Foundational Model AI Startups Are Not For The Faint Hearted

Freedom Preetham
The Simulacrum
Published in
4 min readMay 18, 2024

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Starting a foundational AI startup is a vastly different endeavor compared to leveraging platforms like OpenAI or Anthropic with their pre-trained models. The financial demands are significant, particularly when training specialized, ground up models in the “generative genre” which are vastly different from classical ML jobs.

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For instance, consider the real world example from Cognit. Training a small genomic model — just 0.6 billion parameters — on a 500GB data corpus. The cost? A staggering $30,000 per TPU week. This expense is for a single training job.

Such a model is constrained by genomic sequence length, data corpus size, and ultimately, precision and accuracy. Any minor error in data, bias, code, algorithm, or training procedures can necessitate a costly retraining process. In a startup environment, these small mistakes can be financially devastating.

So, how do we reduce these costly errors? The answer lies in developing models that predict training errors. Yes, you read that right. We build predictive models to forecast the outcome of a training job before it’s run. This involves extensive mathematical analysis on potential data drift, bias-variance tradeoffs, precision, mean correlation, and other metrics based on changes in data, algorithms, parameter sizes, and hyperparameters. This process, known as simulation and sensitivity analysis, allows us to estimate risks and optimize experimental runs.

These predictive models are bespoke to the domain, data, model and constraints. Honestly, the amount of AI and Math research that goes into these predictive models itself is time consuming and vast. Hint: they are based on complex stratified sampling and TRPO (Trust Region Policy Optimization) learning mechanisms.

It’s important to understand that small foundational models have limited utility and deployability, leading to delayed revenues. This dynamic is critical to grasp before diving into the creation of a foundational AI startup.

When these models scale successfully, the revenue they generate can far outweigh the training costs, as inference costs are significantly lower than training costs.

If you are a startup, explaining this to investors and venture capitalists (VCs) can be a challenge, especially if they lack advisors with experience in building foundational models. Many VCs are trained to think in terms of software development paradigms, like building a Minimum Viable Product (MVP), which involves coding features incrementally. Foundational models, however, are trained as a whole, not coded feature by feature. They are even vastly different from classical or traditional ML training jobs which are not as complex or intricate.

The path to developing foundational AI models is full of financial and technical challenges. Understanding these complexities and preparing for the high costs of training and the necessity of predictive analysis to minimize errors is crucial.

Open discussion and education about these intricacies are vital for securing the right support and investment. Let’s push the boundaries of what’s possible in AI, armed with the knowledge and tools to navigate this challenging yet exhilarating landscape.

Why is Building Generative Models Expensive?

Training generative AI models presents substantial financial and technical challenges, distinctly surpassing those of classical ML tasks. Key differentiators include:

  1. Architectural Sophistication: Generative models, often leveraging architectures like transformers with billions of parameters, demand intricate design and optimization, necessitating significant computational power.
  2. Massive Data Requirements: These models require extensive, diverse datasets to capture complex patterns, far exceeding the data needs of traditional ML, thereby increasing preprocessing and storage costs.
  3. Computational Intensity: Training involves specialized hardware (e.g., TPUs, high-performance GPUs) over extended periods (weeks to months), driving up costs.
  4. Precision and Error Sensitivity: High precision is critical; minor errors in data or algorithms necessitate costly retraining cycles, emphasizing the need for meticulous error management.
  5. Algorithmic Complexity: Cutting-edge algorithms with advanced features (e.g., attention mechanisms, adversarial training) require continuous integration of the latest research innovations, escalating development costs.
  6. Inference Load: Generative models perform complex data synthesis during inference, requiring substantial computational resources, impacting deployment efficiency.
  7. Scalability and Optimization: Efficiently scaling and deploying these models demands significant engineering to manage latency, throughput, and resource utilization, adding to the complexity and cost.
  8. Risk Management: Advanced simulation and sensitivity analyses are crucial to predict training outcomes, optimize runs, and mitigate risks, reducing the likelihood of expensive retraining.

These factors underscore the profound complexity and high costs associated with generative AI model development, necessitating robust financial and technical strategies far beyond those of classical machine learning projects.

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