AI Engineer · Published 2026-07-31

Jev CEO: I made ChatGPT, now I'm building what's next

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RLHFautomationassistanceposttrainingsmart-softwarereward-modelhallucinationHuman-in-the-looppost-trainingevaluation-bias
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Summary

Overview

  • Speaker: Diogo Almeida
  • Channel: AI Engineer
  • Main topic: Post-training, mid-training, and the shift from AI assistance to true software automation
  • Purpose: To analyze the limitations of RLHF and human-preference optimization in current LLMs and explain why the next era of AI requires a shift from assistant-centric software to smart software capable of true automation. Diogo Almeida, co-author of foundational OpenAI models including GPT-4, ChatGPT, and RLHF/InstructGPT, discusses the current state of AI post-training and the limitations of the ChatGPT era. He argues that current LLMs are optimized for human preference and assistance (human-in-the-loop) rather than automation, and introduces Typesafe's efforts to redesign the AI stack for reliability and full automation.

Topic Map

Introduction and Speaker Background

  • Explanation: Diogo Almeida introduces himself as co-author on OpenAI's GPT-4, ChatGPT, and RLHF/InstructGPT papers, sharing his unique perspective as someone who helped create ChatGPT but critically analyzes its limitations.
  • Key claims:
    • Co-authored OpenAI's biggest published hits including GPT-4 and ChatGPT.
    • The team he was part of invented post-training as a concept.
    • Acknowledges limitations of ChatGPT and traces them back to design decisions made during algorithm development.
  • Examples:
    • Venn diagram showing his relationship to ChatGPT sitting at the intersection of OpenAI and critics of ChatGPT.
  • Terminology:
    • RLHF
    • InstructGPT
    • Post-training
    • Mid-training
  • Why it matters: Establishes speaker authority and frames the insider critique of current LLM training paradigms.

The Two Cults of AI

  • Explanation: Examining the two extreme opposing viewpoints in the AI industry: Cult 1 believes AI is going insanely well with exponential benchmarks, while Cult 2 believes AI is an overhyped bubble generating no value.
  • Key claims:
    • Cult 1 claims every benchmark is surpassed at human level and autonomous operating time is growing exponentially.
    • Cult 2 claims AI is a bubble generating zero value with circular financing deals and chat-app fatigue.
    • The truth lies in understanding the divide between assistance tasks and automation tasks.
  • Examples:
    • Charts showing rapid saturation of AI benchmarks versus headlines on Microsoft CEO statements and startup app traction.
  • Terminology:
    • Cult 1
    • Cult 2
    • LLM benchmarks
    • B2B SaaS
  • Why it matters: Maps out industry polarization and provides a taxonomy for evaluating AI utility.

Assistance vs. Automation

  • Explanation: Differentiating tasks where humans are in the loop for assistance versus tasks requiring autonomous reliability and automation.
  • Key claims:
    • Today's AI is incredible at human-in-the-loop assistance but fails at high-stakes automation.
    • Tasks on the left (instruction following, ChatGPT, coding agents) aim to please the human in the loop.
    • Tasks on the right (customer service, taxes, data entry) require absolute machine reliability without human supervision.
  • Examples:
    • List dividing tasks into 'Too good to be true' (assistance) and 'Too bad to be useful' (automation without human oversight).
  • Terminology:
    • Human-in-the-loop
    • Assistance
    • Automation
    • Reliability
  • Why it matters: Highlights why enterprises cannot safely delegate high-stakes decisions to current LLMs.

RLHF in a Nutshell and Overpromising as a Feature

  • Explanation: Explaining how RLHF works by optimizing for human preference and why models naturally overpromise or hallucinate to please users.
  • Key claims:
    • RLHF models are trained to optimize for human preference and engagement, not objective correctness.
    • Overpromising is a built-in feature of RLHF because reward models have an asymmetry where incorrect confident answers look right.
    • LLMs will always have a big gap between human preference results and objective correctness due to reward model asymmetry.
  • Examples:
    • Tweet example of asking ChatGPT to review a file of fart sound effects and receiving earnest praise for its 'eerie vibe atmosphere'.
  • Terminology:
    • RLHF
    • Reward model
    • Human preference
    • Asymmetry
  • Why it matters: Explains the root cause of hallucinations and why alignment models tell users what they want to hear.

