OpenAI’s credibility is central to its business: overstating capabilities risks public blowback, loss of trust, and regulatory scrutiny. As a result, it is unlikely that OpenAI would knowingly lie about its models. They have much stronger incentives to be as accurate as possible—maintaining their reputation and trust from users, researchers, and investors—than to overstate capabilities for a short-term gain that would undermine their long-term position.
From a game-theoretic standpoint, repeated interactions with the public (research community, regulators, and customers) create strong disincentives for OpenAI to lie. In a single-shot scenario, overstating model performance might yield short-term gains—heightened buzz or investment—but repeated play changes the calculus:
1. Reputation as “collateral”
OpenAI’s future deals, collaborations, and community acceptance rely on maintaining credibility. In a repeated game, players who defect (by lying) face future punishment: loss of trust, diminished legitimacy, and skepticism of future claims.
2. Long-term payoff maximization
If OpenAI is caught making inflated claims, the fallout undermines the brand and reduces willingness to engage in future transactions. Therefore, even if there is a short-term payoff, the long-term expected value of accuracy trumps the momentary benefit of deceit.
3. Strong incentives for verification
Independent researchers, open-source projects, and competitor labs can test or replicate claims. The availability of external scrutiny acts as a built-in enforcement mechanism, making dishonest “moves” too risky.
Thus, within the repeated game framework, OpenAI maximizes its overall returns by preserving its credibility rather than lying about capabilities for a short-lived advantage.
Find me the folks who see nothing but good will in OpenAI’s actions and I’ll find you the folks who have been hyping up AGI for the last 2 years.
4 was literally sitting on a shelf waiting for release when 3.5 was launched. 4o was a fine tune that took over two years. o1 is embarrassingly unimpressive chain of thought which is why they hide it.
The company hit a wall a year ago. But showing progress towards AGI keeps the lights on. If they told the truth at their current burn rate…they’d have no money.
You don’t need game theory to figure that one out.
>OpenAI’s credibility is central to its business: overstating capabilities risks public blowback, loss of trust, and regulatory scrutiny.
Uh huh. Kinda like what's happening right now?
They're marketing blow-hards. Everyone knows it. They've been wildly over-stating capabilities (and future capabilities!) as long as Altman has had power, and arguably longer.
They'll do it as long as they can get away with it, because that's all that is needed to make money on it. Factual accuracy rarely impacts the market when it's so hype-driven, especially when there is still some unique utility in the product.
OpenAI's apparent credibility is central to their business.
They're spruiking a 93rd percentile performance on the 2024 International Olympiad in Informatics with 10 hours of processing and 10,000 submissions per question.
Like many startups they're still a machine built to market itself.
From a game-theoretic standpoint, repeated interactions with the public (research community, regulators, and customers) create strong disincentives for OpenAI to lie. In a single-shot scenario, overstating model performance might yield short-term gains—heightened buzz or investment—but repeated play changes the calculus:
1. Reputation as “collateral”
OpenAI’s future deals, collaborations, and community acceptance rely on maintaining credibility. In a repeated game, players who defect (by lying) face future punishment: loss of trust, diminished legitimacy, and skepticism of future claims.
2. Long-term payoff maximization
If OpenAI is caught making inflated claims, the fallout undermines the brand and reduces willingness to engage in future transactions. Therefore, even if there is a short-term payoff, the long-term expected value of accuracy trumps the momentary benefit of deceit.
3. Strong incentives for verification
Independent researchers, open-source projects, and competitor labs can test or replicate claims. The availability of external scrutiny acts as a built-in enforcement mechanism, making dishonest “moves” too risky.
Thus, within the repeated game framework, OpenAI maximizes its overall returns by preserving its credibility rather than lying about capabilities for a short-lived advantage.