What is AI Infrastructure Debt?
In short: it's what happens when enterprises rush to deploy agents and set money on fire
(Here for AI news? Scroll to the very bottom for recent AI headlines you should know about.)
Cisco just released their third annual AI Readiness Index, surveying 8,039 senior business and IT leaders across 30 markets worldwide, and the results are... let’s call them “clarifying.”
While 83% of companies plan to deploy AI agents, only 13% have the foundations to make them work.
The rest? They’re about to learn an expensive lesson about the difference between ambition and capability.
Despite all the investment, all the pilots, all the executive enthusiasm, we’re watching the market split into two distinct camps: the prepared and everyone else.
We’ll dive into what separates them in a moment, but first! An announcement!
I’m piloting the first cohort of my Decision-Making with ChatGPT course next week on Friday (Oct 24) from 9 AM to 12 PM Eastern Time. There’s 2h of content covering a blend of cognitive science and ChatGPT tips and tricks, followed by 1h of Q&A. Both parts are live and you can learn more about the course on the sign-up page (bit.ly/decisiongptcourse):
My gift for subscribers of this newsletter is an exclusive set of promo codes (scroll to the bottom to find them).
And now, back to that AI infrastructure debt!
The New Debt That Nobody’s Talking About
Remember technical debt? That pile of shortcuts and “we’ll fix it later” compromises that eventually brought your digital transformation to its knees?
AI infrastructure debt is not technical debt’s cousin. It’s technical debt’s evolution: the modern form that emerges when organizations rush to deploy AI without building the foundation to support it.
Cisco defines it as “the accumulation of gaps, trade-offs, short-cuts and lags in compute, networking, data management, security, and talent that compound as companies rush to deploy AI.”
Think of it as the silent tax you pay for every “we’ll upgrade later” decision, every “good enough for now” compromise, every budget cut that seemed reasonable at the time: the governance you skipped, the data you never cleaned, the security you promised to “add later,” and the change management plan still trapped in a slide deck. Except now “later” is here, AI workloads are exploding, and your infrastructure is about to send you the bill.
In the report:
Only 26% of organizations have adequate GPU capacity
Only 34% feel their infrastructure is fully adaptable and scalable for AI
Only 31% feel equipped to secure AI agents they’re about to unleash
72% say AI talent costs are exploding their budgets
85% admit their networks can’t handle AI workloads at scale
Yet 83% plan to deploy autonomous AI agents (!!)
Think of it as trying to run a Formula 1 race with an engine duct-taped to a shopping cart. Sure, you’ve got AI, but can your infrastructure handle it when it actually tries to accelerate?
What Winning Actually Looks Like
Cisco calls the leading companies who consistently deliver value “Pacesetters” — what does their advantage look like? Those 13% who got this right are not just slightly ahead. They’re operating in a different reality.
Execution:
77% have finalized their AI use cases (vs. 18% overall)
97% deploy at sufficient speed and scale (vs. 41% overall)
95% have processes to measure AI impact (vs. 32% overall)
Infrastructure:
62% have robust GPU infrastructure (vs. 26% overall)
79% have fully integrated networks (vs. 34% overall)
61% have high resource allocation for compute (vs. 30% overall)
Results:
92% report increased revenue (vs. 63% overall)
91% report increased profitability (vs. 64% overall)
1.5x more likely to report gains across profitability, productivity, and innovation
This isn’t about having fancier toys. It’s about having infrastructure that doesn’t actively sabotage your AI ambitions. When your network can’t scale, your GPU capacity is a joke, and your data architecture is held together with hope, every AI initiative becomes a slog.
Pilots that never make it to production. ROI that never materializes. Talent that leaves because they’re tired of fighting your infrastructure instead of building solutions.
The Pacesetters’ Secret Sauce
Here’s what separates the Pacesetters from everyone else drowning in pilots:
They measure what matters.
While 68% of companies can’t tell you if their AI investments are working, 95% of Pacesetters track actual impact.
They’re 4x more likely to have moved beyond “wouldn’t it be cool if...” to actual production use cases.
You can’t improve what you don’t measure, so they build the ability to measure before they need to. (I have a guide for this here.)
They’re planning for the future.
