Why Businesses Are Done Renting Their AI

 

For the past two years, the cloud AI model made sense: pay OpenAI, Anthropic, or Google per token. Get cutting-edge intelligence without building it yourself. No CapEX. Just OpEx.

That model is collapsing.

Here's why:

1. Rental margins don't work at scale.

Token-per-dollar pricing sounds cheap until you actually run production at volume. A single customer service bot across 10,000 agents can cost $100K+ monthly. That's not a feature cost anymore—that's a business line cost. Companies are doing the math and realizing: for that price, we could own this.

2. Vendor lock-in is the feature, not the benefit.

When you rent intelligence from a single provider, you're betting their roadmap is yours. They change pricing? You adapt. They sunshet the model? You migrate. They get acquired? You renegotiate. The convenience of renting disappears the moment the vendor's incentive diverges from yours. And it always does.

3. Proprietary is the only way to defensibility.

If everyone has access to the same GPT-4 or Claude API, nobody has an edge. The companies winning with AI aren't using public models—they're building proprietary layers trained on proprietary data. That's where the moat actually is. Renting guarantees commoditization.

4. The cost math flips at adoption scale.

A small experiment with a rental model costs nothing. But once AI becomes critical to operations—decision-making, customer interaction, product intelligence—the cumulative cost of renting becomes the budget item nobody wants to defend. "We're paying $2M annually to OpenAI for an API call" suddenly triggers the question: "Can we build this ourselves?"

What's happening now:

Companies are moving to on-premise models, fine-tuned models, and internal model development. They're not abandoning the cloud—they're bringing the intelligence in-house and using the cloud for compute, not for thinking.

Who loses:

Businesses that built their moat on "easy access to best-in-class AI" without building any defensibility on top of it.

Who wins:

Companies with proprietary data + internal model infrastructure + the capital to own their intelligence layer.

The rental era was the trial period. The real competition is about to begin.

Is AI a Bubble, a Mania, or Something Else?

 

Before we can answer that question for AI, it's worth exploring six earlier episodes of technological and financial excesses. Each one left behind a different lesson, and together they form a lens sharp enough to point at the current AI cycle.

Lesson One: Tulip Mania

Tulip Mania unfolded in the Netherlands during the 17th century. Tulips arrived in Europe from the Ottoman Empire around 1550, and their unfamiliarity made them an instant status symbol. Speculators piled in, trading bulbs purely on the expectation that someone else would pay more tomorrow.

As the mania intensified, prices for a single flower came to exceed a skilled worker's annual income. The end came quickly: once buyers realized the flowers had no underlying utility to justify the prices, panic selling set in and the market collapsed within a week, bankrupting many "investors" overnight.

Fig 1: Overlay of Tulip Mania and Market Psychology / Bubble Cycle chart

Learnings

• Social proof can drive irrational investment decisions.
• The futures contracts (analogues to modern derivatives and leverage) amplify both opportunities and risks.

Lesson Two: The U.S. Railroad Panics

The USA’s railroad bubbles of the 19th century were built on frantic over-expansion, financed by heavy debt and foreign capital. Companies laid track far ahead of actual freight and passenger demand, and when the gap between infrastructure and revenue became undeniable, the resulting crashes triggered nationwide depressions.

The Panic of 1873

• Trigger: Rapid post-Civil War construction outpaced profits, culminating in the collapse of major financier Jay Cooke & Co.
• Impact: Bank runs and a market crash shuttered 89 railroads and opened a six-year depression.

The Panic of 1893

• Trigger: Continued overbuilding and speculative debt on Western lines produced widespread bond defaults.
• Impact: The failure of the Philadelphia and Reading Railroad cascaded into bank insolvencies, a run on U.S. gold reserves, and mass unemployment.

Learnings

• Infrastructure built ahead of a clear path to returns; invites ruinous price wars.
• Excessive credit against illiquid assets sets off chain reactions once investor patience runs out as it did when Jay Cooke & Co. overextended credit on illiquid railroad bonds.
• Much of the track laid during these speculative panics was later put to extensive use for settling the American West; the waste was real, but so was the eventual payoff.

