Perhaps most telling in this entire report is Table 1. It shows that the non-work has grown 8x in 1 year, whereas work has only ~3.4x. Considering that non-work related usage of ChatGPT now makes up 73% of the requests, ChatGPT is very much in the consumer market, despite substantial marketing of LLM products in a professional context and even as much as compelled usage in some corporations.
Since many consumers are typically relatively tight-fisted in the b2c market, I don't think this bodes well for the long-term economics of the market. This may explain the relatively recent pivot to attempt to "discover" uses.
OpenAI makes a profile of you based on your chat history and people are far more personal with these things than Google search. It's gonna be a goldmine when they decide to use that profile to make money.
Ads won't be slapped onto answers, my guess is that they will be subtly and silently inserted into them so that you don't even notice. It won't always be what you see either as companies, political groups, and others who seek to influence you will pay to have specific words/phrases omitted from answers as well.
AI at this point is little more than a toy that outright lies occasionally yet we're already seeing AI hurting people's ability to think, be creative, use critical thinking skills, and research independently.
And friendster at one point had over 100m users. A gross margin (and more importantly, positive cash flow) business is more important than users. This data is not a good indicator of either.
They have literally hundreds of millions of users that are completely free. Not google search or facebook free, but free free, and only suffer a few billion in losses. Inference is cheap and their unit economics is fine. There is literally no business that would be making profit under those constraints. If they need to make profit, they can implement ads and that will be that.
In 2024 (when customer mix was more favorable) they lost 5B on 10 in forward looking ARR.
They aren't pulling an Amazon snd balancing cash flow with costs. They're just incinerating money for a low value userbase. Even at FB arpu the economics are still very poor.
>They aren't pulling an Amazon snd balancing cash flow with costs.
Nobody said they were. I said having hundreds of millions of completely free users would suck the profitability of any business, and that the remedy would be simple, should the need for it arise.
>They're just incinerating money for a low value userbase.
If you don't see how implementing ads in a system designed for having natural conversations to users whose most common queries are for “Practical Guidance” and “Seeking Information” could be incredibly valuable then you have no foresight and I don't know what to tell you.
>Even at FB arpu the economics are still very poor.
No they aren't and I honestly have no idea what you're talking about. Inference is cheap and has been for some time.
I don't think you realize the issue. They aren't monetizing their SaaS product satisfactorily -- hence the Amazon cash flow imbalance statement. This indicates they must find new markets to survive. Despite this, however, they are gaining only in poorer markets, limiting the monetizability of a high cost product.
Implementing adds is a hail-mary. It puts them in a knife fight with google which will likely result in a race to the bottom which OpenAI cannot sustain and win.
FB global ARPU is about 50 USD. At 700M customers, they do 35B in revenue annually. This compares to a publicly stated expected cost of approximately 150B in computing alone over the next 5 years (see: https://fortune.com/2025/09/06/openai-spending-outlook-115-b...). This leaves a profit of 5B per year, with 90B expected r&d costs. Even if OpenAI develops a product and fires all employees, you are looking at a ROIC of about 18 years.
Fundamentally, OpenAI does not have the unit economics of a traditional SaaS. "Hundreds of millions of users" is hundreds of millions of people consuming expenses and not generating sufficient revenue to justify the line of business as a going concern. This, coupled with declining enterprise AI adoption (https://www.apolloacademy.com/ai-adoption-rate-trending-down...) paints an ugly picture.
>Despite this, however, they are gaining only in poorer markets
They are gaining everywhere. Some more than others, but to say they are only gaining in poorer markets is blatantly untrue.
>FB global ARPU is about 50 USD. At 700M customers, they do 35B in revenue annually.
Yeah, and that would make them healthily profitable.
>This compares to a publicly stated expected cost of approximately 150B in computing alone over the next 5 years
Yes, because they expect to serve hundreds of millions to potentially billions more users. Your math quite frankly makes no sense. 'This leaves a profit of 5B per year' makes some very bizarre assumptions. You’re conflating a future-scale spending projection with today’s economics. That number is a forward-looking projection tied to massive scale - it doesn’t prove current users alone justify that spend, and they clearly don't. There is no reality where they are spending that much if their userbase stalls at today's numbers, so it's just a moot point and '5B per year' a made up number.
>Fundamentally, OpenAI does not have the unit economics of a traditional SaaS.
Again, Everything points to their unit economics being perfectly fine.