When everyone else discovers their infrastructure can’t handle AI agents (spoiler: it can’t), Pacesetters will already have the capacity. 98% are designing for future demands right now, not scrambling when workloads explode.
They treat people as part of the system.
Only 36% of companies have change management plans for AI adoption. Among Pacesetters? 91%. They understand a truth that tech enthusiasts hate: your fancy AI is worthless if humans won’t use it.
AI Agents: The Incoming Tsunami
AI agents don’t just analyze — they act. They’ll write your code, talk to your customers, and make decisions while you sleep.
Within 12 months, 63% of companies expect agents to handle software engineering. Within three years, they’ll be controlling industrial robots and supply chains.
But here’s the rub: agents aren’t just another app you download. They need infrastructure that can handle continuous adaptive cycles, not just processing data but acting on it. Not to mention very skilled leadership.
Your network that barely handles current loads? It’s about to face workloads growing 50% in the next 3-5 years.
And security? Less than a third of organizations feel ready to control autonomous systems that’ll have access to your entire digital infrastructure. What could possibly go wrong?
The Executive Playbook
#1 Stop expecting one-and-done AI
Stop treating AI readiness as a one-time achievement. It’s not.
The Pacesetters prove it’s an ongoing discipline that determines whether you capture value or burn cash.
#2 Measurement matters
Face reality. If you can’t measure AI’s impact, you’re not doing AI… you’re doing expensive theater.
Build measurement first, moonshots second.
#3 Invest in foundations before you need them
Second, invest in foundations now. Even though per-call costs of AI are dropping, but agentic orchestration means more calls. Multi-agent workflows can multiply token usage exponentially, so cheaper per-call rates can hide higher overall spend. (Explained here.)
The infrastructure you need for AI agents isn’t the infrastructure you have. The choice isn’t whether to upgrade but whether to do it proactively or in crisis mode when your competitors are already three moves ahead.
#4 Don’t forget the humans
Stop pretending this is just about technology.
Without governance, clean data, and change management, your AI investments are just very expensive ways to annoy your employees and confuse your customers.
#5 Take AI infrastructure debt seriously
Every shortcut you take today compounds into tomorrow’s crisis.
The companies that acknowledge and address these gaps systematically are the ones turning AI into competitive advantage. The rest are just renting buzzwords.
The Question Leaders Should Be Asking
If you’re an enterprise leader, stop asking “Are we using AI?”
Start asking “Are we ready for AI at scale?”
Because the uncomfortable truth is that every day you don’t actively plan for the AI future, you’re accumulating AI infrastructure debt whether you realize it or not. Every quarter you defer that network upgrade, every budget cycle where you underfund your data center capacity, every security compromise you make to “move faster” …it’s all debt.
And like all debt, it compounds.
For CIOs and CTOs
CIOs and CTOs, you now have the language to make infrastructure investments sound less like IT housekeeping and more like what they actually are — strategic imperatives. “AI infrastructure debt” isn’t operational overhead. It’s risk management. It’s competitive positioning. It’s the difference between executing your AI strategy and watching it die slowly in committee.
The report gives you the data to have this conversation with your CFO. Point to the Pacesetters who invested early and are now capturing 1.5x more value. Point to the 83% planning to deploy AI agents on infrastructure that 54% admit can’t handle the load. Point to the 63% who expect 30%+ workload increases in the next 2-3 years.
For CEOs and Boards
CEOs and boards, when your tech leadership comes asking for infrastructure investment, understand what they’re really asking for. They’re asking for permission to build the foundation that determines whether your AI strategy is real or performative. The Cisco report just handed you the data to understand the stakes.
The Bottom Line
Value doesn’t chase AI investment. It follows AI readiness.
The 13% of Pacesetters aren’t lucky. They’re methodical. They plan early, build deliberately, and weave AI into their operating fabric instead of strapping it onto disarray.
Everyone else is about to learn that ambition without readiness is an expensive way to miss the point.
The market is splitting fast.
On one side: the few who invested in infrastructure, who know AI at scale demands stability, and who are now accelerating away. On the other: the rest, quietly accruing invisible debt that will soon come due.
The good news: you still have a choice. The bad news: that choice costs more every quarter you wait.