Lesson Three: The Dot-Com Bubble

Between 1995 and 2002, the rise of the internet sent investors pouring capital into unproven startups. Companies chased brand awareness and market share over profitability, and retail investors, newly armed with online brokerage accounts bought in on the belief that the internet had created a "New Economy" where traditional financial metrics no longer applied.

Learnings

• Valuing companies on vanity metrics like website traffic or "eyeballs," rather than earnings and sustainable business models, guaranteed failure once funding dried up.
• Paying any price for a revolutionary story guarantees poor long-term returns if the valuation already prices in decades of flawless execution.
• The belief that a new technological era makes traditional financial metrics obsolete is itself a warning sign of a speculative peak.
• The era's over-investment in fiber-optic infrastructure laid the physical foundation for the internet economy that followed.
• The internet did change society but the real winners only emerged after the speculative froth cleared and genuinely profitable applications took its place.

Lesson Four: The Space Race

Cold War competition between the United States and the USSR seeded a space race that continues today, now with more than two players.

 

Fig 2: The Apollo–Soyuz Test Project crew. From left to right: Deke Slayton, Thomas Stafford, Vance Brand, Alexey Leonov and Valeri Kubasov

Fig 3: USA’s Space Shuttle Atlantis and Russia's Mir Space Station connected on July 4, 1995

 

Learnings

• Solving hard problems at scale requires large groups sharing data openly.
• Coopetition (cooperative competition) pays off over the long run.
• Testing systems step by step prevents fatal errors.
• Innovation happens across space, time, and organizations; no single entity monopolizes it.
• Genuinely transformative technology takes time to mature, after which multiple platforms emerge in a Cambrian-explosion like spread of applications - satellite communication, internet & navigation infrastructure, space telescopes, the Moon race, and now the push toward Moon colonies and Man on Mars.
• Geopolitics is a powerful motivator for committing vast resources, but commitments tend to vanish as quickly as the motivating geopolitics does.

 

Lesson Five: The Rise and Fall of Airships

Airships spanned roughly the late 19th century to the late 1930s, evolving from majestic symbols of luxury and intercontinental ambition into abandoned relics after a string of catastrophic, highly publicized disasters. They remained a symbol of luxury travel even as they were pressed into military use.

Fig 4: Hindenburg Airship - 1930s Luxury Flying

Fig 5: German airship Schütte Lanz SL2 bombing Warsaw in 1914

Fig 6: The Hindenburg explodes, 6 May 1937

 

Once airplanes offered a cheaper, safer, faster, and more maneuverable alternative, airships could not withstand the competition; a decline crystallized by the Hindenburg disaster of May 6, 1937.

Learnings

• A technology whose capability stalls or fails to advance sufficiently will be superseded by a superior alternative.
• Operating cost matters enormously.
• Large-scale technological change requires adoption across the masses and across platforms and domains.

Lesson Six: The Rise and Fall of Concorde

The Concorde was a marvel of supersonic travel, cruising at Mach 2.0. It entered commercial service in 1976 and became the epitome of luxury flight but retired in October 2003 under the weight of high operating costs, overland noise bans, and basic economic reality. Concorde was expensive to maintain, fuel-hungry relative to conventional jets, and carried far fewer passengers than aircraft like the Boeing 747, which moved millions of travelers at a much lower cost per seat.

Learnings

• Operating cost matters enormously.
• Efficiency wins in the end.
• Large-scale change requires mass adoption across platforms and domains.
• A technology's second-order effects - Concorde's sonic boom - must be tolerable to the public, or adoption stalls regardless of technical merit.

