---
title: "The Best Models Still Reason Like Toddlers"
category: "talks"
date: "2026-06-29"
time: "1:55pm-2:15pm"
track: "Vision & OCR"
room: "Track 2"
speakers: ["Andrew Dai"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Vision & OCR"
scheduleRoom: "Track 2"
scheduleLabels: ["Vision & OCR", "Track 2", "sponsor", "confirmed"]
---
# The Best Models Still Reason Like Toddlers

## Conference Context
- Date/time: 2026-06-29 · 1:55pm-2:15pm
- Track/room: Vision & OCR · Track 2
- Speaker(s): Andrew Dai
- Session type/status: sponsor · confirmed

- Track: Vision & OCR
- Room: Track 2
- Session type: sponsor
- Status: confirmed

## Session Description
Frontier AI models score 80–90% on standard benchmarks like RKGI, yet when tested on visual tasks any 3-year-old handles effortlessly (like counting objects in an image), those same models fall to pieces. I watched this gap widen firsthand during my 14 years at Google Brain and DeepMind, where I co-led development on GLaM, PaLM 2, and Gemini. The problem is that most models hit high RKGI scores not through genuine visual understanding, but by coding – a workaround that scores well and reveals little. Strip that away and you're left with systems that struggle to solve a simple crossword puzzle, identify what's the same or different across two images, or navigate a basic 3D view. These tasks are essential to achieve human-level reasoning capability. And the current benchmark ecosystem wasn’t built to evaluate for it, leaving us with top scoring models that can’t even follow along with Count Von Count. In this talk I'll dig into why the current eval landscape systematically overstates capability, the structural reasons it does so, and how we got here from the viewpoint of someone who was inside a leading frontier lab. I'll close with what I believe a more rigorous, consensus-driven eval framework needs to look like, and why the field needs to build one before the next generation of visual systems ships into the real world. Fixing visual reasoning starts with fixing how we measure it. For engineers building on top of these models today, whether that's document understanding, robotic perception, medical imaging, or any system where visual perception context matters, the cost of getting this wrong is already showing up in production.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[andrew-dai]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# The Best Models Still Reason Like Toddlers ## Conference Context - Date/time: 2026-06-29 · 1:55pm-2:15pm - Track/room: Vision & OCR · Track 2 - Speaker(s): Andrew Dai - Session type/status: sponsor · confirmed - Track: Vision & OCR - Room: Track 2 - Session type: sponsor - Status: confirmed ## Session Description Frontier AI models score 80–90% on standard benchmarks like RKGI, yet when tested on visual tasks any 3-year-old handles effortlessly (like counting objects in an image), those same models fall to pieces. I watched this gap widen firsthand during my 14 years at Google Brain and DeepMind, where I co-led development on GLaM, PaLM 2, and Gemini. The problem is that most models hit high RKGI scores not through genuine visual understanding, but by coding – a workaround that scores well and reveals little. Strip that away and you're left with systems that struggle to solve a simple crossword puzzle, identify what's the same or different across two images, or navigate a basic 3D view.

### Speaker And Company Context
- [[andrew-dai|Andrew Dai]] — Co-founder and CEO at [[elorian|Elorian]].

### Topics Covered
- Topic links are pending transcript-backed classification.

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.