So stop treating AI infrastructure as an expense. See it as the entry fee for the game you already claim to play. Because showing up to a Formula 1 race in a shopping cart isn’t scrappy innovation.
It’s choosing to lose slowly.
Thank you for reading — and sharing!
I’d be much obliged if you could share this post with the smartest leader you know.
New Course! Decision-Making with ChatGPT
If you’re free next Friday (Oct 24) from 9 AM to 12 PM Eastern Time, join me for the first cohort of my Decision-Making with ChatGPT course.
This one is for the leaders who think they are too busy to become ChatGPT power users… it’ll give you back the time you invest in it and it’ll make you the AI champion the rest of your org desperately needs. We’ll have fun with decision science while you learn how to apply ChatGPT to boost your decision-making and get reusable decision frameworks, bias-reduction tools, and documentation templates. As always, it’s a high octane course with recordings available to all enrolled participants.
Promo codes: My gift to subscribers of this newsletter (thank you for being part of my community!) is $200 off the list price with the promo code SUBSCRIBERS.
If you’re keen to be a champion of the course (you commit to telling at least 5 people who you think would really get value out of it) then you are welcome to use the code CHAMPIONS instead for a total of $300 off — that’s an extra $100 off in gratitude for helping this course find its way to those who need it. (Honor system!) Note that you can only use one code per course, the decision is yours.
I know it’s short notice, which is why it’s likely to be a small cohort. There’s 2h of content covering a blend of cognitive science and ChatGPT tips and tricks, followed by 1h of Q&A. Both parts are live and you can learn more about the course on the sign-up page:
Enroll here: bit.ly/decisiongptcourse
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Yup, that’s the URL for my public speaking. “makecassietalk.com” Couldn’t resist. 😂
Use this form to invite me to speak at your event, advise your leaders, or train your staff. Got AI mandates and not sure what to do about them? Let me help. I’ve been helping companies go AI-First for a long time, starting with Google in 2016. If your company wants the very best, invite me to visit you in person.
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🗞️ AI News Roundup!
In recent news:
1. AI becomes the #1 enterprise data security threat
LayerX’s 2025 Enterprise AI and SaaS Data Security Report shows AI has overtaken email and file sharing as the top channel for corporate data leaks. Forty-five percent of employees now use GenAI tools, 67% via unmanaged accounts, and 77% paste data into them—82% from personal accounts—making copy/paste the leading, invisible exfiltration path that legacy DLP can’t detect. Why it matters: AI isn’t a future risk; it’s today’s largest data exposure point, demanding that CISOs and boards treat AI governance and browser-level DLP as immediate priorities.
2. Poisoning attacks on LLMs don’t scale with data size
As if that wasn’t fun enough, a new study from researchers at Anthropic, Oxford, the UK AI Security Institute, and the Alan Turing Institute finds that data poisoning attacks on large language models require only a near-constant number of poisoned samples—roughly 250 documents—to succeed, regardless of dataset or model size. The team trained models up to 13B parameters on Chinchilla-optimal datasets and found backdoors could be implanted even when those poisons represented less than 0.0002% of total tokens. The finding overturns assumptions that larger models are safer due to dilution, showing instead that as models scale, they become easier to poison. This highlights a major security concern for LLMs trained on open web data and underscores the urgency of developing scalable defenses.
3. AI disinformation reaches ‘fifth-generation’ threat level
More cheer! A new Research and Markets report warns that AI-powered disinformation has entered a “fifth-generation information operations” era, marked by machine-speed narrative creation, cultural adaptation, and semi-autonomous systems. Analyzing 2,347 campaigns across 137 countries, it finds that by 2026–2027, AI-generated propaganda will outpace global detection capabilities—posing a systemic threat to democracy, security, and economic stability. The study calls for international coordination and cognitive security infrastructure as advanced language models, synthetic media, and quantum-AI tools make disinformation nearly indistinguishable from truth.
4. Home robots edge closer to reality
MIT’s new 3D “steerable scene” system and Figure’s redesigned 03 humanoid mark major steps toward scalable, trainable home robots. MIT’s tool builds physics-accurate virtual homes to train AI at foundation-model scale, while Figure’s Helix-powered humanoid targets mass production in the tens of thousands and are set to enter the kitchen. The remaining gaps—like folding laundry—are shrinking as data realism, tactile sensing, and manufacturability improve. The convergence of simulation and scalable hardware is turning general-purpose robotics from lab demos into products poised to reshape labor, safety standards, and everyday life.