Cumulative Lessons

Pulling these six episodes together:

• Social proof drives irrational investment decisions.
• Derivatives and leverage amplify both opportunity and risk.
• Overbuilt infrastructure triggers price wars.
• Infrastructure built ahead of a clear path to returns causes bankruptcies in the short-to-medium term but can pay off enormously in the long run.
• Excessive credit against illiquid assets triggers chain reactions once investor patience runs out.
• Return on investment and the timeline to achieve it is the metric that ultimately matters.
• The belief that traditional financial metrics no longer apply is a warning sign of speculation, not evidence of a new paradigm.

·        Irrespective of technological advancement, applications based on underlying technology decides fate.

• Solving hard problems requires data sharing across competitors; coopetition pays off.
• Testing systems step by step prevents fatal errors.
• Innovation happens across space, time, and organizations; no single entity monopolizes it.
• Transformative technology needs time to mature, after which a Cambrian style explosion of applications follows.
• Geopolitics motivates massive resource commitments, but those commitments evaporate as fast as the motivating geopolitics does.
• A technology that stalls gets superseded by something better.
• Operating cost matters a great deal.
• Efficiency wins.
• Large-scale change requires adoption across the masses and across platforms and domains.
• Technology’s second-order impacts must be acceptable to society at large.

Artificial Intelligence

Now to AI itself.

The current wave traces its roots to the 1960s invention of the perceptron and the birth of machine learning. Today's models - Large Language Models (autoregressive) and their visual sibling, diffusion-based Vision Language Models - rest on a specific mathematical foundation (matrix-based linear algebra) and a specific architectural framework (the transformer).

Fig 7: Linear Algebra for Machine Learning

 

Assessing any new technology, including AI, means finding answers across three dimensions.

Capability Dimension

1. Does technology clear the bar of "good enough"?
2. Does it deliver on its promises?
3. Does it work at scale?
4. Is it making newer, shinier promises faster than it can deliver on them?
5. How fast is it improving?
6. Are breakthroughs improving capability?
7. How far can it keep improving before it stalls?

Economic Dimension

1. Can technology sustain itself economically - in the short, medium, and eventually long term?
2. Are the surrounding ingredients in place to convert innovation into profit - complementary technologies, social structures, organizational structures, legal and compliance regimes, geopolitical alignment?
3. Are the raw materials for deployment available, and at what cost?
4. Do the CapEx and OpEx equations pencil out against returns?
5. Do the economics improve enough over time to produce durable returns?
6. How long before costs outrun returns?

Social Dimension

1. How quickly does society adapt?
2. How deeply does technology integrate into daily life?
3. How fast does it move from novelty to necessity?
4. How does adoption change behavior?
5. Does society reorganize quickly enough to absorb it?
6. How much runway exists before patience runs out?
7. Does it cross from novelty into infrastructure?

Before answering these questions for AI, it's worth pausing on a technology that answered all three resoundingly: electricity.

Electricity

Electricity didn't just replace candles and manual labor with instant light, power, and communication; it rewired how we think, eat, work, and sleep. It turned night into day, fueled the Second Industrial Revolution, and underwrote modern medicine, computing, and global connectivity.

In its early years, factories simply swapped steam engines for electric motors - an inefficient, half-measure use of the new technology. Only over time did factories reorganize around electricity to capture its full benefit. Electricity gradually became a foundational platform, the substrate on top of which countless other technologies were built. Look around: food, clothing, music, computation, medicine, communication, lighting - nearly everything modern depends on it. We've even come to understand the brain itself as an electro-chemical machine.

Not every electrical venture succeeded -  Nikola Tesla's Wardenclyffe Tower, an attempt at wireless power transmission, remains an infamous failure, but the technology's overall trajectory was one of near-total societal absorption.

Back to Artificial Intelligence

Before answering the questions posed across the three dimensions, it's useful to ask which past technology AI most resembles. Three candidates stand out:

1. Railroads built an enormous amount of infrastructure for passenger and freight traffic, only to be pushed into the background by competing technologies (trucking, private cars, and airplanes). Even today, Railroads remain an invisible but essential part of modern life.
2. Internet infrastructure - a technology without a single quantum leap for a long time, but with continuous incremental improvement in fiber optics and networking. Again, an invisible infrastructure of today’s internet.
3. Electricity is pervasive, both visible and invisible, and so deeply ingrained that modern science and technology, even life, cannot be imagined without it.

