#325 Phelim Brady: Why AI's Future Depends on Human Judgement
AI often looks fully automated. But behind the scenes, a huge amount of human judgment is shaping how these systems actually work.
In this episode, Craig Smith speaks with Phelim Bradley, co-founder and CEO of Prolific, a platform that connects millions of real people with researchers and AI labs to evaluate and improve AI systems.
They explore the hidden human layer behind modern AI, why traditional benchmarks are becoming less reliable, and why AI companies increasingly rely on real human feedback to measure model performance in the real world.
Phelim also explains how demographic differences influence how models are evaluated, why human judgment remains critical even as AI improves, and how the collaboration between humans and AI will shape the next phase of development.
This conversation reveals the human backbone behind today's AI systems.
Stay Updated:
Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI
(00:00) Preview and Intro
(02:45) Founding Prolific And Early Pain Points
(06:30) From Mechanical Turk To Representativeness
(09:55) Academic Research And AI Use Cases Split
(13:40) Vetting Real Participants And Fighting Fraud
(17:45) Scale, Community Growth, And Talent Mix
(22:00) High-Complexity Projects Over Commoditised Labeling
(26:40) Measuring Model Persuasion With Live Conversations
(30:20) Demographic-Aware Model Preference Benchmarks
(34:10) The Rise Of Human Evaluation Over Benchmarks
(38:00) Enterprise Model Choice And Continuous Evaluation
(42:00) Why Humans Won't Disappear From The Loop
Are AI Benchmarks Telling The Full Story? [SPONSORED] (Andrew Gordon and Nora Petrova - Prolific)
Is a car that wins a Formula 1 race the best choice for your morning commute? Probably not. In this sponsored deep dive with Prolific, we explore why the same logic applies to Artificial Intelligence. While models are currently shattering records on technical exams, they often fail the most important test of all: **the human experience.**
Why High Benchmark Scores Don’t Mean Better AI
Joining us are **Andrew Gordon** (Staff Researcher in Behavioral Science) and **Nora Petrova** (AI Researcher) from **Prolific**. They reveal the hidden flaws in how we currently rank AI and introduce a more rigorous, "humane" way to measure whether these models are actually helpful, safe, and relatable for real people.
---
Key Insights in This Episode:
* *The F1 Car Analogy:* Andrew explains why a model that excels at the "Humanities Last Exam" might be a nightmare for daily use. Technical benchmarks often ignore the nuances of human communication and adaptability.
* *The "Wild West" of AI Safety:* As users turn to AI for sensitive topics like mental health, Nora highlights the alarming lack of oversight and the "thin veneer" of safety training—citing recent controversial incidents like Grok-3’s "Mecha Hitler."
* *Fixing the "Leaderboard Illusion":* The team critiques current popular rankings like Chatbot Arena, discussing how anonymous, unstratified voting can lead to biased results and how companies can "game" the system.
* *The Xbox Secret to AI Ranking:* Discover how Prolific uses *TrueSkill*—the same algorithm Microsoft developed for Xbox Live matchmaking—to create a fairer, more statistically sound leaderboard for LLMs.
* *The Personality Gap:* Early data from the **Humane Leaderboard** suggests that while AI is getting smarter, it is actually performing *worse* on metrics like personality, culture, and "sycophancy" (the tendency for models to become annoying "people-pleasers").
---
About the HUMAINE Leaderboard
Moving beyond simple "A vs. B" testing, the researchers discuss their new framework that samples participants based on *census data* (Age, Ethnicity, Political Alignment). By using a representative sample of the general public rather than just tech enthusiasts, they are building a standard that reflects the values of the real world.
*Are we building models for benchmarks, or are we building them for humans? It’s time to change the scoreboard.*
Rescript link:
https://app.rescript.info/public/share/IDqwjY9Q43S22qSgL5EkWGFymJwZ3SVxvrfpgHZLXQc
---
TIMESTAMPS:
00:00:00 Introduction & The Benchmarking Problem
00:01:58 The Fractured State of AI Evaluation
00:03:54 AI Safety & Interpretability
00:05:45 Bias in Chatbot Arena
00:06:45 Prolific's Three Pillars Approach
00:09:01 TrueSkill Ranking & Efficient Sampling
00:12:04 Census-Based Representative Sampling
00:13:00 Key Findings: Culture, Personality & Sycophancy
---
REFERENCES:
Paper:
[00:00:15] MMLU
https://arxiv.org/abs/2009.03300
[00:05:10] Constitutional AI
https://arxiv.org/abs/2212.08073
[00:06:45] The Leaderboard Illusion
https://arxiv.org/abs/2504.20879
[00:09:41] HUMAINE Framework Paper
https://huggingface.co/blog/ProlificAI/humaine-framework
Company:
[00:00:30] Prolific
https://www.prolific.com
[00:01:45] Chatbot Arena
https://lmarena.ai/
Person:
[00:00:35] Andrew Gordon
https://www.linkedin.com/in/andrew-gordon-03879919a/
[00:00:45] Nora Petrova
https://www.linkedin.com/in/nora-petrova/
Event:
Algorithm:
[00:09:01] Microsoft TrueSkill
https://www.microsoft.com/en-us/research/project/trueskill-ranking-system/
Leaderboard:
[00:09:21] Prolific HUMAINE Leaderboard
https://www.prolific.com/humaine
[00:09:31] HUMAINE HuggingFace Space
https://huggingface.co/spaces/ProlificAI/humaine-leaderboard
[00:10:21] Prolific AI Leaderboard Portal
https://www.prolific.com/leaderboard
Dataset:
[00:09:51] Prolific Social Reasoning RLHF Dataset
https://huggingface.co/datasets/ProlificAI/social-reasoning-rlhf
Organization:
[00:10:31] MLCommons
https://mlcommons.org/
Why Humans Are Still Powering AI [Sponsored]
Ever wonder where AI models actually get their "intelligence"? We reveal the dirty secret of Silicon Valley: behind every impressive AI system are thousands of real humans providing crucial data, feedback, and expertise.Guest: Phelim Bradley, CEO and Co-founder of ProlificPhelim Bradley runs Prolific, a platform that connects AI companies with verified human experts who help train and evaluate their models. Think of it as a sophisticated marketplace matching the right human expertise to the right AI task - whether that's doctors evaluating medical chatbots or coders reviewing AI-generated software.Prolific: https://prolific.com/?utm_source=mlsthttps://uk.linkedin.com/in/phelim-bradley-84300826The discussion dives into:**The human data pipeline**: How AI companies rely on human intelligence to train, refine, and validate their models - something rarely discussed openly**Quality over quantity**: Why paying humans well and treating them as partners (not commodities) produces better AI training data**The matching challenge**: How Prolific solves the complex problem of finding the right expert for each specific task, similar to matching Uber drivers to riders but with deep expertise requirements**Future of work**: What it means when human expertise becomes an on-demand service, and why this might actually create more opportunities rather than fewer**Geopolitical implications**: Why the centralization of AI development in US tech companies should concern Europe and the UK
The Secret Engine of AI - Prolific [Sponsored] (Sara Saab, Enzo Blindow)
We sat down with Sara Saab (VP of Product at Prolific) and Enzo Blindow (VP of Data and AI at Prolific) to explore the critical role of human evaluation in AI development and the challenges of aligning AI systems with human values. Prolific is a human annotation and orchestration platform for AI used by many of the major AI labs. This is a sponsored show in partnership with Prolific.
**SPONSOR MESSAGES**
—
cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy
Oct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++
Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst
Submit investment deck: https://cyber.fund/contact?utm_source=mlst
—
While technologists want to remove humans from the loop for speed and efficiency, these non-deterministic AI systems actually require more human oversight than ever before. Prolific's approach is to put "well-treated, verified, diversely demographic humans behind an API" - making human feedback as accessible as any other infrastructure service.
When AI models like Grok 4 achieve top scores on technical benchmarks but feel awkward or problematic to use in practice, it exposes the limitations of our current evaluation methods. The guests argue that optimizing for benchmarks may actually weaken model performance in other crucial areas, like cultural sensitivity or natural conversation.
We also discuss Anthropic's research showing that frontier AI models, when given goals and access to information, independently arrived at solutions involving blackmail - without any prompting toward unethical behavior. Even more concerning, the more sophisticated the model, the more susceptible it was to this "agentic misalignment."
Enzo and Sarah present Prolific's "Humane" leaderboard as an alternative to existing benchmarking systems. By stratifying evaluations across diverse demographic groups, they reveal that different populations have vastly different experiences with the same AI models.
Looking ahead, the guests imagine a world where humans take on coaching and teaching roles for AI systems - similar to how we might correct a child or review code. This also raises important questions about working conditions and the evolution of labor in an AI-augmented world. Rather than replacing humans entirely, we may be moving toward more sophisticated forms of human-AI collaboration.
As AI tech becomes more powerful and general-purpose, the quality of human evaluation becomes more critical, not less. We need more representative evaluation frameworks that capture the messy reality of human values and cultural diversity.
Visit Prolific:
https://www.prolific.com/
Sara Saab (VP Product):
https://uk.linkedin.com/in/sarasaab
Enzo Blindow (VP Data & AI):
https://uk.linkedin.com/in/enzoblindow
TRANSCRIPT:
https://app.rescript.info/public/share/xZ31-0kJJ_xp4zFSC-bunC8-hJNkHpbm7Lg88RFcuLE
TOC:
[00:00:00] Intro & Background
[00:03:16] Human-in-the-Loop Challenges
[00:17:19] Can AIs Understand?
[00:32:02] Benchmarking & Vibes
[00:51:00] Agentic Misalignment Study
[01:03:00] Data Quality vs Quantity
[01:16:00] Future of AI Oversight
REFS:
Anthropic Agentic Misalignment
https://www.anthropic.com/research/agentic-misalignment
Value Compass
https://arxiv.org/pdf/2409.09586
Reasoning Models Don’t Always Say What They Think (Anthropic)
https://www.anthropic.com/research/reasoning-models-dont-say-think
https://assets.anthropic.com/m/71876fabef0f0ed4/original/reasoning_models_paper.pdf
Apollo research - science of evals blog post
https://www.apolloresearch.ai/blog/we-need-a-science-of-evals
Leaderboard Illusion
https://www.youtube.com/watch?v=9W_OhS38rIE MLST video
The Leaderboard Illusion [2025]
Shivalika Singh et al
https://arxiv.org/abs/2504.20879
(Truncated, full list on YT)