Yeah but the problem then becomes you are in a knife fight with google. Welcome to margin compression on already thin margins and high capex. Its not a like they buy commodity hardware that is cheap, or have the depth of talent like Google to do ASICs + DC management.
Once OpenAI turns to ads, I think it's an indicator they are out of ideas.
Nobody expected the total computations from social media they expect from LLM services. And that's the point: users are irrelevant, even large enterprises with advantageous cost structures can die with poor management.
Consumers have low friction on the way in and on the way out. Especially when media hype gets involved.
Business have higher friction - legal, integrations, access control, internal knowledge leaks (a document can be restricted access but result may leak into a more open query). Not to mention the typical general inertia. This friction works both ways.
I don't see how friction is the primary driver here. ChatGPT is available through the most enterprise sales channel available -- Azure. The Microsoft enterprise sales engine is probably the best in the world.
Similarly, if costs double (or worse, increase to a point to be close to typical SaaS margins) and LLMs lose their shine I dont think there will be friction on the way out. People (especially executives) will offer up ChatGPT as a sacrifice.
It’s been shoved down enterprise throats for months/years. Shareholders, CEOs, workers (at the start) and users (at the start) have never had such a unified understanding in what they want than this AI frenzy. All stars were aligned for it to gain more traction. And yet…
The statistic is from ChatGPT consumer plans, so I don't think it says anything useful about enterprise adoption of LLM products or usage patterns in those enterprise contexts.
The chats alone are backbreakingly costly relative to the market mix of ChatGPT.
Rest of the market be damned -- combined with the poor customer mix (low to middle income countries) this explains why there has been such a push by the big labs to attempt to quantize models and save costs. You effectively have highly paid engineers/scientists running computationally expensive models on some of the most expensive hardware on the market to serve instructions on how to do things to people in low income countries.
This doesn't sound good, even for ad-supported business models.
Yep and lets not forget, those people are incredibly price sensitive.
Is there enough product differentiation between OAI and Gemini? Not that I can see. And even if it was a low price, thats not the point - people hate paying a penny for something they expect to be free.
By the time OAI has developed anything that enables them to acquire and exercise market power (profitably), they will have ran out of funding (at least on favourable terms). Which could cause key talent to leave to competitors and so on. Essentially a downward spiral to death.
LLMs are the next ISPs, and those households who haven't yet found room for it on their monthly budgets soon will. And much like ISPs, i'd expect to see the starting $20/mo evolve over time into a full size utility bill. Not all households, of course, but at utility-scale nonetheless.
> ChatGPT is widely used for practical guidance, information seeking, and writing, which together make up nearly 80% of usage. Non-work queries now dominate (70%). Writing is the main work task, mostly editing user text. Users are younger, increasingly female, global, and adoption is growing fastest in lower-income countries
No amount of LinkedEn speech can fix the poor part of it.
In 2025, it's abundantly clear that the mask is off. Only the whales matter in video games. Only the top donors matter in donation funding. Modern laptops with GPUs are all $2k+ dollars machines. Luxury condos are everywhere. McDonalds revenues and profits are up despite pricing out a lot of low income people.
The poor have less of the nothing they already have. You can make a hundred affordable cars or get as much, if not order of magnitudes more, profit with just one luxury vehicle sale.
You have no idea if they’re ambitious or educated. Absolutely no idea. Is it just commonplace to inject “facts” into conjecture? Comes off as desperate.
# How People Use ChatGPT
(Chatterji, Cunningham, Deming, Hitzig, Ong, Shan, Wadman – Sept 2025)
### Scope
- Study of ChatGPT consumer plans (Free, Plus, Pro) from Nov 2022–Jul 2025.
- Covers ~700M weekly active users by mid-2025 (~10% of world’s adults).
- Uses automated, privacy-preserving classification of billions of messages.
---
### Key Findings
*Adoption & Growth*
- Usage grew 5× between mid-2024 and mid-2025.
- Growth from both new users and heavier use by existing users.
*Work vs. Non-Work*
- Mid-2024: ~47% work / 53% non-work.
- Mid-2025: ~27% work / 73% non-work.
- Shift due to both new cohorts and existing users expanding non-work use.
*Topics of Use*
- 3 main topics = ~80% of all use:
1. Practical Guidance (advice, tutoring, ideas)
2. Seeking Information (facts, search-like queries)
3. Writing (drafting, editing, summarizing)
- Technical help (e.g. coding, math) ≈ 4%.
