Key Moments
The Four Wars of the AI Stack - Dec 2023 Recap
Latent Space PodcastKey Moments
AI wars in Data, GPUs, Multimodality, and Ops; emergence of synthetic data, efficiency focus, and hardware.
Key Insights
The AI landscape is defined by "four wars": Data, GPUs/Inference, Multimodality, and RAG Ops, reflecting key battlegrounds for development and investment.
The "Data War" centers on copyright, fair use, and compensation for creators as AI models consume vast amounts of information, leading to lawsuits and new data partnership models.
The "GPU/Inference War" showcases a race to the bottom in pricing, with companies potentially losing money to gain market share, highlighting a complex economic dynamic.
Multimodality is rapidly expanding beyond text-to-image, with significant growth in 3D, video, and voice synthesis, creating new markets and investment opportunities.
Emerging architectures like Mamba offer potential efficiency gains over transformers, shifting focus from just long context to overall computational performance.
The "RAG Ops" space, though initially hyped, remains crucial, with ongoing development in databases and frameworks to make AI operations more robust and useful.
New hardware and form factors (e.g., Rabbit R1, Humane AI Pin, Apple Vision Pro) are emerging, aiming to make AI more integrated and contextually aware in daily life, albeit with privacy concerns.
THE DATA WAR: FIGHTING FOR INTELLECTUAL PROPERTY
The "Data War" is a critical battleground concerning the use of copyrighted material for AI training. Key players range from content creators and journalists to AI researchers and startups. The core issues revolve around attribution, fair use, and creator compensation, exemplified by lawsuits like The New York Times against OpenAI. This conflict dictates how data is sourced, used, and whether creators will be compensated, potentially shaping the future of AI development and content creation.
SYNTHETIC DATA'S RISE AMIDST DATA LOCKDOWN
As human-generated data becomes increasingly locked down and litigated, synthetic data is emerging as a pivotal alternative. Researchers are exploring methods to generate high-quality, verifiably correct synthetic datasets, particularly for domains like math and code. While challenges remain in emulating human nuance and avoiding the perpetuation of model flaws, synthetic data generation is poised to become a major investment area, essential for continued AI progress.
THE GPU AND INFERENCE WAR: A RACE TO THE BOTTOM
The "GPU/Inference War" is characterized by aggressive price competition among inference providers, sparked by models like Mixtral. Companies are slicing prices dramatically, leading to a situation where many are likely operating at a loss. This race for cost leadership is forcing a re-evaluation of what truly matters beyond price, such as latency, uptime, and throughput. Independent benchmarks are crucial for navigating this complex and potentially unsustainable market.
ADVANCEMENTS IN MIXED EXPERTS AND HARDWARE EFFICIENCY
The rise of Mixture-of-Experts (MoE) models, like Mixtral, presents new challenges and opportunities in inference. These models require significant memory to hold all weights, even if only a subset is active, necessitating custom optimizations and hardware. This trend is driving innovation in areas like custom kernels for specific hardware (e.g., H100) and pushing the boundaries of model quantization, impacting inference costs and performance paradigms.
MULTIMODALITY'S EXPANSION BEYOND TEXT-TO-IMAGE
The "Multimodality War" has expanded significantly beyond text-to-image generation. While companies like Midjourney continue to thrive with impressive revenue, the frontier is advancing into 3D, video, and sophisticated voice synthesis. These developments are creating new markets and use cases, challenging traditional notions of art and digital content creation, and demonstrating AI's increasing versatility across various sensory inputs and outputs.
THE STRUGGLE FOR NEW ARCHITECTURES: STATE SPACE MODELS
Emerging architectures such as State Space Models (SSMs) like Mamba are challenging the dominance of Transformers. Initially framed as solutions for extremely long context windows, their primary appeal is now shifting towards computational efficiency and improved performance for a given amount of compute. This efficiency gain positions them as a serious contender, potentially altering the hardware and software requirements for AI models.
RAG OPS AND THE EVOLVING DATABASE LANDSCAPE
The "RAG Ops" landscape, initially a major focus, continues to evolve as foundational models advance. The battle lies not just in storing vector data but in making it useful through sophisticated pipelines and operations. While traditional databases are integrating vector capabilities, dedicated vector databases are vying for market leadership, attempting to define the next generation of data storage and retrieval for AI applications.
THE SEMANTIC SHIFT IN CODING AND AGENT DEVELOPMENT
The integration of AI into coding is moving towards a semantic understanding, enabling non-technical users to intervene in code generation through natural language. This ""inner loop"" versus ""outer loop"" paradigm is crucial for agent development, with the goal of abstracting away low-level coding complexities. While fully autonomous agents remain a distant vision, incremental progress in IDE-integrated tools shows promise for transforming software development.
THE PROVOCATIVE RISE OF AI HARDWARE AND PERSONAL ASSISTANTS
The emergence of new AI hardware, such as the Rabbit R1 and Humane AI Pin, signals a move towards more integrated and context-aware AI assistants. These devices, often prioritizing convenience over privacy, aim to capture unique user context, which is becoming a key differentiator in the AI application landscape. While hardware ventures face high failure rates, they represent a provocative frontier in making AI practical and accessible.
GOOGLE'S GEMINI AS A CREDIBLE ALTERNATIVE TO OPENAI
The release of Google's Gemini models marks a significant development, providing a credible multimodal alternative to OpenAI's offerings. This competition is vital for a healthy AI ecosystem, preventing a single entity from dominating the market. As LLaMA 3 also enters training, the landscape is setting up for continued innovation and competition among major players, driving progress across various AI modalities.
