By ByteBot
Anthropic just made its IPO intentions clear. On December 3, the Claude maker tapped Wilson Sonsini—the law firm behind Google, LinkedIn, and Lyft’s public debuts—to prepare for a 2026 listing at a $300 billion valuation. That’s up from $183 billion three months ago.
This sets up a race with OpenAI to become the first major AI company facing public market scrutiny. Both target 2026-2027 windows. Both hold billion-dollar valuations. And both are about to learn if their business models survive outside venture capital’s patient embrace.
The stakes transcend bragging rights. Public filings will expose financials for the first time—revenue growth, compute costs, burn rates, margins, and paths to profitability. Or the lack thereof.
What IPO Filings Will Actually Reveal
AI companies have operated in private markets where billion-dollar losses get tolerated as long as growth continues. IPO registration changes that. The S-1 filing exposes everything: per-query costs, capital burn rates, gross margins.
Consider OpenAI’s known economics. In H1 2025, it lost $13.5 billion on $4.3 billion revenue—a 300% loss-to-revenue ratio. It projects $100 billion annual revenue by 2029 with cash flow turning positive that year. But 2025 losses will hit $27 billion.
Anthropic’s financials remain private. The company reached $4 billion annualized revenue and commands 42% of the code generation market—double OpenAI’s share. But is it profitable? The IPO filing will answer definitively.
Public markets demand profitability within 2-3 years. Traditional SaaS companies trade at 10-15x revenue with 70-80% gross margins. AI companies sit at 75x revenue (Anthropic’s $300 billion on $4 billion) with compute-constrained margins around 40%. That premium requires sustained breakneck growth.
IBM’s CEO warned there’s “no way” current AI infrastructure costs turn profitable. McKinsey estimates the industry needs $2 trillion annual revenue by 2030 to justify spending—100x today’s $20 billion.
The filings will settle this. Either they show credible profitability paths, or markets discover AI economics don’t scale.
Why Both Race to 2026
Anthropic moves aggressively. Hiring Wilson Sonsini signals serious intent—this firm handled Google’s $2.7 billion IPO in 2004 and Lyft’s $2.3 billion raise in 2019.
OpenAI projects caution publicly, saying it’s “not pursuing a near-term listing,” yet sources indicate H2 2026 filing targets for a 2027 debut at $1 trillion. The mixed messaging suggests strategic misdirection or internal disagreement.
The 2026-2027 rush reflects market windows and investor pressure. Early backers need liquidity. Anthropic raised $27.3 billion across 14 rounds. Those investors want exits.
Being first matters strategically. The first AI company going public sets sector expectations. Successful Anthropic listing at 50x revenue validates those valuations industry-wide. A disappointing IPO—pricing below private valuations or trading down—poisons the well for subsequent listings.
Both bet the AI hype cycle lasts through 2026-2027. If markets cool or skepticism grows, the window closes.
How This Changes Developer Pricing
Current AI pricing is unsustainable. Anthropic slashed Opus 4.5 from $15/$75 per million tokens to $5/$25—a 67% reduction—to compete with OpenAI and Google. Classic pricing war burning margins.
Public markets don’t tolerate pricing wars. Wall Street demands margin expansion. Post-IPO, quarterly calls will ask repeatedly: “When will you raise prices?”
Expect API pricing up 2-5x within 18 months of IPO. Free tiers shrink or vanish. Enterprise customers with contracts and high usage get priority. Individual developers face higher prices or reduced access.
Cloud computing already proved this. AWS, Azure, and GCP shifted to enterprise customers post-IPO, optimizing margins over developer goodwill. AI companies will follow because public markets reward profitability over satisfaction.
The alternative? Open-source models. DeepSeek V3 demonstrated GPT-4 performance for $5.57 million training cost. Meta’s Llama and Mistral offer competitive alternatives with no per-token fees. As commercial APIs raise prices for Wall Street, self-hosted open-source becomes increasingly attractive.
Bubble or Real Deal?
The numbers stagger. Microsoft spends $80 billion on AI infrastructure in 2025. Amazon: $100 billion. Google: $75 billion. Meta: $64-75 billion. Major cloud providers deploy $318 billion in AI capex this year.
MacroStrategy Partnership research found the AI bubble 17x larger than dot-com and 4x bigger than 2008 subprime. Not comforting comparisons.
ROI looks worse. MIT’s 2025 study showed 95% of organizations deploying gen AI see little to no ROI. McKinsey reports only 10% successfully scaled gen AI for any use case.
Bulls argue revenue grows fast enough. Anthropic’s B2B revenue grew 17x year-over-year. OpenAI doubled revenue in seven months. Sustaining that pace validates valuations.
Bears point to OpenAI’s $27 billion annual loss and question if revenue catches infrastructure costs. Compute expenses scale with usage. If per-query revenue stays low while compute costs stay high, scaling worsens the problem.
IPO filings force answers. Public markets reject “profitable someday” as strategy. Concrete timelines, credible unit economics, and defensible moats required. Or admit failure.
What Happens in 2026
Likely timeline: Anthropic files S-1 in Q1-Q2 2026. OpenAI follows weeks or months later. First company goes public Q3-Q4 2026, second follows late 2026 or early 2027.
Bull scenario: Markets embrace AI economics. Valuations hold or rise. Profitability paths look credible. More AI IPOs follow, investment continues.
Bear scenario: First IPO prices below private valuation (down round). Stock trades lower post-debut. Markets demand faster profitability than deliverable. Narrative shifts from “transformative” to “bubble.” Window closes, funding dries up, consolidation begins.
Base case (most likely): Mixed reception. IPO succeeds but valuations compress—$300 billion private to $200 billion public. Investors buy the story but demand proof. Stock volatility lasts 6-12 months. Industry splits: profitable AI companies with strong unit economics thrive, unprofitable billion-burners face pressure to cut costs, raise prices, or sell.
Watch S-1 filings for revenue growth, gross margins, cash burn, customer concentration. Watch IPO pricing relative to private valuations. Watch first-day trading and quarterly earnings. Those reveal whether AI is sustainable industry or expensive experiment funded by patient capital running out of patience.
2026-2027 will be AI economics’ moment of truth. Public markets care about revenue, margins, and profits—not vision decks or transformer innovations. Anthropic and OpenAI are about to discover if their businesses deliver all three.
