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I think that's precisely why the investors aren't as interested - bitcoin had very little value by itself, so investors got dollar signs in their eyes when a st

I think that's precisely why the investors aren't as interested - bitcoin had very little value by itself, so investors got dollar signs in their eyes when a startup claimed to be able to add the value it was missing. ChatGPT already has a lot of value by itself, the value added by any startup is going to be marginal at best.

tsunamifury 3y ago
I think this is a good example of the VC mindset, but I think it is also flawed on their part. LLMs are a lot more like a generalized processor than people are admitting right now. Granted you can talk to it, but it becomes significantly more capable when you learn how to program it -- and thats where the value will be added.
- LordDragonfang 3y ago
  >when you learn how to program it I don't know if you mean, like, LoRAs and similar (actual substantive changes), but the vast majority of "learning how to program" LLMs (accounting for the majority of startup pitches as well) is "prompt engineering" - which, as the meme goes, isn't a moat. There's a skill to it, yes, but if your singular advantage boils down to a few lines of English prose, your product isn't able to control a market - and VCs are (rightly) not interested unless you have the possibility to be a near-monopoly.
  - tsunamifury 3y ago
    This is the error of the thinking. It would be like saying software doesn't have a moat because thats just clever talking to a processor. But no one would say that now, thats ridiculous. There is a sufficient degree of prompt engineering that is already defensible, I'm already doing it myself IMO. You'll see very sophisticated hybrid programming/prompting systems being developed in the next year that will prove out the case. For example 30 parallel prompts that then amalgamate into a decision and an audit, with 10 simulation level prompts running chained afterwards to clean the output. These types of atomic configurations will become sufficiently complex to not be just for 'anybody'.
    - __loam 3y ago
      The output of that many chained prompts is probably so unreliable that it's useless.
      - tsunamifury 3y ago
        Again, this is a misunderstanding of what LLMs are capable of. These aren't chained, you can run parallel prompts of 15 personas with X diversity of perspective, that reason on a singular request, string, input or variable, they provide output plus audit explanation. You then run an amalgamation or committee decision (sort of like mixture of experts) on it to output variable or string. Then run parallel simulation or reflection prompts based on X different context personas to double check their application to outside cases, unconsidered context, etc. It's pretty effective on complex problems like Spam, Trust and Safety, etc. And the applications of these sort of reasoning atomic configurations I think are unlimited. It's not just 'talking fancy' to an AI, its building processes that systematically improve reasoning to different very hard applied problems.
        ironborn123 3y ago
        Brave are those who have set out to make prompt engg an entire industry, with gpt-5 and gemini lurking on the horizon.
        tsunamifury 3y ago
        Sam has specifically stated it's unlikely there will be a GPT5, and likely GPT4 is just a deeply prompt optimized and multimode version of GPT3. But overall, hasn't that theme been true for like... all tech ever? You have to set up and build your own innovation path at some point.
        matmulbro 3y ago
        [flagged]
        3y ago
  - svnt 3y ago
    > I don't know if you mean, like, <extensions> and similar (actual substantive changes), but the vast majority of "learning how to program" <computers> (accounting for the majority of startup pitches as well) is "<punchcard> engineering" - which, as the meme goes, isn't a moat. There's a skill to it, yes, but if your singular advantage boils down to a few lines of <clever pointer dereferences>, your product isn't able to control a market - and VCs are (rightly) not interested unless you have the possibility to be a near-monopoly.
    - tsunamifury 3y ago
      Ha I think you get it. The only moat is either user engagement, sales or sufficient sophistication of otherwise known techniques. There is not dark arts magic moat.
  - treprinum 3y ago
    There is a lot of moat for intelligent people who leverage emerging properties of huge LLMs. I am not going to give you a hint but I already use those LLMs in unexpected ways that weren't possible before.
- alfalfasprout 3y ago
  > LLMs are a lot more like a generalized processor than people are admitting right now. Granted you can talk to it, but it becomes significantly more capable when you learn how to program it -- and thats where the value will be added. So... prompt engineering? They're an extremely inefficient processor though and very prone to error (despite what synthetic benchmarks may show).
  - tsunamifury 3y ago
    As with any truly promising technology, out of the box performance should not be mistaken for long term potential. I think this is very much like the CCD sensor, that Kodak couldn't envision using because it was "so expensive, slow and low resolution."
boredumb 3y ago
This is absolutely correct. As soon as I was able to get access I built my own... GPT proxy to generate marketing copy and all that for people and while it was neat it comes down to a regular crud application that has a wrapper around an OpenAI API, the moat isn't there, the app was alright but I realized pretty quickly my "value" being that I'm basically using a template engine against a text prompt - I probably shouldn't shut down my consulting business over pursuing it.
- quickthrower2 3y ago
  Now get ChatGPT to detect transaction fraud for a bank.
  - boredumb 3y ago
    use k clustering for anomaly detection and not blow your budget on a bunch A100s or API access?
quickthrower2 3y ago
> ChatGPT already has a lot of value by itself, the value added by any startup is going to be marginal at best. This assumes that all AI startups are squarely competing with ChatGPT, or that ChatGPT is some kind of AGI that can do most machine learning tasks making AI startups redundant "thin wrappers" around ChatGPT. How does ChatGPT make say Weights and Biases irrelevant, or a startup detecting bank transaction fraud, or say a product that detects when someone at your door is a stranger.

tsunamifury 3y ago

I think this is a good example of the VC mindset, but I think it is also flawed on their part. LLMs are a lot more like a generalized processor than people are admitting right now. Granted you can talk to it, but it becomes significantly more capable when you learn how to program it -- and thats where the value will be added.

