The article explains how unglamorous, overlooked industries—funeral homes, parking lots, trash hauling—generate outsized profits because competitors simply ignore them. Steve Ross built a media empire by rolling up these "invisible" businesses, and companies like Constellation Software continue this playbook today by acquiring niche software firms others dismiss as too small or slow-growing.

• Invisible companies exist in plain sight because no one searches for them: they're unknown, their data is private or buried, they're assumed to be mature dead-ends, or they carry social stigma. Since potential competitors don't know what they're missing, they never compete, leaving profits untouched.
• Standard business filters—looking for large markets, rapid growth, novel technology—screen out smaller, operationally mundane businesses that are quietly profitable. The opportunities aren't hidden; the algorithm most investors use just skips over them.
• Constellation Software has returned 34% annually since 2006 by buying boring vertical-market software businesses (marina management, funeral home records, library cataloging) that venture capitalists and other acquirers abandoned as too small. The company estimates 38,000+ similar businesses still exist.

Leaked SEC filings show OpenAI’s revenue jumped from $3.7 billion in 2024 to $13.07 billion in 2025 but its R&D alone cost $19.18 billion, including $10.59 billion paid to Microsoft. High compute, sales, and marketing expenses drove its operating loss to $20.92 billion in 2025, about 160 percent of revenue, even as it targets profitability by 2030.

• OpenAI's revenue nearly quadrupled to $13.07 billion in 2025, but operating losses still hit $20.92 billion, about 160% of revenue.
• R&D costs alone ($19.18 billion) exceeded total revenue, with over half ($10.59 billion) paid to Microsoft.
• Compute costs and sales/marketing spending both roughly tripled year-over-year, driving the overall cost explosion.
• Losses as a share of revenue improved from 237% to 160%, the main evidence OpenAI cites for its 2030 profitability target.

The article analyzes the unit economics of large language models (LLMs), focusing on the compute costs associated with training and inference. It discusses how companies like OpenAI and Anthropic manage their financial projections and cash flow, emphasizing the need for revenue growth or reduced training costs to achieve profitability.

• Inference costs are falling faster than training costs, so gross margins on deployed models improve over time even as frontier training runs get more expensive.
• OpenAI and Anthropic's path to profitability depends on either scaling revenue much faster than compute spend or finding ways to cut training costs, since current cash burn is dominated by training rather than serving models.
• Reported "profitability" claims from these labs often exclude massive R&D/training expenditures, making headline numbers misleading about true unit economics.