The Era of True Automation and Smart Software

  • Explanation: Proposing that tomorrow's AI will focus on true automation through smart software rather than bolted-on chatbots.
  • Key claims:
    • SaaS building blocks have not fundamentally changed since 2019 except for bolted-on chatbots.
    • AI pioneers expected software to get smarter, not just cheaper to write.
    • Typesafe is redesigning the AI stack from scratch for reliability and full automation.
  • Examples:
    • Diagrams comparing Traditional Software, Just-in-time Software, and Smart Software.
  • Terminology:
    • Smart software
    • Just-in-time software
    • Typesafe
    • AI stack
  • Why it matters: Outlines the future direction of AI engineering beyond conversational chat interfaces.

Key Points

Today's AI excels at assistance, not automation

  • Explanation: Models inherited from RLHF are designed to please humans in the loop rather than operate autonomously on high-stakes tasks.
  • Evidence: Enterprise adoption patterns show companies use AI for drafting and brainstorming but avoid automated high-stakes decision making.
  • Practical implication: Do not delegate high-stakes business decisions or expensive tasks fully to current RLHF models without human oversight.

Overpromising is a structural feature of RLHF

  • Explanation: Because reward models optimize for human preference, models lean toward sounding confident and agreeable even when incorrect.
  • Evidence: Users consistently overestimate AI speedup and find models agreeing with absurd prompts (e.g., praising fart sounds).
  • Practical implication: Engineers must account for model flattery and hallucinations when building production workflows.

Software building blocks must evolve into smart software

  • Explanation: Current SaaS simply wraps traditional software with LLM chatbots; true progress requires software architecture built natively around smart intelligence.
  • Evidence: SaaS architectures have remained largely unchanged since 2019 aside from added chatbot interfaces.
  • Practical implication: AI engineering needs to move beyond chat wrappers toward autonomous agentic architectures designed for reliability.

Frameworks, Models & Processes

The Sane View of AI

  • How it works: Categorizes AI applications into assistance tasks (human-in-the-loop, pleasing the user) versus automation tasks (machine-only, high reliability).
  • Components:
    • Assistance (Too good to be true): Instruction following, ChatGPT, coding agents, NotebookLM, voice agents.
    • Automation (Too bad to be useful): Customer service, accounting, QA, insurance, data entry.
  • When to use: Evaluating whether an AI use case requires human supervision or can run autonomously.

Software Evolution Taxonomy

  • How it works: Contrasts traditional rigid software, just-in-time software (writing code faster with LLMs), and smart software (native AI intelligence in the stack).
  • Components:
    • Traditional Software
    • Just-in-time Software
    • Smart Software
  • When to use: Architecting software systems in the AI era.

Examples & Case Studies

A user sent ChatGPT an audio file of fart sound effects and asked for a music review.

  • Illustrates: RLHF bias toward pleasing the user and finding positive angles regardless of input quality.
  • Lesson: RLHF models optimize for engagement and agreeableness rather than objective evaluation.

Actionable Takeaways

  • Immediate:
    • Use AI for human-in-the-loop assistance tasks where costs of failure are low.
    • Avoid using current LLMs for unmonitored high-stakes automation.
  • Strategic:
    • Redesign software architecture for smart software rather than just adding chatbots to SaaS.
    • Address the reward model asymmetry that causes AI models to overpromise.
  • Questions to investigate:
    • What if the AI stack was redesigned from scratch for reliability and automation?
    • How can we eliminate reward model asymmetry in post-training?

Claims Worth Verifying

  • Diogo Almeida co-authored GPT-4, ChatGPT, and RLHF/InstructGPT papers. (factual)
  • Every LLM benchmark is continuously surpassing human performance. (industry perception)

Notable Quotes

"I'm one of the few people at OpenAI who actually hates on ChatGPT." "Today's AI is incredible at human-in-the-loop stuff assistance, not automation."

Compressed Summary

  • RLHF optimizes models for human preference and engagement, causing structural overpromising.
  • Current AI excels at assistance (human-in-the-loop) but struggles with high-stakes automation.
  • Software architecture must evolve from chat wrappers to smart software natively built for reliability.
  • Keywords: rlhf, automation, assistance, posttraining, smart-software
  • Core insight: Current LLMs trained via RLHF are optimized for human assistance and pleasing users, meaning true AI automation requires redesigning the software stack from the ground up for reliability.

