Скачать или смотреть DeepSeek and Beyond: Navigating AI for Non-Technical People. 2 Tips.

This post is for anyone non-technical feeling a bit overwhelmed by the DeepSeek AI breakthroughs and associated AI and business implications. Here are 2 specific tips…

1: Keep leaning on LinkedIn but join AI Twitter too.

A legendary investor who recently joined Twitter, in describing his initial experience, he said “it’s like reading the news 2-3 days ahead of when it’s actually reported” which sounds about right.

When it comes to AI, “AI Twitter” is absolutely incredible as the quality and quantity of AI knowledge being shared is outrageous.

And to that legendary investor’s point, it’s definitely pulling the future forward but by a lot more than 2-3 days.

Join AI Twitter, start following AI leaders that resonate with you and try to make it a regular part of your day.

Not a big ask but spend 5-10 minutes a few times a day checking in on what’s being discussed, create lists to follow etc.

2: Consider using a few critical AI questions to guide your research.

Getting up to speed on all things AI can definitely feel like drinking from the firehose but after the DeepSeek developments, the intensity has increased to a whole new level.

Sometimes, especially in circumstances like this, it can be useful to narrow the focus by picking a few, especially important questions to guide your research.

The benefit being you’ll likely get up to speed on terms, concepts, trends etc that will be referenced often in other content you more casually consume which will make things less confusing and more clear.

I’m sure there are a whole lot of other questions to consider but I think these 6 provide a useful and super relevant framework to move down the path.

1: What is open-source and why are companies like Meta taking an approach that’s different from others like OpenAI?

2: What are the tradeoffs of each approach?

3: When it comes to Large Language Models, what does “pre-training a model” vs “inference” mean and how much has it cost historically, since the launch of ChatGPT?

4: How does DeepSeek stack up?

5: If the “cost of intelligence” continues to go down, what are the implications of Jevons Paradox on Enterprise Innovation efforts powered by AI?

6: More specifically, what are the implications on the way Enterprises can invest in Compound AI Systems + Intelligent Applications to operationalize more / better business outcomes, faster?

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