Why OpenAI's $157B valuation misreads AI's future (Oct 2024)

First, OpenAI’s valuation is a bit wild—$157B on 13.5x forward revenue? That’s Meta/Facebook-level multiples at IPO, and OpenAI’s economics don’t scale the same way. Generative AI costs grow with usage, and compute isn’t getting cheaper fast enough to balance that out. Throw in the $6B+ infrastructure spend for 2025, and yeah, there’s a lot of financial risk. But that said... their growth is still insane. $300M monthly revenue by late 2023? That’s the kind of user adoption that others dream about, even if the profits aren’t there yet.

Now, the “no moat” argument... sure, DeepSeek showed us what’s possible on a budget, but let’s not pretend OpenAI is standing still. These open-source innovations (DeepSeek included) still build on years of foundational work by OpenAI, Google, and Meta. And while open models are narrowing the gap, it’s the ecosystem that wins long-term. Think Linux vs. proprietary Unix. OpenAI is like Microsoft here—if they play it right, they don’t need to have the best models; they need to be the default toolset for businesses and developers. (Also, let’s not forget how hard it is to maintain consistency and reliability at OpenAI’s scale—DeepSeek isn’t running 10M paying users yet.)

That said... I get the doubts. If your competitors can offer “good enough” models for free or dirt cheap, how do you justify charging $44/month (or whatever)? The killer app for AI might not even look like ChatGPT—Cursor, for example, has been far more useful for me at work. OpenAI needs to think beyond just being a platform or consumer product and figure out how to integrate AI into industry workflows in a way that really adds value. Otherwise, someone else will take that pie.

One thing OpenAI could do better? Focus on edge AI or lightweight models. DeepSeek already showed us that efficient, local models can challenge the hyperscaler approach. Why not explore something like “ChatGPT Lite” for mobile devices or edge environments? This could open new markets, especially in areas where high latency or data privacy is a concern.

Finally... the open-source thing. OpenAI’s “open” branding feels increasingly ironic, and it’s creating a trust gap. What if they flipped the script and started contributing more to the open-source ecosystem? It might look counterintuitive, but being seen as a collaborator could soften some of the backlash and even boost adoption indirectly.

OpenAI is still the frontrunner, but the path ahead isn’t clear-cut. They need to address their cost structure, competition from open models, and what comes after ChatGPT. If they don’t adapt quickly, they risk becoming Yahoo in a Google world. But if they pivot smartly—edge AI, better B2B integrations, maybe even some open-source goodwill—they still have the potential to lead this space.

I experimented today with Aider (to get R1 involved) and had less success, but it might be that I don't have the workflow down.

I have found cursor can handle a .NET C# back-end using highly standard code structures very well. SignalR for networking.

I've created servers and very basic HTML visualization for three projects - a fairly simple autobattler (took a day), a web-based beat-em-up (2 days), and now a bit more ambitiously my dream RTS-MMO (3rd weekend running).

I started with concise MVP specifications including requirements for future scaling, and from these worked with the AI to make dot point architectural documents. Once we had those down I moved step-by-step, developing elements and tests simultaneously then having the agent automatically run the tests and debug. The test-driven debugging is the part that saved the most frustration, as the initial implementation was almost always broken, but leaving the agent to its own devices (tabbing in and typing "continue" when hitting Cursor's 25 tool call limit, sometimes for hours) the tests guided bug fixing and amazingly it got there fairly consistently, though occasionally it will go off the rails and start modifying the tests to pass or inventing unwanted functionality.

The code is as standard as possible, with the servers all organized identically API -> Application -> Domain I'm a professional game developer but without formal CS/programming training, so I'm aware of my requirements but not always how to implement them cleanly. I understand the code it writes which feels vital when it occasionally rolls a critical miss, but these projects would have taken me months without AI.