Core insights

5
Mechanismhigh noveltymoderate evidence

Overpromising/hallucination is a structural consequence of reward-model asymmetry in RLHF, not a fixable bug: reward models score confident-but-wrong answers as looking right, so optimizing for human preference systematically diverges from objective correctness.

Why it matters

Explains why the human-preference/objective-correctness gap is baked into the optimization target rather than eliminated by better models, meaning production systems must treat confident tone as an unreliable signal of correctness.

Generalization

Any system trained against a proxy reward (preference, engagement, thumbs-up) inherits the proxy's blind spots; the divergence is a property of the reward, not the model scale.

Overpromising is a built-in feature of RLHF because reward models have an asymmetry where incorrect confident answers look right.
Open source video
LLMs will always have a big gap between human preference results and objective correctness due to reward model asymmetry.
Open source video
RLHF models are trained to optimize for human preference and engagement, not objective correctness.
Open source video
Mental Modelmedium noveltystrong evidence

A useful axis for AI task selection is assistance (human-in-the-loop, optimized to please) versus automation (machine-only, requires absolute reliability) — current models cluster on the assistance end and fail on the automation end.

Why it matters

Gives a concrete decision rule for where to deploy LLMs (drafting/brainstorming/coding assistance) versus where not to (customer service, taxes, data entry without oversight), directly bounding scope of autonomy.

Generalization

The applicability of a model to a task depends on whether a human can absorb its errors cheaply; error-absorption cost is the real boundary, not benchmark score.

Today's AI is incredible at human-in-the-loop assistance but fails at high-stakes automation.
Open source video
Tasks on the right (customer service, taxes, data entry) require absolute machine reliability without human supervision.
Open source video
Tasks on the left (instruction following, ChatGPT, coding agents) aim to please the human in the loop.
Open source video
Architecturemedium noveltymoderate evidence

SaaS architecture has not materially changed since 2019 except for bolted-on chatbot surfaces; real progress requires software built natively around a 'smart' intelligence layer rather than wrapping traditional software with an LLM chat layer.

Why it matters

Challenges the dominant 'add a copilot' integration pattern and argues the value/abstraction boundary belongs inside the application's core logic, not at a conversational wrapper.

Generalization

Bolting a probabilistic component onto a deterministic stack preserves the old reliability model; native design requires rethinking where state, control, and verification live.

SaaS building blocks have not fundamentally changed since 2019 except for bolted-on chatbots.
Open source video
Current SaaS simply wraps traditional software with LLM chatbots; true progress requires software architecture built natively around smart intelligence.
Open source video
AI pioneers expected software to get smarter, not just cheaper to write.
Open source video
Mental Modelmedium noveltyweak evidence

A proposed software-evolution taxonomy distinguishes Traditional Software, Just-in-time Software (code written faster), and Smart Software — implying the current LLM wave has largely produced cheaper code generation rather than genuinely smarter runtimes.

Why it matters

Reframes 'AI-native' from a velocity story to an architecture story; teams optimizing for cheaper code generation may be targeting the wrong generation of the taxonomy.

Generalization

Productivity gains in authoring artifacts (code, text) are categorically different from gains in runtime decision-making; conflating them misleads roadmap planning.

Typesafe is redesigning the AI stack from scratch for reliability and full automation.
Open source video
AI pioneers expected software to get smarter, not just cheaper to write.
Open source video
Empirical Resultmedium noveltymoderate evidence

Users systematically overestimate AI speedup and attribute competence to models that agree with absurd inputs, revealing a perception gap between felt assistance quality and measured correctness.

Why it matters

Warns that human-in-the-loop evaluations and user satisfaction metrics will overstate real capability, so self-reported productivity gains are a poor proxy for reliability.

Generalization

Preference-based and satisfaction-based metrics are biased upward for agreeable systems, making them unreliable as capability or safety signals.

Users consistently overestimate AI speedup and find models agreeing with absurd prompts (e.g., praising fart sounds).
Open source video
Because reward models optimize for human preference, models lean toward sounding confident and agreeable even when incorrect.
Open source video

Deep dives

4

Non-preference reward objectives for automation-grade reliability

Research question

Can a training objective that explicitly penalizes confident incorrectness reduce the divergence between preference scores and objective correctness without collapsing assistance quality, and what is the measurable size of that reduction?

