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"ChatGPT Plus has no real moat" other than the fact that they still have the best models. Competitors can be useful in some situations, but in the general case,

"ChatGPT Plus has no real moat" other than the fact that they still have the best models. Competitors can be useful in some situations, but in the general case, GPT-4 (and now o1-preview) are more useful than anything else out there. And that's before factoring in what other models they are cooking that they're in no hurry to release. If ChatGPT is so easy to outcompete, where's the competition?

__loam 2y ago
Claude is better than gpt-4.
- lambdaba 2y ago
  o1 is better than Claude at more difficult tasks, I have used and continue to use both extensively
- rsanheim 2y ago
  Do you find that true for tech / programming tasks, general purpose things, or both?
  - deleted 2y ago
    [deleted]
- airstrike 2y ago
  Not better than o1-preview based on my subjective experience.
BryanLegend 2y ago
Their moat is the data and feedback they get from their users. Each chat has the potential to learn something from. AB tests. Upvotes & Downvotes. All that feedback has made their product better and they're at a very different scale than everyone else.
- visarga 2y ago
  Agreed. There is both explicit and implicit feedback in user responses. Did they continue or stop? Clarification? Pushing back? Correction? so many signals. Even more, you can judge a response by looking at how the conversation went after that moment. Hindsight analysis to predict their usefulness. The more interesting thing is when the user tests the AI ideas. Like running some code or applying advice for your hobbies. The user returns again and again communicating the outcomes in order to get new advice. The model can collect all chats across many days by topic, and judge them in retrospective. Which answers were good or bad? The user base is so large, 200M users means lots of perspectives, lots of ideas, chatGPT puts trillions of tokens in human brains per month. They influence on a grand scale. After a while some article will describe the outcomes, and that data percolates back in the next training set. A feedback loop. And an indirect agent, working through people. An experience flywheel, collecting from hundreds of millions and serving back.
visarga 2y ago
> they still have the best models Only few use cases need the best models. Most of them work ok on mini models and LLaMAs. The problem is that it costs very much to develop this shrinking segment of high end. With each new release in open / smaller models, the island of superiority shrinks for OpenAI. But people's task don't get any more difficult on average. So eventually your phone model will suffice for 99% of use cases, and be free and private. What will OpenAI do with 1% of the market?
- airstrike 2y ago
  The "I need some help on some vague problem and want to ask a machine to act like a junior member of my team I could delegate to and get mostly-right results that I would still need to verify" is a very compelling use case. Arguably the best.
  - consteval 2y ago
    I think it's, by far, the least compelling use case. AI first and foremost will be focused on singular tasks that are fairly complex, in order to automate. These "catch all" use cases sound cool, but in practice are nearly worthless. Because you'll spend more time verifying than if you just did it yourself. The compelling use cases are the human-less use cases. Imagine ridding your entire accounting department because it can be handled by highly specific LLMs and calculators.
    - adamtaylor_13 2y ago
      I have never, in 2+ years of using AI spent more time verifying the output lol. It takes like 10 seconds. You’re just using it to point you in a direction, not solve the entire problem.
      - consteval 2y ago
        Then I'm questioning if the output is actually correct. That's just not a compelling use case. It makes far more sense to just hire junior engineers. Because then they're actual engineers, and their knowledge base will grow and transfer over time. If you have proprietary systems, then it may make sense to train an LLM on those. But I would consider that a more "focused" use case. I just don't see the main value of AI being a glorified, generalized google search. There's potential here to automate tasks with MUCH less friction. Now business folk and people who understand processes (but not algorithms) can potentially automate business processes.

__loam 2y ago

Claude is better than gpt-4.

lambdaba 2y ago
o1 is better than Claude at more difficult tasks, I have used and continue to use both extensively
rsanheim 2y ago
Do you find that true for tech / programming tasks, general purpose things, or both?
- deleted 2y ago
  [deleted]
airstrike 2y ago
Not better than o1-preview based on my subjective experience.

BryanLegend 2y ago

Their moat is the data and feedback they get from their users. Each chat has the potential to learn something from. AB tests. Upvotes & Downvotes. All that feedback has made their product better and they're at a very different scale than everyone else.

visarga 2y ago
Agreed. There is both explicit and implicit feedback in user responses. Did they continue or stop? Clarification? Pushing back? Correction? so many signals. Even more, you can judge a response by looking at how the conversation went after that moment. Hindsight analysis to predict their usefulness. The more interesting thing is when the user tests the AI ideas. Like running some code or applying advice for your hobbies. The user returns again and again communicating the outcomes in order to get new advice. The model can collect all chats across many days by topic, and judge them in retrospective. Which answers were good or bad? The user base is so large, 200M users means lots of perspectives, lots of ideas, chatGPT puts trillions of tokens in human brains per month. They influence on a grand scale. After a while some article will describe the outcomes, and that data percolates back in the next training set. A feedback loop. And an indirect agent, working through people. An experience flywheel, collecting from hundreds of millions and serving back.

visarga 2y ago

> they still have the best models Only few use cases need the best models. Most of them work ok on mini models and LLaMAs. The problem is that it costs very much to develop this shrinking segment of high end. With each new release in open / smaller models, the island of superiority shrinks for OpenAI. But people's task don't get any more difficult on average. So eventually your phone model will suffice for 99% of use cases, and be free and private. What will OpenAI do with 1% of the market?

airstrike 2y ago
The "I need some help on some vague problem and want to ask a machine to act like a junior member of my team I could delegate to and get mostly-right results that I would still need to verify" is a very compelling use case. Arguably the best.
- consteval 2y ago
  I think it's, by far, the least compelling use case. AI first and foremost will be focused on singular tasks that are fairly complex, in order to automate. These "catch all" use cases sound cool, but in practice are nearly worthless. Because you'll spend more time verifying than if you just did it yourself. The compelling use cases are the human-less use cases. Imagine ridding your entire accounting department because it can be handled by highly specific LLMs and calculators.
  - adamtaylor_13 2y ago
    I have never, in 2+ years of using AI spent more time verifying the output lol. It takes like 10 seconds. You’re just using it to point you in a direction, not solve the entire problem.
    - consteval 2y ago
      Then I'm questioning if the output is actually correct. That's just not a compelling use case. It makes far more sense to just hire junior engineers. Because then they're actual engineers, and their knowledge base will grow and transfer over time. If you have proprietary systems, then it may make sense to train an LLM on those. But I would consider that a more "focused" use case. I just don't see the main value of AI being a glorified, generalized google search. There's potential here to automate tasks with MUCH less friction. Now business folk and people who understand processes (but not algorithms) can potentially automate business processes.