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| OpenAI and Anthropic IPOs: The Real Bet Isn’t a Trillion Dollars—It’s Cheap Tokens and the Harness That Turns Intelligence Into Work |
While headlines fixate on trillion-dollar valuations, the deeper story of OpenAI and Anthropic’s IPO path is about whether they can deliver intelligence at massive scale while owning the critical work layer above the models. This analysis cuts through the noise to examine what public investors are truly being asked to believe.
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Hey dear, I'm Rahul Sanaudwala, News Analyst, Founder & CEO of Tap2Call and OyeTools.
Most coverage of OpenAI and Anthropic’s moves toward IPOs collapses quickly into one question: Are these companies worth the enormous numbers being floated? It’s the trillion-dollar question everyone is asking, but it may be the least useful place to begin. The sharper question is what public investors are actually being asked to believe.
The answer is straightforward yet profound. Investors are being asked to believe that these labs can do two difficult things simultaneously: make intelligence cheap enough to serve at massive scale, and build the layer around that intelligence fast enough that companies prefer to rent the entire system rather than build it themselves.
That is the core bet. Cheap tokens paired with proprietary harnesses could equal a trillion-dollar business.
A token is raw intelligence—the commodity you buy by the meter. A harness is everything that transforms that raw intelligence into actual work: the files the model can access, the tools it can use, the permissions it holds, the memory it maintains, the evaluations that verify output, the routing between cheap and expensive models, and the workflows that define what “done” looks like.
Products like Codex and Claude for Code are harnesses. ChatGPT is evolving into one. Inside enterprises, nearly every serious AI initiative today is fundamentally a harness project. This explains why the IPO narrative carries such weight. The contest is not merely about who has superior models. It is about who can own the work layer sitting above the models.
What Actually Happened
OpenAI and Anthropic are both advancing toward public listings. The conversation has centered on sky-high valuations, but the strategic moves beneath the surface reveal a clearer picture. Both companies continue pushing inference efficiency, model capabilities, and enterprise integration. They are not just scaling raw intelligence—they are deliberately constructing the surrounding systems that make intelligence operationally useful at scale.
What Most Coverage Misses
Mainstream reporting often highlights eye-popping user value estimates, such as analyses suggesting heavy users of $200 plans receive $8,000–$14,000 in notional API value. The instinctive reaction is that these companies are burning cash unsustainably. That view misses the nuance.
API prices represent retail pricing, complete with markup and margins. They do not directly reflect the internal cost of serving tokens. When gross margins are substantial and labs aggressively improve inference efficiency through routing, caching, batching, distillation, chip utilization, and other optimizations, the $200 plans may function partly as strategic subsidies. They allow power users to consume heavily while the labs race down the cost curve.
The deeper signal is that OpenAI and Anthropic are not content being pure API companies selling raw intelligence indefinitely. Raw intelligence, once abundant, becomes less defensible. Value migrates to the layer that makes it useful—the harness.
Why This Really Matters
If tokens become truly cheap, the competitive battleground shifts. Intelligence itself matters—like electricity, bandwidth, or compute—but once commoditized, differentiation moves upstream to the systems built around it.
OpenAI and Anthropic want to sell the work surface: the operating layer that delivers results before users need to understand the underlying mechanics. This is not abstract. Codex succeeds not only because the model is intelligent, but because it operates within a harness that understands the software engineering job—accessing repositories, editing files, running tests, tracking changes, and iterating through workflows.
A model provides intelligence. A harness delivers work.
This creates a fundamental asymmetry. Labs possess frontier models, infrastructure, product talent, usage data, and speed. Enterprises possess private context: internal documents, real workflows, Salesforce fields that matter, approval processes, sources of truth, and historical nuances that explain why certain steps exist.
The struggle is which side converts its advantage into the superior harness.
Forward-deployed engineering is one key response. Beyond surface-level views of labs becoming consultancies, this approach lets them embed inside customer organizations, map workflows, connect tools, and adapt the harness to specific realities. Success here turns generic products into sticky, company-specific systems. Switching costs rise dramatically—not because of the model, but because workflows are rebuilt around one lab’s way of operating.