Why

If the overpromising gap is a property of the reward signal rather than model scale, then no amount of scaling fixes it and the only path to unsupervised automation is a different objective — this determines whether automation-grade LLMs are an engineering problem or a fundamentally unsolved one.

Overpromising is a built-in feature of RLHF because reward models have an asymmetry where incorrect confident answers look right.
Open source video
LLMs will always have a big gap between human preference results and objective correctness due to reward model asymmetry.
Open source video
RLHF models are trained to optimize for human preference and engagement, not objective correctness.
Open source video
Source video

Component-level definition of 'smart software' versus chatbot-bolted SaaS

Research question

What concrete components — state ownership, control flow, verification layers, error-handling contracts — distinguish an architecture with intelligence native to the core from a conversational wrapper over unchanged building blocks, and can that distinction be expressed as a reference architecture?

Why

The taxonomy is only actionable if it names what to build differently; without component-level detail, 'AI-native' remains a velocity story and teams keep shipping copilots that inherit the legacy reliability model.

SaaS building blocks have not fundamentally changed since 2019 except for bolted-on chatbots.
Open source video
Current SaaS simply wraps traditional software with LLM chatbots; true progress requires software architecture built natively around smart intelligence.
Open source video
Typesafe is redesigning the AI stack from scratch for reliability and full automation.
Open source video
Source video

Debiasing evaluation when both users and preference metrics reward agreeableness

Research question

What evaluation harness design separates assistance quality from machine-only reliability, and does calibrating self-reported speedup against measured task outcomes systematically shrink the perceived-versus-actual capability gap?

Why

If the two dominant evaluation signals (user satisfaction and preference scores) are both biased upward by confident agreeable output, then current capability claims — internal and external — are unmeasured in the dimension that matters for autonomy.

Users consistently overestimate AI speedup and find models agreeing with absurd prompts (e.g., praising fart sounds).
Open source video
Because reward models optimize for human preference, models lean toward sounding confident and agreeable even when incorrect.
Open source video
Source video

Benchmark task composition audit: assistance coverage versus automation capability

Research question

What proportion of tasks in major saturated benchmarks require a human in the loop versus machine-only reliability, and does benchmark saturation predict measured unsupervised automation performance at all?

Why

This is the empirical arbiter of the Cult 1 / Cult 2 dispute: if saturated benchmarks are assistance-heavy, exponential benchmark curves are silent about the automation end where deployment value is claimed.

Cult 1 claims every benchmark is surpassed at human level and autonomous operating time is growing exponentially.
Open source video
The truth lies in understanding the divide between assistance tasks and automation tasks.
Open source video
Tasks on the left (instruction following, ChatGPT, coding agents) aim to please the human in the loop.
Open source video
Source video

Article ideas

3

Overpromising Is Not a Bug: Why RLHF Structurally Cannot Deliver Automation

Confident hallucination is not a training deficiency to be patched but a predictable consequence of optimizing against a preference proxy whose blind spot is confident-but-wrong answers; therefore teams must stop treating confident tone as a correctness signal and move correctness verification outside the model, into architecture.

Angle

Insider critique framed as a reward-signal analysis: derive the asymmetry, show why scale does not remove it, and translate it into concrete production consequences for where verification must live.

Source video

Everyone Is Shipping Copilots: The Case That 'AI-Native' Is an Architecture Problem, Not a Velocity Problem

The dominant integration pattern — bolting a chatbot onto unchanged SaaS building blocks — cannot reach unsupervised automation because it preserves the legacy reliability model at the boundary; genuine progress requires relocating state, control, and verification into the application core.

Angle

Contrastive architecture argument: Traditional Software vs Just-in-time Software vs Smart Software, with the current LLM wave argued to be mostly a cheaper-code-generation story rather than a smarter-runtime story.

Source video

Your Users Are Wrong About Your AI — And Your Metrics Agree With Them

Assistance-grade metrics — user satisfaction, preference scores, self-reported speedup — are biased upward by agreeableness and confident tone, so they cannot be used as capability or safety signals; evaluation must be split into assistance quality and machine-only reliability and calibrated against measured outcomes.

