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The problem is not if the 80% of them fail, but if of the remaining 20% you get several black swans that make profit skyrocket for the whole investment set. Th

The problem is not if the 80% of them fail, but if of the remaining 20% you get several black swans that make profit skyrocket for the whole investment set. The problem is if none of them, even the surviving ones, don't worth too much anyway. In that case those billions would had been wasted. But if you invested everything to just one player, and that player failed, then your whole bet failed.

kjkjadksj 2y ago
It all depends if the rate of black swan is too low to bother with. There is probably a point where spending vc money on lottery tickets starts looking like the more pragmatic investment.
llamaimperative 2y ago
Pretty sure this is about AI projects, i.e. potentially career-ending failures, not failed AI companies which in a VC context have a high expected rate of failure.
- marcinzm 2y ago
  That's bad management if it's seen as career ending failure. Proper management just does the ROI on the successful ones versus overall cost. And doesn't risk everything on them. Large tech companies test hundreds of model changes and rollouts per year in AB tests for that reason.
- lumost 2y ago
  Anecdotally, I've never seen a project be a career ender. Most individuals have many projects under their belt, it's rare that an individual has bet the farm on an effort in such a way that others would not employ them. When a project fails, the lessons are often valuable for the next project - when a project succeeds it can often just be do to market position.
HarHarVeryFunny 2y ago
I guess that perspective depends if you are a VC or a company trying to apply AI. The VC expects to lose most of the time and hit it out of the park once in a while, for a large net gain. For companies trying to automate or increase productivity with AI, there are unlikely to be any massively profitable winners that will make up for the failures, so too many failures is going to hurt.
- Closi 2y ago
  I'm not sure the second statement is true - there are plenty of sectors I can think of which could be massive winners that would make up for the failures. Obvious examples are things like AI-assisted language translation for books if you are a publisher, implementation of cancer screening technologies if you are a healthcare provider, AI-assisted drug discovery if you are a pharma firm, AI-assisted discovery if you are a legal provider, AI trading algo's if you are a hedge fund... All of these could result in some pretty profitable winners. It's also the sort of thing that can result in 'losers' if you call it wrong - e.g. if there is a 20% chance of success and I don't invest and my competitors do, what happens if I am wrong? You don't want to be the Kodak of your industry. As an example - if I am the worlds biggest provider of call centres, do I really want to bet big that my industry will never be automated, or do I want to start investing now to prepare for that as a possibility?
  - HarHarVeryFunny 2y ago
    The 20% chance of success isn't typically 20% chance of a moonshot paying off, but rather of a project being successful and achieving it's cost saving/productivity and user acceptance goals. I don't think your Kodak example works very well here - they were a classic business school case of not realizing what business they were really in, but most uses of AI (whether one means LLMs, or something else like most of your examples) are going to be automation/productivity enhancement, not "pivotal" changes.
    - Closi 2y ago
      In most cases they might be incremental enhancement, but in some cases I am arguing that they will be 'pivotal' changes.
lolinder 2y ago
This isn't about AI startups, it's about projects that are started under the umbrella of an existing organization. See the actual report: https://www.rand.org/pubs/research_reports/RRA2680-1.html https://www.rand.org/pubs/research_reports/RRA2680-1.html
- bugbuddy 2y ago
  I would guess that the failure rate is worse for AI startups in general because of lower capital and experience. These failing experiments can only shell out billions for Nvidia’s shovels for so long before they have to start selling their shirts for lunch.
  - pas 2y ago
    on the other hand internal teams are not exactly known for their amazing inventiveness and daring bets that then make bank.
- josh-sematic 2y ago
  Sufficiently large companies should have a healthy “internal portfolio” of projects as well. Some safe bets/easy wins, some “this probably won’t work but if it does it’s game changing” projects as well. If they want to continue to exist in a competitive market they need to strike gold occasionally (which means hitting rock a lot of the time as well).

kjkjadksj 2y ago

It all depends if the rate of black swan is too low to bother with. There is probably a point where spending vc money on lottery tickets starts looking like the more pragmatic investment.

llamaimperative 2y ago

Pretty sure this is about AI projects, i.e. potentially career-ending failures, not failed AI companies which in a VC context have a high expected rate of failure.

marcinzm 2y ago
That's bad management if it's seen as career ending failure. Proper management just does the ROI on the successful ones versus overall cost. And doesn't risk everything on them. Large tech companies test hundreds of model changes and rollouts per year in AB tests for that reason.
lumost 2y ago
Anecdotally, I've never seen a project be a career ender. Most individuals have many projects under their belt, it's rare that an individual has bet the farm on an effort in such a way that others would not employ them. When a project fails, the lessons are often valuable for the next project - when a project succeeds it can often just be do to market position.

HarHarVeryFunny 2y ago

I guess that perspective depends if you are a VC or a company trying to apply AI. The VC expects to lose most of the time and hit it out of the park once in a while, for a large net gain. For companies trying to automate or increase productivity with AI, there are unlikely to be any massively profitable winners that will make up for the failures, so too many failures is going to hurt.

Closi 2y ago
I'm not sure the second statement is true - there are plenty of sectors I can think of which could be massive winners that would make up for the failures. Obvious examples are things like AI-assisted language translation for books if you are a publisher, implementation of cancer screening technologies if you are a healthcare provider, AI-assisted drug discovery if you are a pharma firm, AI-assisted discovery if you are a legal provider, AI trading algo's if you are a hedge fund... All of these could result in some pretty profitable winners. It's also the sort of thing that can result in 'losers' if you call it wrong - e.g. if there is a 20% chance of success and I don't invest and my competitors do, what happens if I am wrong? You don't want to be the Kodak of your industry. As an example - if I am the worlds biggest provider of call centres, do I really want to bet big that my industry will never be automated, or do I want to start investing now to prepare for that as a possibility?
- HarHarVeryFunny 2y ago
  The 20% chance of success isn't typically 20% chance of a moonshot paying off, but rather of a project being successful and achieving it's cost saving/productivity and user acceptance goals. I don't think your Kodak example works very well here - they were a classic business school case of not realizing what business they were really in, but most uses of AI (whether one means LLMs, or something else like most of your examples) are going to be automation/productivity enhancement, not "pivotal" changes.
  - Closi 2y ago
    In most cases they might be incremental enhancement, but in some cases I am arguing that they will be 'pivotal' changes.

lolinder 2y ago

This isn't about AI startups, it's about projects that are started under the umbrella of an existing organization. See the actual report: https://www.rand.org/pubs/research_reports/RRA2680-1.html https://www.rand.org/pubs/research_reports/RRA2680-1.html

bugbuddy 2y ago
I would guess that the failure rate is worse for AI startups in general because of lower capital and experience. These failing experiments can only shell out billions for Nvidia’s shovels for so long before they have to start selling their shirts for lunch.
- pas 2y ago
  on the other hand internal teams are not exactly known for their amazing inventiveness and daring bets that then make bank.
josh-sematic 2y ago
Sufficiently large companies should have a healthy “internal portfolio” of projects as well. Some safe bets/easy wins, some “this probably won’t work but if it does it’s game changing” projects as well. If they want to continue to exist in a competitive market they need to strike gold occasionally (which means hitting rock a lot of the time as well).