*Work Activities (ONET mapping)*
- Common across occupations:
- Getting Information
- Documenting / Interpreting
- Problem Solving & Decision Making
- Thinking Creatively
- Advising/Consulting
*Demographics*
- Early skew male; now near gender balance.
- Younger users dominate; older users more work-focused.
- Strong growth in low- & middle-income countries.
- Higher education correlates with more work use.
*Quality / Satisfaction*
- Positive (“good”) user feedback outnumbers negative and is improving.
- Asking messages have highest satisfaction rates.
---
### Implications
- ChatGPT’s value is broad: not just coding/technical, but also decision support, writing, learning, and personal tasks.
- Increasing non-work use suggests large social value beyond workplace productivity.
- Broad relevance across occupations → general-purpose tool.
- Adoption trends point to narrowing gaps across gender and geography.
I get that not everyone wants to read a 62-page paper. But this has an abstract, a conclusion, and an accompanying blog post that each serve the same purpose as this summary. Just use the existing, better materials if you're not willing to go in depth.
i think that's the point of the article.. people dont want to generate their own slop (read an article and be forced to form an informed opinion) they want it made for them (article summarized so they can skim an opinion and move on to the next slop)
Since many consumers are typically relatively tight-fisted in the b2c market, I don't think this bodes well for the long-term economics of the market. This may explain the relatively recent pivot to attempt to "discover" uses.
I don't think this ends happily.
Still, 700 million users, and they can still add a lot of products within ChatGPT. Ads will also be slapped on answers.
If all fails, Sam will start wearing "Occupy Jupiter" t-shirts.
Ads won't be slapped onto answers, my guess is that they will be subtly and silently inserted into them so that you don't even notice. It won't always be what you see either as companies, political groups, and others who seek to influence you will pay to have specific words/phrases omitted from answers as well.
AI at this point is little more than a toy that outright lies occasionally yet we're already seeing AI hurting people's ability to think, be creative, use critical thinking skills, and research independently.
They aren't pulling an Amazon snd balancing cash flow with costs. They're just incinerating money for a low value userbase. Even at FB arpu the economics are still very poor.
Okay, so still hundreds of millions of users
>They aren't pulling an Amazon snd balancing cash flow with costs.
Nobody said they were. I said having hundreds of millions of completely free users would suck the profitability of any business, and that the remedy would be simple, should the need for it arise.
>They're just incinerating money for a low value userbase.
If you don't see how implementing ads in a system designed for having natural conversations to users whose most common queries are for “Practical Guidance” and “Seeking Information” could be incredibly valuable then you have no foresight and I don't know what to tell you.
>Even at FB arpu the economics are still very poor.
No they aren't and I honestly have no idea what you're talking about. Inference is cheap and has been for some time.
Implementing adds is a hail-mary. It puts them in a knife fight with google which will likely result in a race to the bottom which OpenAI cannot sustain and win.
FB global ARPU is about 50 USD. At 700M customers, they do 35B in revenue annually. This compares to a publicly stated expected cost of approximately 150B in computing alone over the next 5 years (see: https://fortune.com/2025/09/06/openai-spending-outlook-115-b...). This leaves a profit of 5B per year, with 90B expected r&d costs. Even if OpenAI develops a product and fires all employees, you are looking at a ROIC of about 18 years.
Fundamentally, OpenAI does not have the unit economics of a traditional SaaS. "Hundreds of millions of users" is hundreds of millions of people consuming expenses and not generating sufficient revenue to justify the line of business as a going concern. This, coupled with declining enterprise AI adoption (https://www.apolloacademy.com/ai-adoption-rate-trending-down...) paints an ugly picture.
They are gaining everywhere. Some more than others, but to say they are only gaining in poorer markets is blatantly untrue.
>FB global ARPU is about 50 USD. At 700M customers, they do 35B in revenue annually.
Yeah, and that would make them healthily profitable.
>This compares to a publicly stated expected cost of approximately 150B in computing alone over the next 5 years
Yes, because they expect to serve hundreds of millions to potentially billions more users. Your math quite frankly makes no sense. 'This leaves a profit of 5B per year' makes some very bizarre assumptions. You’re conflating a future-scale spending projection with today’s economics. That number is a forward-looking projection tied to massive scale - it doesn’t prove current users alone justify that spend, and they clearly don't. There is no reality where they are spending that much if their userbase stalls at today's numbers, so it's just a moot point and '5B per year' a made up number.
>Fundamentally, OpenAI does not have the unit economics of a traditional SaaS.