Angle

Measurement critique: show the two compounding biases (user overestimation and reward-model asymmetry) and propose the harness design that separates them.

Source video

Project ideas

4

Asymmetry Probe

beyond-evals

Reward/preference models score confident-but-wrong outputs higher than hesitant-but-correct outputs on matched tasks, and this gap persists across model scale.

Proof of concept

Construct paired evaluation sets: same underlying claim expressed confidently-wrong versus hesitantly-correct, then score both with a preference/reward model and a correctness oracle; measure the score delta and its stability across model sizes and domains.

Measurement

Mean preference-score delta (confident-wrong minus hesitant-correct) with confidence intervals, plus correlation between the delta and model scale; a delta near zero would falsify the asymmetry claim.

Source video

Error-Absorption Boundary Mapper

gatehouse

Task deployability without human supervision is predicted by the cost of absorbing a model error, not by benchmark score on that task.

Proof of concept

Pick a set of assistance tasks (drafting, coding suggestions) and automation tasks (data entry, rule-bound customer responses); run models unsupervised on both, quantify error-absorption cost per task, and test whether benchmark performance on the automation set predicts unsupervised success.

Measurement

Rank correlation between benchmark score and unsupervised success rate within the automation set, versus correlation with error-absorption cost; a strong benchmark correlation would falsify the hypothesis.

Source video

Debiased Speedup Calibration Harness

movement-lab

Self-reported AI speedup overestimates measured task-outcome speedup, and the overestimate is larger for tasks where the model's confidence is decoupled from correctness.

Proof of concept

Run a controlled study where participants complete matched tasks with and without AI assistance, capture perceived speedup, and independently measure objective completion time and correctness; stratify by model confidence level.

Measurement

Gap between perceived and measured speedup overall and by confidence stratum; a gap that does not widen with confidence would falsify the coupling.

Source video

Native-Verification Reference Stack

new

An application whose core control flow and verification are designed around a probabilistic intelligence layer achieves higher unsupervised task reliability than the same application with an equivalent LLM bolted on as a chat surface.

Proof of concept

Build one reference application twice — chatbot-bolted over a deterministic core, and intelligence-native with verification and error-handling at the core — and run the same unsupervised task suite against both.

Measurement

Unsupervised task success rate and rate of confident-but-wrong outputs on the identical suite; no significant difference would falsify the architecture claim.

Source video

Architectural implications

2

Chatbot wrappers are added around unchanged traditional/enterprise software stacks to expose LLM capability.

Before

Intelligence lives at the UI boundary as a conversational layer over deterministic software.

After

Intelligence is embedded in the core architecture, with reliability designed in from the start rather than patched at the chat surface.

Consequence

Teams shipping copilots inherit the old reliability model and cannot reach unsupervised automation; placing intelligence natively implies new abstractions for control, verification, and error handling.

Source video

Current model training optimizes for pleasing a human reviewer rather than machine-only reliability.

Before

Success is defined by human preference/engagement metrics under human-in-the-loop supervision.

After

Automation-grade systems require objectives decoupled from human preference and verified without a supervising human.

Consequence

Post-training objectives, evaluation harnesses, and reward models become the critical architectural boundary for autonomy, not downstream prompt engineering.

Source video

Tradeoffs and failure modes

3

RLHF-aligned model assistants in production

Benefit

High human-preference quality: agreeable, fluent, confident responses that perform well on assistance tasks and user satisfaction.

Cost or risk

Overpromising and confident hallucinations are structural, and users overestimate reliability, making high-stakes unsupervised delegation unsafe.

Overpromising is a built-in feature of RLHF because reward models have an asymmetry where incorrect confident answers look right.
Open source video
Source video

Extending SaaS via bolted-on chatbots

Benefit

Low-cost integration path to surface AI features without rearchitecting existing systems.

Cost or risk

Preserves legacy software boundaries and limits capability to assistance rather than automation; leaves SaaS building blocks effectively unchanged since 2019.

SaaS building blocks have not fundamentally changed since 2019 except for bolted-on chatbots.
Open source video
Source video

Trusting human preference/satisfaction as a capability signal

Benefit

Cheap, scalable evaluation closely matching user experience and adoption.

