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Andrew Ng: Building Faster with AI (AI column)

#AI #ML #Software #Engineering #Agents #Architecture

I saw it. play Andrew Ng at AI Startup School by Y Comibnator Andrew is co-founder and principal partner AI Fund, co-founder and chairman CourseraHe is the founder of DeepLearning.AI and an associate professor at Stanford University. It is interesting that AI Fund is a venture studio created in 2018 It works as a “co-founder” of startups, helping entrepreneurs build AI companies from scratch.

1. Speed as the main factor of success The ability of a management team to quickly complete tasks is a strong predictor of startup success. Interestingly, large companies are not so dependent on speed, but they can act with great leverage. n (For example, through distribution to a current audience.) 2. Artificial intelligence stack Andrew introduced the structure of the AI stack, consisting of five levels

  • Semiconductor companies (baseline) Hyperscalable cloud services Developers of basic models
  • Agent orchestration level (layer)
  • Application level (where there are the greatest opportunities) At the application level (apps layer) It should be the most valuable, as applications must generate enough revenue to support all the lower levels. 3. The Agent AI Revolution Agent workflows are an iterative approach to problem solving in which an AI system can plan, research, review and improve its performance. Instead of linear content creation, agent systems can:
  • Make a plan.
  • Conduct research
  • Write drafts.
  • Critically evaluate results Review and improve work 4. Concrete ideas against vague ones Endrb emphasizes the importance of specific ideas – ideas so detailed that an engineer can implement them immediately. For example: A vague idea: “Use AI to optimize healthcare assets” Specific idea: “Create software for online booking of MRI slots in hospitals” Concrete ideas provide speed as the team can implement them quickly and get feedback. It’s funny that corporations often talk about vague ideas – they can be easily thrown in and everyone agrees with them, but when it comes time to implement them, then pss. 5. Transforming Development with AI Assistants Here it was interesting to hear Andrew's assessments of how AI assistants affect development. Production code: improvement on 30-50% Rapid prototypes: acceleration in 10plus Tools evolve from code completion (GitHub Copilot) high-agent (Claude Code, new versions with O3). 6. Shifting development bottlenecks With the acceleration of software development, product management becomes the main bottleneck. Andrew notes the change in traditional ratios: instead of 1 product manager 4-7 Engineers, some teams offer 1 product manager 0,5 engineer. It seems that this is typical for startups from AI Fund. 7. The importance of open models Andrew advocates for the protection of open source in the field of AI, warning of attempts by some companies to create regulations that will establish them as gatekeepers of large language models.

These are some of the tips from Andrew’s speech. Focus on specific ideas – avoid vague concepts Learn programming – even non-technical roles benefit from understanding code Get quick feedback – from personal intuition to testing with strangers Deepen your understanding of AI – technical solutions can save months of work Use different tactics to get feedback from users

It is worth noting that Andrew criticizes the hype around AI, including Threats to human existence (The safety of AI does not depend on technology, but on its responsible application.) Massive job losses The need for only nuclear energy for AI

So this is a very good talk with interesting thoughts.

#AI #ML #Software #Engineering #Agents #Architecture