References: Where multimodal AI is going

Source material

Source material (the track's primary curriculum):
• Stanford CS25: Transformers United
  V4 (Spring 2024): https://web.stanford.edu/class/cs25/past/cs25-v4/
  V5 (Spring 2025): https://web.stanford.edu/class/cs25/past/cs25-v5/
  V6 (Spring 2026): https://web.stanford.edu/class/cs25/
  Instructors: Steven Feng, Karan Singh, Michael C. Frank, Christopher Manning,
               with rotating guest speakers each edition
  License: as published on Stanford's public CS25 YouTube channel (link-out only)

This lesson is the Clawdemy-authored closer of T24. Like L1 (the orientation
opener), it draws on the CS25 series as a whole rather than mirroring a single
lecture; each of L2 through L9 maps to a specific CS25 guest lecture and cites
it as that lesson's primary source. This closer synthesizes the cross-cutting
threads across all nine prior lessons + names the frontiers the track did not
cover.

Clawdemy provides original notes, summaries, and quizzes derived from this
material for educational purposes. All rights to the original lectures remain
with Stanford and the speakers.

What this closer draws from

This lesson is a synthesis. It draws on every lesson of T24 (L1-L9) and the CS25 V4/V5/V6 lectures those lessons mirror, plus the publicly-documented multimodal-AI literature each lesson’s references already cite. No new primary source.

The six-thread structure (tokenize-everything + one transformer, fusion-pushed-earlier, tokenizer-as-floor-and-ceiling, generative-vs-JEPA paradigm tension, capability stacks, scope-line discipline), the explicit naming of what the track did not cover, and the trajectory framing are Clawdemy’s own.

Going deeper (per interest)

The strongest next-direction pointers depend on which thread pulled you most.

The architectural side (Threads 1, 2, 3, 5 in this closer). Stanford CS25: Transformers United continues each year; new editions bring new frontier lectures. The track’s per-lesson references.mdx files cite the specific papers and lectures per topic.
The training-paradigm side (Thread 4). The I-JEPA paper (Assran et al., 2023), V-JEPA paper (Bardes et al., 2024), and LeCun’s “A Path Towards Autonomous Machine Intelligence” position paper (2022) together are the strongest reading on the JEPA direction.
The production-engineering side (Threads 5, 6 as applied in L9). Karina Nguyen’s talks are the speaker’s own collection; the broader public AI-engineering literature on RL co-design and evaluation harness design is younger and dispersed, so reading widely is the right posture.
The scope-line meta-pattern (Thread 6). This is a habit worth carrying beyond multimodal AI. Apply it to any technical reading where engineering work touches non-engineering conversations; the discriminating-instrument question travels well.

Adjacent Clawdemy tracks

T11: Intro to Deep Learning. Foundational coverage that prepares the architectural side of this track.
T13: Build Neural Networks from Scratch. The Karpathy-style depth on what transformers actually are at the matrix level; complements every architecture lesson in T24.
T20: AI Agents and Tool Use. Goes deeper on agent design and tool use, the territory L4 and L9 gesture toward (planning, memory, multi-agent systems, metacognition).

Community discussion

None selected for this closer. The CS25 series itself is the canonical public discussion; the per-lesson references already point to the strongest reading per topic. If a single resource emerges that synthesizes multimodal AI as of 2026 at the right level for this audience, it will be added at the next review.