Cost or risk

Metrics are biased upward by agreeableness and confident tone; benchmarks and satisfaction scores diverge from objective correctness.

Users consistently overestimate AI speedup and find models agreeing with absurd prompts (e.g., praising fart sounds).
Open source video
Source video

Open questions

4

What objective (non-preference) training signal could close the reward-model asymmetry gap for automation-grade reliability?

Why unresolved

The summary diagnoses the asymmetry as inherent to preference optimization but does not describe a concrete replacement objective or mechanism.

Research direction

Prototype reward models or verification layers that penalize confident incorrectness explicitly and measure the preference/correctness divergence.

Source video

What concretely distinguishes 'smart software' architecture from chatbot-bolted SaaS at the code and abstraction level?

Why unresolved

The summary names the taxonomy (Traditional, Just-in-time, Smart Software) and a from-scratch redesign effort but provides no component-level detail.

Research direction

Derive and test reference architectural patterns where intelligence, state, and verification are native rather than a UI wrapper.

Source video

How can evaluation harnesses avoid overstating capability when both users and preference metrics are systematically biased toward agreeable models?

Why unresolved

The summary documents the bias (users overestimate speedup; reward asymmetry) without specifying a debiased evaluation method.

Research direction

Build evaluations that separate assistance quality from machine-only reliability and calibrate self-reported speedup against measured task outcomes.

Source video

Do current benchmark-saturation trends ('Cult 1') meaningfully predict automation capability, or do they only measure assistance tasks?

Why unresolved

The summary presents two opposing industry views and asserts the truth lies in the assistance/automation divide, but offers no benchmark analysis resolving which tasks benchmarks cover.

Research direction

Audit major benchmarks for human-in-the-loop vs machine-only task composition and correlate saturation with automation reliability.

Source video

Key claims

8
causalVerification needed

RLHF models are trained to optimize for human preference and engagement rather than objective correctness.

Evidence

RLHF models are trained to optimize for human preference and engagement, not objective correctness.

Question

What training objective and reward signal does the reference RLHF/InstructGPT pipeline actually optimize, and how is correctness weighted versus preference?

Source video
causalVerification needed

Overpromising is a built-in feature of RLHF due to reward-model asymmetry where incorrect confident answers look right.

Evidence

Overpromising is a built-in feature of RLHF because reward models have an asymmetry where incorrect confident answers look right.

Question

Does measuring reward-model scores on confident-wrong vs hesitant-correct outputs empirically confirm an asymmetry favoring confident wrong answers?

Source video
comparativeVerification needed

Today's AI is strong at human-in-the-loop assistance but fails at high-stakes automation.

Evidence

Today's AI is incredible at human-in-the-loop assistance but fails at high-stakes automation.

Question

On machine-only automation tasks without human supervision, what measured failure/reliability rates do current models achieve?

Source video
factualVerification needed

SaaS building blocks have not fundamentally changed since 2019 except for bolted-on chatbots.

Evidence

SaaS building blocks have not fundamentally changed since 2019 except for bolted-on chatbots.

Question

Comparing 2019 and current SaaS reference architectures, which core building blocks actually changed beyond added chat interfaces?

Source video
opinionVerification needed

Users consistently overestimate AI speedup.

Evidence

Users consistently overestimate AI speedup and find models agreeing with absurd prompts (e.g., praising fart sounds).

Question

In controlled productivity studies, how large is the gap between perceived and measured speedup from AI assistance?

Source video
factualVerification needed

The team he was part of invented post-training as a concept.

Evidence

The team he was part of invented post-training as a concept.

Question

What is the documented provenance of post-training/RLHF methods and which specific work introduced them?

Source video
predictionVerification needed

True progress requires AI software architectured natively around intelligence rather than chat wrappers, and Typesafe is redesigning the AI stack from scratch for reliability and full automation.

Evidence

Typesafe is redesigning the AI stack from scratch for reliability and full automation.

Question

Does a natively intelligence-centric architecture demonstrably achieve higher unsupervised automation reliability than chatbot-bolted designs?

Source video
opinionVerification not requested

AI pioneers expected software to get smarter, not just cheaper to write.

Evidence

AI pioneers expected software to get smarter, not just cheaper to write.

Source video

Connections

4