Findings
We provide high-level bottlenecks in AI using primary and secondary sources.
1. Data Bottlenecks:
Data availability, quality, and accessibility are consistently identified as significant bottlenecks for the progress of AI, particularly for training large language models (LLMs).
Limited Supply of Public Data: Leading AI researchers, like Dario Amodei of Anthropic, express concerns that the scaling of AI systems could stagnate due to insufficient data. Projections suggest that data could become a significant bottleneck for training LLMs this decade, especially if they continue to be overtrained. The finite supply of public human text data underscores this importance.
Data Acquisition and Management: Acquiring, cleansing, preparing, and storing the vast amounts of data, especially multimodal and vertical-specific data, poses a substantial challenge. Startups, in particular, face immature data operations.
Data Licensing and Access: Access to relevant data is further complicated by licensing restrictions and the existence of datasets in different languages.
Need for High-Quality Labeled Data: Improving the performance of both specialized and generalist models relies on the collection and management of high-quality human-labeled data, requiring robust platforms for labeling, quality management, and storage. However, obtaining millions of screens to annotate can be difficult, necessitating shortcuts like using models to provide data.
Immature Version Control for Datasets: The ecosystem for managing changes and retrieving datasets is less developed than for code, and the sheer size of datasets adds complexity to version control solutions.
Bias in Training Data: AI models learn from associations in their training data, which can lead to the replication of existing biases related to gender, race, and disability. This can result in reductive and stereotypical representations, raising ethical concerns.
2. Compute Bottlenecks:
The immense computational resources required for training and serving large AI models present a significant bottleneck.
Cost and Availability of Hardware: Access to necessary hardware, such as TPUs and GPUs, can be limited even within large organizations due to bureaucratic processes. The high cost of compute resources is a persistent challenge.
Infrastructure Limitations: Designing and operating the infrastructure needed to support models of unprecedented size and complexity is challenging. Sub-bottlenecks include energy consumption, cooling requirements, and geolocation of data centers.
Inference Costs: High inference costs remain a barrier to a seamless user experience for consumer applications of AI agents.
3. Evaluation Bottlenecks:
The lack of consistent and effective methods for evaluating AI models hinders the transition from research to real-world applications.
Lack of Standardized Benchmarks: The AI field lacks open-source research benchmarks that align with enterprise metrics and end-user needs. Current academic benchmarks like MMLU may not reflect real-world use cases.
Inefficiency of Manual Evaluations: Researchers often manually evaluate model responses and datasets, leading to significant delays and inaccuracies. Only a small percentage of AI evaluations currently meet the necessary standards.
Defining Quality and Desired Behavior: Establishing clear definitions of "good" AI and understanding the limitations of generative AI requires a high technical bar for leadership. Determining the desired behavior for models during post-training is also challenging.
Subjectivity and Lack of Reliability: Evaluating different model options can be subjective and lack a reliable methodology, with companies often relying on internal engineers to "play around" with models.
4. Talent and Skills Bottlenecks:
A shortage of skilled AI professionals poses a significant impediment to AI innovation and adoption.
Talent War: There is intense competition for top AI researchers and engineers.
Skills and Knowledge Gaps: Organizations struggle with a lack of employees possessing the necessary AI skills and knowledge to develop, deploy, and manage AI solutions. Upskilling existing employees for generative AI is proving to be extremely difficult.
Expertise for Fine-Tuning: Even after choosing a base model, companies often lack the in-house expertise required to fine-tune it effectively for their specific needs.
5. Deployment and User Experience Bottlenecks:
Successfully deploying AI solutions and ensuring user adoption faces several hurdles.
Complexity of Integration: Weaving together AI models with existing databases, guardrails, and workflows in enterprise settings is often bespoke and challenging.
Bureaucratic and Regulatory Hurdles: Heavily regulated industries like healthcare and finance face endless layers of approvals and legal hurdles that can stifle innovation.
Lack of User Trust and Understanding: Building trust in AI systems and addressing user confusion about AI technologies remain crucial for adoption. The current reliance on text interfaces for AI agents may also limit broader adoption as many prefer more natural and interesting interactions.
Organizational Alignment and Priorities: Overcoming organizational silos and aligning teams around AI initiatives are significant barriers to innovation. Securing organizational buy-in for new AI investments can also be challenging.
6. Model-Specific Limitations:
Current AI models still exhibit several limitations that restrict their applicability in critical domains.
Hallucinations: LLMs can generate incorrect or nonsensical information, which is unacceptable for many enterprise applications requiring high accuracy.
Bias and Fairness: Models can perpetuate and even amplify existing societal biases, leading to unfair or discriminatory outcomes. Ensuring fairness in AI systems remains a significant challenge.
Reasoning Abilities: While progress is being made, AI agents still face limitations in their reasoning abilities, making delegation of complex tasks difficult.
7. AI Supply Chain Security Risks:
As AI becomes more integrated into software applications, securing the AI software supply chain is increasingly important. Risks include unexpected or malicious training data and vulnerabilities in the training frameworks. The immature state of version control for datasets also poses a security risk.
In summary, the key bottlenecks for AI are multifaceted, spanning data, compute, evaluation, talent, deployment, model limitations, and supply chain security. Addressing these bottlenecks will be crucial for unlocking the full potential of AI and enabling its effective application for strategic goals.
We provide deep dives into some of these bottlenecks to better understand the factors shaping the AI landscape.