😺 AI expectations vs. reality

PLUS: 6 actual AI use cases!

Welcome, humans.

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Here’s what you need to know about AI today:

  • Some employees ask if AI is a “solution in search of a problem”.

  • Our 5 essential tips for finding use cases in AI.

  • We share a great explainer video on how AI actually works!

  • China is falling behind the US in a big way when it comes to AI.

Your company is probably doing AI wrong.

Many executives have pie-in-the-sky expectations that AI will soon deliver the kind of insane productivity gains and cost savings they dreamed about at Wharton.

yo Nvidia, stop setting unrealistic expectations for the rest of us!

But when it comes to employees, some harbor doubts, wondering whether AI is just a “solution looking for a problem.”

In other words, CEOs are saying, “We should be using AI!”, and employees are like, “Use AI for what? And how?”

It’s worth mentioning that yes: AI in 2024 is capable of unlocking substantial productivity gains (think 20-40%) across various use cases—customer support, for instance. We’ve seen it for our business and many others.

But in its current state, AI won’t replace thousands of employees or 10x your profit overnight.

Rather than jumping on the AI bandwagon cause it's a hot trend, organizations should take a more level-headed approach.

Here’s our two cents (forward this to your boss!):

  1. Start by clearly identifying the business problems / inefficiencies you want to solve. 

  2. Isolate small parts that you can solve with easy-to-setup tools like ChatGPT.

  3. Even better, consider whether you even need AI. Sometimes you just need a project tracker, not fancy AI!

  4. Start with small pilot projects to prove value before making big bets.

  5. Provide training and education for employees on how to use AI effectively, starting with our Intro to ChatGPT course!

And how do you know if AI can tackle a particular problem?! Look no further, my young padawans…👇

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So what are the AI use cases?!

reddit

We had Claude summarize this Reddit thread to find out what knowledge workers are actually finding AI useful for. Here’s what they said:

  • "danielt1263": writing promotional and sales copy.

  • "originalchronoguy": using a chatbot to quickly find info in a vast repository of appliance repair documents.

  • "DockEllis17": processing unpredictable, semi-structured data like complicated PDFs / invoices into neat formats.

  • "Post-mo": processing thousands of scanned docs and faxes, categorizing them into a handful of usual types.

  • "hfourm": using GitHub Copilot for whipping up code.

  • "master_mansplainer": using customer support bots to take on basic, repetitive queries, allowing human reps to focus on the trickier stuff.

Around the Horn.

  • China is falling way behind the US in AI development cause of its dependence on US tech for building its models.

  • r/ChatGPTPro: a discussion on which tools are worth paying for in 2024 (ChatGPT Pro, Phind Pro, Claude Pro, Perplexity Pro, etc).

  • India said it won’t require companies to seek approval before rolling out AI models anymore.

  • Rather than build their own AI, Apple might just put Google Gemini in your iPhone instead.

  • xAI’s Grok is now open source on GitHub.

Treat To Try.

AI is cool, but every wondered how the heck it actually works on the inside?

This video does a pretty good job explaining LLMs for beginners!

click to watch

Monday Meme.

A Cat's Commentary.

That’s all for today, for more AI treats, check out our website.

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See you cool cats on Twitter: @nonmayorpete & @noahedelman02

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