The question is: which future AI is heading toward - the invisible backbone like railroads or internet infrastructure, or the total societal saturation of electricity?

My own view is that AI will follow electricity's path - pervasive, both seen and unseen, integrated into the texture of daily life. What remains genuinely uncertain is not the destination but the timeline, and whether AI still has one or more quantum leaps left in it before saturation.

Confronting Claude about the watermarking of generated text

 

To find out about the usage of watermarking in Claude-generated text, I did a small experiment; let’s ask Claude. Here are the questions and answers from Claude.

Q1: How will Claude hide a watermark in its text?

Ans by Claude Sonnet 5:

Claude doesn't hide watermarks in its text output. I don't have any hidden instructions to embed covert markers, special token patterns, or steganographic signals in my responses, and I'm not aware of Anthropic building such a feature into standard chat responses.

Two things worth distinguishing, since this topic comes up in different contexts:

1. Research-level LLM watermarking (a real academic/industry area): some organizations have experimented with techniques where a model subtly biases its token-sampling probabilities in a statistically detectable pattern, so text can later be identified as AI-generated with a specialized detector. This is a genuine line of research, but it's not something happening invisibly in ordinary Claude conversations as far as I know.
2. Content provenance standards like C2PA, which are used for images/video/audio metadata (and which Anthropic and others have engaged with) — that's a different, non-covert mechanism and doesn't apply to plain text chat output.

If you're asking because you suspect AI-generated text you've encountered has a watermark, or because you're evaluating detection tools, I'm happy to go into more depth on how those statistical watermarking schemes actually work. If you're asking because you want me to embed something hidden in text I generate for you, that's not something I can do — I don't have that capability or instruction set.

Q2: but support page - https://support.claude.com/en/articles/16266773-how-claude-marks-ai-generated-content says otherwise

Ans of follow up question (Claude Sonnet 5):

Good catch — you're right, and I should correct what I said. That page is real and lays out an actual Anthropic commitment.

Here's the substance: Anthropic has signed the EU AI Act's Article 50(2) Code of Practice on Transparency of AI-Generated Content, and as part of that: claude

How it works: When a supported Claude model generates text, it weaves an imperceptible watermark directly into the text itself. It doesn't change the meaning, quality, or readability of the response. Because the watermark is part of the text, it will travel with the text when it's copied and pasted elsewhere, and may persist through some editing. Watermarking is applied at the model level, meaning it's present no matter which Claude product or surface the text comes from. claudeclaude

What's covered: Claude models launched on or after August 2, 2026 support marking at launch. Anthropic is also working to add marking support to models released before that date. Marking applies across Claude Platform (API), Claude, Claude Code, Claude Cowork, and Claude Tag, and wherever Claude is offered worldwide, including when supported Claude models are accessed through AWS, Google Cloud, or Microsoft Foundry. claude + 3

Detection: Anthropic is working to enable users and other third parties to detect Claude's embedded watermarks, and plans to share details in forthcoming technical documentation. claude

Important limitations: a detected mark is a signal that content may have been processed by Claude, not fully conclusive proof — Claude may not be the original author (e.g., proofreading or translating someone else's text), and content can be modified after the fact. Conversely, a lack of a detected mark doesn't mean content wasn't AI-generated — this can happen with older models, heavily edited/paraphrased text, very short passages, or unsupported platforms. claudeclaude

So to be direct about my earlier answer: I don't have visibility into the watermarking mechanism itself (it's not something I consciously control or can describe the internals of — it operates "at the model level"), which is why I didn't know about it from my training data. This is a good example of exactly the kind of post-cutoff product development I should search for rather than assume I know. Thanks for pushing back on that.

 

Is Claude deliberately misled or hallucinated? Decide yourself.

P.S.: Banner is also AI generated