Again, Everything points to their unit economics being perfectly fine.
Once OpenAI turns to ads, I think it's an indicator they are out of ideas.
Business have higher friction - legal, integrations, access control, internal knowledge leaks (a document can be restricted access but result may leak into a more open query). Not to mention the typical general inertia. This friction works both ways.
Think capacitive vs induction electric circuits.
Similarly, if costs double (or worse, increase to a point to be close to typical SaaS margins) and LLMs lose their shine I dont think there will be friction on the way out. People (especially executives) will offer up ChatGPT as a sacrifice.
On the other hand, I remember when BlackBerry had enterprise locked down and got wiped out by consumer focused Apple.
In any event, having big consumer growth doesn't seem like a bad thing.
It will be bad if it starts a race to the bottom for ad driven offering though.
It’s the prodigal child of tech.
When OpenAI sells a ChatGPT subscription, they incur large costs just to serve the product, shrinking margins.
Big difference in unit economics, hence the quantization push.
Rest of the market be damned -- combined with the poor customer mix (low to middle income countries) this explains why there has been such a push by the big labs to attempt to quantize models and save costs. You effectively have highly paid engineers/scientists running computationally expensive models on some of the most expensive hardware on the market to serve instructions on how to do things to people in low income countries.
This doesn't sound good, even for ad-supported business models.
Is there enough product differentiation between OAI and Gemini? Not that I can see. And even if it was a low price, thats not the point - people hate paying a penny for something they expect to be free.
By the time OAI has developed anything that enables them to acquire and exercise market power (profitably), they will have ran out of funding (at least on favourable terms). Which could cause key talent to leave to competitors and so on. Essentially a downward spiral to death.
https://openai.com/index/how-people-are-using-chatgpt/
> ChatGPT is widely used for practical guidance, information seeking, and writing, which together make up nearly 80% of usage. Non-work queries now dominate (70%). Writing is the main work task, mostly editing user text. Users are younger, increasingly female, global, and adoption is growing fastest in lower-income countries
Young moms with no money in poor countries use this product the most. I bet that was fun news to deliver up the chain.
In 2025, it's abundantly clear that the mask is off. Only the whales matter in video games. Only the top donors matter in donation funding. Modern laptops with GPUs are all $2k+ dollars machines. Luxury condos are everywhere. McDonalds revenues and profits are up despite pricing out a lot of low income people.
The poor have less of the nothing they already have. You can make a hundred affordable cars or get as much, if not order of magnitudes more, profit with just one luxury vehicle sale.
# How People Use ChatGPT (Chatterji, Cunningham, Deming, Hitzig, Ong, Shan, Wadman – Sept 2025)
### Scope - Study of ChatGPT consumer plans (Free, Plus, Pro) from Nov 2022–Jul 2025. - Covers ~700M weekly active users by mid-2025 (~10% of world’s adults). - Uses automated, privacy-preserving classification of billions of messages.
---
### Key Findings
*Adoption & Growth* - Usage grew 5× between mid-2024 and mid-2025. - Growth from both new users and heavier use by existing users.
*Work vs. Non-Work* - Mid-2024: ~47% work / 53% non-work. - Mid-2025: ~27% work / 73% non-work. - Shift due to both new cohorts and existing users expanding non-work use.
*Topics of Use* - 3 main topics = ~80% of all use: 1. Practical Guidance (advice, tutoring, ideas) 2. Seeking Information (facts, search-like queries) 3. Writing (drafting, editing, summarizing) - Technical help (e.g. coding, math) ≈ 4%.
*Intent* - Asking (49%), Doing (40%), Expressing (11%). - Work-related chats skew toward Doing (esp. Writing tasks).
*Work Activities (ONET mapping)* - Common across occupations: - Getting Information - Documenting / Interpreting - Problem Solving & Decision Making - Thinking Creatively - Advising/Consulting
*Demographics* - Early skew male; now near gender balance. - Younger users dominate; older users more work-focused. - Strong growth in low- & middle-income countries. - Higher education correlates with more work use.
*Quality / Satisfaction* - Positive (“good”) user feedback outnumbers negative and is improving. - Asking messages have highest satisfaction rates.
---
### Implications - ChatGPT’s value is broad: not just coding/technical, but also decision support, writing, learning, and personal tasks. - Increasing non-work use suggests large social value beyond workplace productivity. - Broad relevance across occupations → general-purpose tool. - Adoption trends point to narrowing gaps across gender and geography.