Some predictions for 2024 – keeping only the more controversial ones. You certainly saw the non-controversial ones (multimodality, etc) already 1. At least 10 new unicorn companies building SOTA open foundation models in 2024 Stars are so aligned: - a smart, small and dedicated team can reach close to OpenAI level in a few months as Mistral, or showed - non-AI startups are struggling to raise money while VCs are eager to join the AI revolution - knowledge around training large models keeps spreading from teams to teams each time a new SOTA model is created leading many new venture to form - consequence: we now understand much better the push for regulatory capture that we witnessed from early AI-labs in 2023 (e.g. the idea of licences to train models, etc) 2. 2024 will be reality-check for older AI-unicorn pioneers Optimistic bet: we'll see a few older AI unicorn startups successfully transitioning to sustainable business models by leveraging widespread public adoption of AI 3. Model quality will be harder and harder to evaluate in 2024 With the surge of models and the saturation of open-benchmarks, users will tend to fall back on "brand quality perception". It will become essential for new teams to not be perceived as cheating on public evals and leaderboards 4. The return of academia Academia is back as we saw at NeurIPS 2023. With many private and open-source labs closing the doors on publishing their results and data, academia rise again in visibility and is shining with many impactful papers in 2023 and exciting new work coming 5. Dangerous times for annotation companies It's much easier to quickly spin and iterate on a pay-by-usage API than to hire and manage annotators. With model performance strongly improving and the privacy guarantee of open models, it will be harder and harder to justify making complex annotations contracts. 6. The rise of synthetic data We're running out of human data, there are many copyright questions on these and our largest models are already reaching human level annotation on many tasks. The next step coming is about large and good quality synthetic data.