Mentioned in This Episode
●Products
●Software & Apps
●Companies
●Organizations
●People Referenced
Inference Provider Pricing vs. Break-Even Point (Estimated)
Data extracted from this episode
| Provider | Price per Million Tokens | Estimated Break-Even | Profit/Loss |
|---|---|---|---|
| Perplexity | $0.56 | $0.50 - $0.75 | Likely Profit |
| AnyScale | $0.50 | $0.50 - $0.75 | Possible Loss |
| Octo AI | $0.50 | $0.50 - $0.75 | Possible Loss |
| Abacus AI | $0.30 | $0.50 - $0.75 | Loss |
| Deepinfra | $0.27 | $0.50 - $0.75 | Loss |
Common Questions
The major conflicts discussed are the Data War (content creators vs. AI developers), the GPU Rich vs. Poor war (model trainers vs. alternative methods), the Multimodality War (specialized models vs. all-encompassing models), and the RAG Ops/Tooling war (databases, frameworks, and operational tooling).
Topics
Mentioned in this video
Highlighted a DeepMind paper on bootstrapping verifiable synthetic data at NeurIPS.
From Pide, discussed Replit's role in code models.
From Hex, discussed in the context of semantic layer and data engineering.
CTO of MongoDB, formerly GM of AWS RDS, discussing the rise of unstructured data.
His leadership battle inspired the mock Wikipedia entry format for tracking AI wars.
Mentioned as having a previous episode on GPU Rich vs. Poor.
Left to start BrightWave and was previously a guest on the podcast.
Known for prompt engineering techniques, mentioned in the context of state-of-the-art prompting.
Associated with Twitter and the 'side of chaos'.
Brought up the analogy of low background steel to explain low background tokens.
Promising an approach to synthetic data generation: 'pre-trained scale synthetic data'.
Invited guest for discussions on the podcast.
Mentioned as a company in the RAG (Retrieval-Augmented Generation) space.
A company discussed in the context of semantic layer and data engineering.
A code model mentioned in the discussion about code models.
A company mentioned in the context of semantic layer and data engineering.
An example of an 'inner loop' agent, offering limited self-driving capabilities.
Mentioned as an early winner but not following up significantly on its code models.
A popular IDE for developers, contributing to tooling fragmentation in coding.
A vector database company, mentioned in relation to data storage and operations.
A NoSQL database company now led by Mark Porter, who believes unstructured data is rising.
A model that people are focusing on fine-tuning.
A company in the RAG (Retrieval-Augmented Generation) space.
Mentioned for providing analysis of the New York Times lawsuit.
A code model mentioned in the discussion about code models.
A serverless vector database that smart people are adopting.
Mentioned in the context of the recurring 'year of AI in production' prediction.
Currently in training, expected to be a contender in the AI model space.
Amazon Web Services Relational Database Service, formerly managed by Mark Porter.
A powerful model from OpenAI, enabling new use cases like computer vision integration.
Mentioned as a database that can handle vector embeddings, challenging dedicated vector databases.
Mentioned as a tool that allows writing code in English.
A framework company that does not own the cloud, and struggles monetarily.
A company working on outer-loop coding agents.
A credible alternative to OpenAI's models, seen as a leading contender.
A platform used by developers, contributing to tooling fragmentation in coding.
A company discussed in the context of semantic layer and data engineering.
Used by Anthropic and OpenAI for their internal RAG solutions, passing internal evaluations.
An example of a company that introduced social discomfort but ultimately succeeded based on convenience.
Used as an example of a company that was provocative and faced regulatory challenges, similar to new AI hardware.
Scraped transcribed lyrics from Rap Genius and is a major player in AI development.
A cloud platform company mentioned in the context of the Jamstack era.
Mentioned in the context of releasing multimodality content.
A lyric annotation website that faced similar copyright issues with music labels and Google.
An inference platform involved in benchmarking drama and accused of releasing biased benchmarks.
Shut down its API to train its own models, contributing to the data lockdown.
A leading vector database company with a significant valuation.
A company that evolved from a CDN (Vercel) to a framework provider.
A cloud platform for AI, involved in benchmarking drama with AnyScale.
A major AI company, mentioned regarding context window limitations and prompting techniques.
Published research on 'flow engineering' as an evolution of prompt engineering.
The newsletter platform used by the podcast; mentioned as having technical issues.
A company developing a new 3D model, to be featured on the podcast.
A major player in the AI space, facing lawsuits and impacting the GPU inference market.
Shut down its API to train its own model, contributing to the data lockdown.
Authored a paper on bootstrapping verifiable synthetic data, highlighted by Andrej Karpathy.
Mentioned as an example of an outer-loop coding agent.
Demonstrated issues with context window handling, requiring prompt engineering workarounds.
The podcast's host company or initiative.
A stealth company that raised $50 million, spending most on GPUs.
Mentioned in the context of shutting down APIs for training models.
A company started by Mike Conover.
Mentioned for its work in building instruction-tuned datasets like 'Dolly 5K'.
Source of Suth, who commented on the AnyScale benchmarking drama.
Launched a new AI hardware device, alongside Tab, representing a new form factor.
A new AI hardware device launched at CES with nostalgic appeal.
Used as a comparison for the nostalgic design of the Rabbit R1.
An AI hardware device associated with the speaker's investment, focusing on context and processing.
A new hardware device expected to drive experimentation in AI and spatial computing this year.
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