5. NVIDIA unveils DGX Spark, its smallest-ever AI supercomputer
NVIDIA launched the DGX Spark, a 1.2 kg desktop supercomputer delivering a petaflop of AI performance, personally handed to Elon Musk by Jensen Huang at SpaceX’s Starbase. Powered by the new GB10 Grace Blackwell Superchip and equipped with 128 GB unified memory, Spark lets developers fine-tune and run models with up to 200 billion parameters locally, no cloud needed.
Two of tech’s biggest personalities staging a hardware delivery photoshoot at a rocket launchpad is peak tech industry theater. It’s equal parts genuine milestone (desktop petaflop computing) and marketing genius (reminding everyone that NVIDIA decides who gets to play in AI). The 2016 DGX-1 delivery to OpenAI became legendary; this is Huang trying to recreate that moment with Musk instead.
6. Two new releases give AI agents more muscle
OpenAI and Google DeepMind both launched new AI agent platforms this week, pushing AI deeper into software creation and security. OpenAI’s AgentKit lets developers visually build, deploy, and evaluate multi-agent workflows, while DeepMind’s CodeMender uses Gemini models to automatically find and patch software vulnerabilities. Together, they mark a shift from AI as a helper to a hands-on developer—automating coding, debugging, and deployment at scale.
7. Google’s new Gemini model can browse the web like a human
Google unveiled Gemini 2.5 Computer Use, an AI model that can open a browser, click, scroll, and type to perform tasks on web pages that don’t have APIs. The system mimics human web navigation for use cases like UI testing or form-filling and is available via Google AI Studio and Vertex AI. Unlike OpenAI’s or Anthropic’s broader “computer use” tools, Gemini’s version runs only in a browser sandbox with 13 supported actions. The launch shows Google’s push to keep pace with agent-style AI that can actually operate the internet — not just read it.
8. Exec endorsement fuels AI adoption
A new MIT–McKinsey study finds that companies with strong executive backing for AI projects outperform peers by nearly fourfold. Top adopters report performance improvements 3.8 times higher than lagging firms, driven partly by greater digital investment but largely by CEO and board-level sponsorship. Forty-four percent of AI leaders now have executive or board support—double that of bottom performers and up 17 points from prior years—showing that senior commitment, not just technology spend, is a decisive factor in turning AI investments into measurable results.
9. Frontiers launches AI system to rescue lost scientific data
Frontiers has unveiled FAIR² Data Management, an AI-powered platform designed to make research data reusable, creditable, and AI-ready. The system automates curation, compliance, metadata generation, and peer review to turn raw datasets into certified, interactive, and citable outputs — addressing the problem that 90% of scientific data is never reused. By embedding FAIR (Findable, Accessible, Interoperable, Reusable) principles into an AI-driven workflow, Frontiers aims to accelerate breakthroughs in health, climate, and technology while giving scientists proper recognition for their data contributions.
10. The Geometry of Reasoning paper sheds light on the inner workings of LLMs
Duke researchers argue LLMs “reason” as smooth flows in representation space. Using a dataset that holds logic constant while swapping topics and languages, they find positions cluster by semantics, but velocity and curvature align by logical form—implying logic controls how embeddings move, not just where they sit. Why it matters: this offers a measurable, model-agnostic handle on reasoning, enabling new tools for interpretability, steering, and failure analysis.
11. Marvel joins DC in rejecting AI-made comics
At New York Comic Con 2025 last week, Marvel editor-in-chief C.B. Cebulski declared that the company “never used, will not use, and does not condone” generative AI in its comics — directly aligning with DC’s Jim Lee, who made a similar statement a day earlier. The back-to-back announcements signal a unified stance from both major publishers against AI-generated storytelling and art, with an industry built on superpowers and impossible physics taking a principled stand against augmented art. And the fact that Marvel says they explicitly check for AI tells like extra fingers shows how much AI art still sucks at hands… for now, the uncanny valley remains uncanny.
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