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AI Patterns

๐ŸŒˆ Abstract

The article discusses the current state of Artificial Intelligence (AI) technology and its impact on various industries. It highlights the growing focus on AI models and their interfaces, the emergence of different model sizes, and the resurgence of on-premises hardware due to the rise of AI technologies.

๐Ÿ™‹ Q&A

[01] Decades of Technology Trends

1. What are the key trends that have influenced the technology industry's approach to product design and user experience?

  • The article mentions that in the past, technology vendors typically designed products for buyers and treated users as an afterthought. This has changed due to trends like cloud and open source, which have led to a greater appreciation for user experience and developer experience.
  • However, the article notes that comparatively little attention has been paid to the interface question when it comes to AI technologies.

2. How has the industry's focus on AI models impacted the user experience and adoption of these technologies?

  • The article suggests that while enterprises and vendors focus on discussing AI models, users are actively imprinting on their specific tool of choice, making it difficult for them to switch to other tools.
  • This "Baby Duck syndrome" with their tool of choice can make it challenging for enterprises to get developers to switch to different AI tools, similar to the difficulty of getting them to switch IDEs.

[02] The Variety of AI Models

1. What is the current state of the AI model landscape?

  • The article notes that there is a proliferation of AI models, with Hugging Face listing over 683,310 available models as of the time of writing.
  • However, the article suggests that the industry's focus has been primarily on large, state-of-the-art models from companies like Google, Microsoft, and OpenAI.

2. How are vendors and users approaching the different sizes of AI models?

  • The article observes that while large models have significant capabilities, they can also be expensive to run at scale. As a result, many users are turning to medium or small-sized models for cost-saving, local deployment, or other reasons.
  • The article suggests that it will be interesting to see if small and medium-sized models start to receive more attention and airtime compared to their larger counterparts.

[03] The Resurgence of On-Premises Hardware

1. What factors have contributed to the renewed interest in on-premises hardware for AI workloads?

  • The article notes that the rise of AI technologies, particularly the release of ChatGPT, has led enterprises to consider the risks of granting these models access to their private, internal data.
  • This has resulted in an observable resurgence in interest in on-premises hardware, as enterprises seek to leverage AI capabilities while maintaining control over their data.

2. How do the concepts of data gravity and trust play a role in the expansion of on-premises AI implementations?

  • The article explains that large datasets, which are the fuel for AI, are difficult to move quickly and safely. This "data gravity" concept implies that data is likely to remain where it is currently stored, which is often on-premises.
  • The article also suggests that the location of large datasets is an implicit assertion of trust, which can give incumbent data management providers an advantage over new market entrants when it comes to AI adoption.

[04] Factors Influencing AI Adoption

1. What are the key considerations that enterprises face when adopting AI technologies?

  • The article suggests that AI adoption involves multiple factors, including capabilities, size, speed, cost, and trust. Enterprises must weigh these different aspects when making decisions about which AI tools and models to use.
  • The article notes that trust is an important factor, and can favor incumbent providers over new market entrants, at least in the short term.
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