LordDragonfang 3y ago
>when you learn how to program it I don't know if you mean, like, LoRAs and similar (actual substantive changes), but the vast majority of "learning how to program" LLMs (accounting for the majority of startup pitches as well) is "prompt engineering" - which, as the meme goes, isn't a moat. There's a skill to it, yes, but if your singular advantage boils down to a few lines of English prose, your product isn't able to control a market - and VCs are (rightly) not interested unless you have the possibility to be a near-monopoly.
- tsunamifury 3y ago
  This is the error of the thinking. It would be like saying software doesn't have a moat because thats just clever talking to a processor. But no one would say that now, thats ridiculous. There is a sufficient degree of prompt engineering that is already defensible, I'm already doing it myself IMO. You'll see very sophisticated hybrid programming/prompting systems being developed in the next year that will prove out the case. For example 30 parallel prompts that then amalgamate into a decision and an audit, with 10 simulation level prompts running chained afterwards to clean the output. These types of atomic configurations will become sufficiently complex to not be just for 'anybody'.
  - __loam 3y ago
    The output of that many chained prompts is probably so unreliable that it's useless.
    - tsunamifury 3y ago
      Again, this is a misunderstanding of what LLMs are capable of. These aren't chained, you can run parallel prompts of 15 personas with X diversity of perspective, that reason on a singular request, string, input or variable, they provide output plus audit explanation. You then run an amalgamation or committee decision (sort of like mixture of experts) on it to output variable or string. Then run parallel simulation or reflection prompts based on X different context personas to double check their application to outside cases, unconsidered context, etc. It's pretty effective on complex problems like Spam, Trust and Safety, etc. And the applications of these sort of reasoning atomic configurations I think are unlimited. It's not just 'talking fancy' to an AI, its building processes that systematically improve reasoning to different very hard applied problems.
      - ironborn123 3y ago
        Brave are those who have set out to make prompt engg an entire industry, with gpt-5 and gemini lurking on the horizon.
        tsunamifury 3y ago
        Sam has specifically stated it's unlikely there will be a GPT5, and likely GPT4 is just a deeply prompt optimized and multimode version of GPT3. But overall, hasn't that theme been true for like... all tech ever? You have to set up and build your own innovation path at some point.
        matmulbro 3y ago
        [flagged]
      - 3y ago
- svnt 3y ago
  > I don't know if you mean, like, <extensions> and similar (actual substantive changes), but the vast majority of "learning how to program" <computers> (accounting for the majority of startup pitches as well) is "<punchcard> engineering" - which, as the meme goes, isn't a moat. There's a skill to it, yes, but if your singular advantage boils down to a few lines of <clever pointer dereferences>, your product isn't able to control a market - and VCs are (rightly) not interested unless you have the possibility to be a near-monopoly.
  - tsunamifury 3y ago
    Ha I think you get it. The only moat is either user engagement, sales or sufficient sophistication of otherwise known techniques. There is not dark arts magic moat.
- treprinum 3y ago
  There is a lot of moat for intelligent people who leverage emerging properties of huge LLMs. I am not going to give you a hint but I already use those LLMs in unexpected ways that weren't possible before.
alfalfasprout 3y ago
> LLMs are a lot more like a generalized processor than people are admitting right now. Granted you can talk to it, but it becomes significantly more capable when you learn how to program it -- and thats where the value will be added. So... prompt engineering? They're an extremely inefficient processor though and very prone to error (despite what synthetic benchmarks may show).
- tsunamifury 3y ago
  As with any truly promising technology, out of the box performance should not be mistaken for long term potential. I think this is very much like the CCD sensor, that Kodak couldn't envision using because it was "so expensive, slow and low resolution."

boredumb 3y ago

This is absolutely correct. As soon as I was able to get access I built my own... GPT proxy to generate marketing copy and all that for people and while it was neat it comes down to a regular crud application that has a wrapper around an OpenAI API, the moat isn't there, the app was alright but I realized pretty quickly my "value" being that I'm basically using a template engine against a text prompt - I probably shouldn't shut down my consulting business over pursuing it.

quickthrower2 3y ago
Now get ChatGPT to detect transaction fraud for a bank.
- boredumb 3y ago
  use k clustering for anomaly detection and not blow your budget on a bunch A100s or API access?

quickthrower2 3y ago

> ChatGPT already has a lot of value by itself, the value added by any startup is going to be marginal at best. This assumes that all AI startups are squarely competing with ChatGPT, or that ChatGPT is some kind of AGI that can do most machine learning tasks making AI startups redundant "thin wrappers" around ChatGPT. How does ChatGPT make say Weights and Biases irrelevant, or a startup detecting bank transaction fraud, or say a product that detects when someone at your door is a stranger.