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HomeTechnology peripheralsAIWhy Sam Altman And Others Are Now Using Vibes As A New Gauge For The Latest Progress In AI

Why Sam Altman And Others Are Now Using Vibes As A New Gauge For The Latest Progress In AI

Let's discuss the rising use of "vibes" as an evaluation metric in the AI field. This analysis is part of my ongoing Forbes column on AI advancements, exploring complex aspects of AI development (see link here).

Vibes in AI Assessment

Traditionally, generative AI and LLMs are evaluated using quantitative metrics like TTFT (time to first token) and TPS (tokens per second). While debate continues on the best metrics, the process relies on measurable data. While AI developers might attempt to manipulate results, independent verification often reveals discrepancies. Ultimately, numbers provide a relatively objective assessment.

Recently, a more subjective evaluation method has emerged: "vibes." AI developers may claim their LLM possesses "great vibes," suggesting that even if hard metrics are lacking, the overall feel or experience compensates.

The problem? "Vibes" are entirely subjective. This creates a convenient loophole for developers; claims are difficult to refute. Challenging the assertion of "good vibes" is met with the response that it's a matter of personal perception. This allows developers to continue promoting positive, yet unsubstantiated, claims.

The widespread adoption of "vibes" as a legitimate metric threatens the accurate assessment of AI progress.

The Origins of "AI Vibes"

The trend toward using "vibes" can be traced to tweets by OpenAI's Sam Altman in early 2025. He isn't alone; a future article will explore the rise of "vibe coding" initiated by another AI leader.

Altman's March 11th, 2025, X post exemplifies this: "We trained a new model excelling at creative writing (release date TBD). For the first time, I'm truly impressed by AI writing; it perfectly captured the metafiction vibe."

Earlier posts (February 27th, 2025) hinted at AI's "magic" and a shift towards a "different kind of intelligence":

  • "GPT-4.5 is ready! It feels like talking to a thoughtful person. I've been astonished by the advice received."
  • "It's not a reasoning model, won't top benchmarks. It's a different intelligence, with a magic I haven't felt before. Excited for people to try it!"

Some in the AI community embrace "vibes," arguing that quantitative measures alone are insufficient. They draw a parallel to human assessment, suggesting that overall "chemistry" is crucial.

Critics, however, argue this is flawed, a form of anthropomorphism. They see the concept as detrimental, potentially becoming ingrained in AI discourse. A heated debate continues.

Arguments For and Against "Vibes"

Discussions with fellow AI researchers revealed key arguments for and against using "vibes":

Arguments for "Vibes":

  1. Visceral Essence: Some LLMs create an emotional connection with users, a quality deserving recognition.
  2. Positive Connotations: The positive association encourages wider adoption of generative AI.
  3. Differentiation: When quantitative metrics are similar, "vibes" can distinguish between LLMs.
  4. Catch-All Term: "Vibes" is universally understood, avoiding the need for new, potentially confusing terminology.

Arguments Against "Vibes":

  1. Subjectivity: The amorphous nature of "vibes" makes it unreliable for objective assessment.
  2. Mystification of AI: Associating "vibes" with AI implies a mysterious, potentially sentient quality.
  3. Masking Slowdowns: "Vibes" can distract from a lack of substantial AI advancements.
  4. Lack of Comparability: The subjective nature hinders meaningful comparisons between LLMs.

Making "Vibes" Measurable

Eliminating the use of "vibes" is unlikely. Its appeal is too strong. However, we could attempt to quantify "vibes" by breaking them down into measurable components.

This could involve:

  • Analyzing conversational flow (responsiveness, dialogue turns).
  • Measuring sentiment alignment (comparing AI and user tone).
  • Assessing engagement and likeability through user ratings.

Establishing standardized metrics would encourage transparency and prevent developers from creating proprietary measures. Leaderboards could then incorporate "vibes" metrics, allowing for independent verification and preventing manipulation.

Ultimately, some argue that quantifying "vibes" contradicts its essence. However, given its prevalence, attempting to make it a measurable metric might be necessary to maintain objectivity in AI assessment. I remain optimistic about this approach.

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