Top 5 reasons OpenAI is probably not worth 90 billion dollars
We recorded 9 claims from this post. 3 were checked against evidence. 6 can’t be checked. 3 more are held. Read the post at its source.
Claims from this post (9)
In the order they appear in the post. Each quotation is the speaker’s words in this post. A label such as “Supported” or “Can’t be checked” is the claim’s current assessment; its page has the evidence and the reasons.
Assessment: Can’t be checkedSaid
Observation
“That projection probably comes from their two best quarters, and some data suggest their spectacular growth has already been a slow down after the amazing initial takeoff.”
Our reading: OpenAI's projected $1 billion in revenue this year probably comes from its two best quarters, and some data suggest its growth has already slowed after the initial takeoff.
No horizon
Qualifier: probably
Assessment: Can’t be checkedSaid
Forecast
“Current models still have a lot of problems, necessitating future models. Future models will likely be bigger models, even more expensive to train, even more expensive to operate, further reducing profits.”
Our reading: OpenAI's future models will likely be bigger and more expensive to train and operate, further reducing profits.
No horizon
Assessment: Can’t be checkedSaid
Observation
“In the current state, even small changes to the underlying database might require complete retraining.”
Our reading: Currently, even small changes to an LLM's training data might require complete retraining.
No horizon
Qualifier: small changes to training data
Assessment: Can’t be checkedSaid
Forecast
“Students don’t as rule have a lot of money; they may quickly turn to free alternatives.”
Our reading: Students using OpenAI products may quickly switch to free alternatives.
No horizon
Assessment: SupportedSaid
Forecast
“The competitors may not (yet) be as good, but they are free, which will suit some customers fine.”
Our reading: Free open-source competitors to OpenAI, though perhaps not yet as good, will suit some customers.
Settled before its deadline Evidence as of
Limitation: The deployment and reception account comes from a Meta case study containing customer testimony, without an independent evaluation.
Assessment: SupportedSaid
Observation
“Hallucinations are a huge, unsolved issue.”
Our reading: LLM hallucinations are a huge, unsolved problem.
No horizon Evidence as of
Limitation: Huge is a qualitative judgment; no universal hallucination rate is established.
Assessment: Can’t be checkedSaid
Forecast
“It might be OK in domains where 70% correct is OK, but unlikely to be acceptable in mission-critical applications.”
Our reading: LLMs may be acceptable where 70% accuracy suffices but are unlikely to be acceptable for mission-critical applications.
No horizon
Qualifier: mission-critical applications
Assessment: SupportedSaid
Observation
“Explicit verbal instruction is key in training human employees, and LLMs aren’t very good at following those instructions.”
Our reading: LLMs are not very good at following explicit verbal instructions.
No horizon Evidence as of
Limitation: The conclusion depends on reading the claim as a reliability criticism.
Assessment: Can’t be checkedSaid
Forecast
“LLMs will likely never deliver on the promise that they are somehow close to AGI.”
Our reading: LLMs will likely never deliver on the promise of being close to AGI.
No horizon
Held from this post (3)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The two reviews read the claim differently
- The record does not meet the current rules. Our reading, not assessed: GPT cannot be trusted to follow the rules of chess despite those rules being in its training data.
- The record does not meet the current rules. Our reading, not assessed: Big customers are still skittish about LLMs: JP Morgan, Apple, and others ban their use at least partly over concerns about data leaks, and reliability is also a problem.