Scenario Analysis
Best Case: Labs successfully manage token costs, compete on price with open-source alternatives, leverage their own models to accelerate internal improvements, and build harnesses compelling enough that most companies choose to rent rather than own. Recursive self-improvement becomes practical and iterative: better models help labs enhance code, evals, routing, and inference faster than customers can respond. Scale compounds advantages. Harnesses become dominant, creating powerful lock-in and high-margin software businesses.
Likely Case: A mixed outcome where labs capture significant value through improved efficiency and strong harness products, while sophisticated enterprises build their own layers for core workflows. Labs remain large, profitable companies with substantial moats in consumer and mid-market segments, but the highest-value enterprise work layers see more contestation. Valuations reflect strong but not absolute dominance.
Worst Case: Companies rapidly learn to own their harnesses—controlling routing, context, evals, and workflows—treating labs primarily as intelligence suppliers. Token margins compress as costs fall and competition intensifies. Labs deliver massive utility but capture less of the total workflow value. They become highly successful infrastructure providers, yet the trillion-dollar premium tied to owning the work layer evaporates.
What Happens Next
The S-1 filings (when they arrive) will offer crucial signals. Watch these metrics closely:
- Whether heavy users become cheaper to serve over time
- Improvement in gross margins as usage scales
- Whether enterprise deals emphasize scalable software or ongoing custom deployment labor
- Evidence that customers are building real, persistent workflows inside the products
- Whether forward-deployed engineering serves as a bridge to product-led experiences or remains a structural necessity
These details will reveal if the businesses are evolving into high-margin work-layer platforms or remain more token-centric.
The Practical Question for Companies and Individuals
For organizations, the strategic fork is clear: Are you renting the harness or building toward owning it? Using the tools is obvious. Owning the layer that decides which model serves which job, maintains context, defines evals, and enables seamless swapping is where durable advantage lies. Almost no company should train frontier models, but owning the orchestration layer is different.
For individuals, the shift is already underway. Prompting is becoming table stakes—thin leverage. The higher-value skill is harness building at a personal scale: clearly defining recurring jobs, providing rich context, connecting tools and files, verifying outputs, and iteratively improving the system.
Cheap intelligence is coming regardless. The question is who knows how to harness it effectively.
The OpenAI and Anthropic IPOs represent the first major public test of this thesis: Can the labs make tokens cheap enough and build harnesses fast enough to own the work layer of the AI era? Or will enterprises use abundant intelligence to construct their own layers and retain more value?
The harness is the engine that makes the token economy truly valuable. Whoever controls it holds the dominant position.
I’ll be watching the S-1 details closely as they emerge, along with early enterprise adoption patterns. This story is just beginning to unfold, and the next chapters will clarify the shape of the AI economy for years ahead.
Conclusion
The trillion-dollar conversation around OpenAI and Anthropic’s IPOs is understandable but incomplete. The enduring value will flow to those who best convert abundant intelligence into reliable work. Whether through lab-controlled harnesses or enterprise-owned layers, the real winners will be those who master the integration of context, workflow, and orchestration. The technology is moving fast—those who build thoughtful strategies around it will capture disproportionate returns. Stay focused on the harness, not just the models.
5 FAQs
- What is the "harness" in the context of AI companies like OpenAI and Anthropic? The harness refers to the full system surrounding the raw model—tools, memory, permissions, evals, routing, and workflows—that turns intelligence into practical work outputs.
- Are the $200 AI plans from OpenAI and Anthropic financially sustainable? They may function as strategic investments or subsidies if internal serving costs are significantly lower than retail API prices and efficiency improvements continue rapidly.
- Why is forward-deployed engineering important for these labs? It helps overcome the labs’ lack of private customer context by embedding teams to map workflows and customize harnesses, increasing stickiness and value.
- Should companies build their own AI harnesses? Sophisticated organizations should aim to own the orchestration layer (routing, context, evals) while using labs as intelligence suppliers, rather than training frontier models.
- What should investors look for in the upcoming S-1 filings? Focus on trends in serving costs per heavy user, gross margin expansion, the nature of enterprise deals (software vs. services), workflow adoption inside products, and the role of professional services.