Sun Jun 23 2024

Foundation Models and Model Commoditization:

• OpenAI recognized early on that the future of AI post-Transformer involved tackling major scientific challenges rather than focusing solely on research papers.
• The performance of foundation models is not experiencing diminishing returns, with every top-tier cloud provider striving to dominate this sector. Every doubling of GPUs leads to predictable improvements in model intelligence.
• It is anticipated that in the long term, there will only be around 5-7 key foundation model providers due to the high costs and intense competition in the field. Companies are advised to concentrate on assembling large teams of scientists dedicated to solving specific real-world problems instead of engaging in purely curiosity-driven research.

Model Performance Improvement and Memory Challenges:

• Enhancing model performance necessitates breakthroughs in reasoning, which involves synthesizing existing thoughts to uncover new insights.
• While short-term working memory has seen significant advancements, long-term memory remains a challenge within AI systems. Application developers should take charge of managing long-term memory related to user preferences as an integral part of comprehensive software systems utilizing AI models.

Vertical Integration Between Model Providers and Chip Makers:

• Securing control over both the model layer and chip layer is critical for companies aiming for success in the AI industry. NVIDIA's dominance may face threats from internal efforts by other firms if they fail to have authority over their own chips.
• Apple's possession of consumer devices gives them an edge in running intelligent models offline without depending on external providers. This advantage positions them well for tasks not requiring extensive reasoning capabilities.

Ownership of Vertical Stack and Competition Among Providers:

• Apple's capability to operate smart models at the edge provides them with an advantage, particularly for private tasks not requiring extensive reasoning capabilities.
• The future landscape might witness consolidation around a few primary providers who can efficiently deliver cutting-edge models through vertical integration with chip manufacturers.

Future of Foundational Model Layer:

• Major cloud players are expected to thrive due to their substantial resources, while independent companies selling models to developers may need partnerships or rapid economic growth strategies to remain competitive solo.
• Independent entities must generate significant cash flow or establish robust enterprise go-to-market tactics to maintain competitiveness independently.

Foundation Models and AI Agents:

• Adept is developing an AI agent tailored for knowledge workers, allowing them to assign diverse work tasks.
• The company's strategy involves a vertically integrated stack rather than solely focusing on selling foundation models.
• Vertical integration in the agent sector is deemed critical to owning the entire system from user interface to foundational modeling layer for optimal performance.
• Adept aims to become the go-to platform for enterprise workflows, enabling employees to instruct the AI agent on various tasks with high variability.
• The focus is on building a vertically integrated stack instead of just training foundation models for sale. This approach ensures control over the entire system, from end-user interface design to foundational model layers.

Distinction Between Traditional RPA and New Era of Agents:

• Robotic Process Automation (RPA) excels at repetitive high-volume tasks, while agents require continuous thinking and planning at each step to achieve objectives.
• RPA functions akin to robots following predetermined paths, whereas agents necessitate reasoning and adaptability similar to full self-driving systems.
• The key difference lies in how RPA automates routine tasks based on predefined rules, while agents are designed to think dynamically and plan steps continuously throughout a task.

Business Model Shift Towards Agent Solutions:

• Companies transitioning from traditional RPA services to agent-based solutions face disruptive changes in business models due to implementing agents that observe end-user actions and automate tasks using natural language.
• The shift towards agent solutions disrupts existing business models by moving away from pre-programmed workflows towards systems that learn from observing human actions and can be interacted with using natural language commands.