment. But you maintain that for a massive twenty four/seven enterprise production environment, the calculus changes entirely. Yes.
• 18:15
  In a massive enterprise environment, the time cost of manual reviews must be weighed very carefully against the actual financial waste.
• 18:22
  Dragging multiple department heads into a conference room over fifty dollars worth of orphan disks is simply bad business logic.
• 18:28
  In those massive environments, accepting some automated savings plans, like the Reservations Azure advisor suggested, might be highly appropriate.
• 18:36
  Accepting a small, manageable margin of hidden waste might just be the cost of doing business at high speed. That is a vital distinction. Context dictates the strategy.
• 18:47
  But regardless of your scale or whether you work in technology or not, the mindset of seeking out invisible waste is universally valuable. Which actually brings us to a practical next step for anyone listening.
• 18:57
  You do not have to be an IT director managing cloud infrastructure to apply this thinking today.
• 19:03
  Think of the streaming services, the phone apps, the forgotten gym memberships quietly charging your personal credit card every single month. That is your personal hidden cloud bill. Right.
• 19:14
  The television is dark, but the cable box is still running up the power bill. Exactly. So here is your Friday homework. Run one simple inventory on one of your subscriptions before next Friday.
• 19:25
  Whether that means using a tool like the ARI module on an Azure subscription at work, or simply pulling up your personal credit card statement and highlighting the recurring charges.
• 19:34
  Find one thing you didn't know was there, one thing you are paying for but not actually using. It is the first critical step in moving from merely consuming technology to actively governing it.
• 19:45
  We invite you to read the full post free at intechornot.com. We'll see you next time as we continue to pull back the curtain on the digital world we're building.
• 19:53
  Just remember to check your invoice to see if you're paying for the curtain.