Scale & Strategy
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This is Scale & Strategy, the newsletter that catches you up like a blue shell in Mario Cart.
Here’re the quick highlights from the week:
- AI coding tools are pulling in serious capital.
- OpenAI is starting to feel the weight of its own ambition.
AI coding tools are pulling in serious capital.
Lovable, the Sweden-based “vibe coding” platform, is reportedly raising at a $6 billion valuation, per Forbes. That jump follows its $1.8 billion valuation after a $200 million Series A in July.
Cursor wasn’t far behind on the headline chase. The company just announced a $2.3 billion round that pushed its valuation to $29.3 billion, more than 12x where it stood in January.
Investor heat isn’t the whole story. Both platforms are seeing real usage.
Lovable has moved fast since launching in November, clearing $100 million in subscription revenue. CEO Anton Osika says the platform is approaching 8 million users, with roughly 100,000 new products created each day.
Cursor says it has passed $1 billion in revenue and now serves more than one million users.
Why the traction? These tools actually make engineers faster. Thomas Randall, research director at Info-Tech Research Group, put it simply to The Deep View: they cut cognitive load by baking generation, debugging, and refactoring into environments developers already live in, like VS Code. The tech works, and it saves time. That’s the entire pitch.
And the ceiling is still high. By lowering the barrier to entry for non-software engineers – data scientists, IT teams, low-code operators – the total addressable market expands as the product improves.
That runway won’t last forever. Medium-term, these companies will need real differentiation, likely through enterprise integrations, compliance capabilities, or other defensible features. Long-term, profitability will depend on reducing reliance on outside model providers, especially as those same providers start rolling out their own coding tools.
There’s a downside forming under the surface. The rise of AI-assisted coding is reshaping the job market for young developers. A Stanford study from August found employment for software engineers ages 22 to 25 is down nearly 20% from its late-2022 peak. A July Microsoft study listed data scientists and web developers among the most exposed roles.
And while these tools can supercharge productivity, they’re not replacements – at least not yet. They hallucinate, they make mistakes, and without enough human oversight, bugs slip through. The tools are powerful, but they’re not foolproof.
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OpenAI is starting to feel the weight of its own ambition.
The company announced a multiyear partnership with Intuit on Tuesday, giving the finance giant access to OpenAI’s APIs and putting Intuit products in front of ChatGPT’s massive user base. The deal is worth more than $100 million over an undisclosed window.
It’s another example of OpenAI’s do-everything-everywhere strategy. The company wants a presence in every corner of the stack: consumer apps, enterprise tooling, finance, browsers, you name it. The problem is that this horizontal land grab is brutally expensive.
OpenAI has already committed to more than $1 trillion in infrastructure spending over the next eight years through deals with Oracle, SoftBank, Nvidia, Amazon and others. That scale of capex is rare air, and it’s already putting pressure on partners. The Financial Times reports Oracle may be underwater on its $300 billion agreement with the company.
The burn isn’t just in future buildouts. Operating the current models is costly. Tech blogger Ed Zitron reported that OpenAI’s inference costs from Q1 to Q3 hit $8.67 billion, against revenues of $4.33 billion over the same period. Altman, for his part, claims revenues are “well above” $13 billion with an annualized run rate of $20 billion by year-end. Even if the top-line number is closer to Altman’s, the company is still spending far faster than it’s earning.
The bigger issue is strategic: OpenAI is tying its long-term survival to massive infrastructure bets and deep reliance on other tech giants. Scott Bickley of Info-Tech Research Group told The Deep View that the economics may break before the vision materializes. With AI valuations stretched and bubble fears high, any correction could hammer data center expansion plans. In his view, cost to serve alone could collapse the model.
A more disciplined approach may be overdue. Rather than trying to be a universal platform at trillion-dollar scale, OpenAI might be better off doubling down on the segments where it’s actually converting value, like enterprise partnerships. Anthropic is already proving that path can be both lucrative and sane.
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