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WLAIM

Will the AI bubble burst in 2025?


AI industry needs to earn $600 billion per year to pay for massive hardware spend — fears of an AI bubble intensify in wake of Sequoia report.

Main topic: AI Investment Bubble / Infrastructure Costs / Revenue Gap


In a nutshell: The AI industry faces a potential bubble as current infrastructure investments require approximately $600 billion in annual revenue to be sustainable, yet actual revenue generation falls significantly short of this target.


According to Sequoia Capital partner David Cahn's analysis, there's a concerning gap between AI infrastructure investments and revenue generation. 


While companies are spending massively on AI hardware (with Nvidia alone earning $47.5 billion in datacenter revenue last year), the industry needs to generate about $600 billion annually to justify these investments. This figure is derived from doubling Nvidia's revenue to account for total datacenter costs, then doubling again to ensure 50% gross margins for end-users. Even with optimistic…


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Aleksandra
Aleksandra
Dec 17, 2024

So that means that AI companies will need to charge more for their services (kind of like the streaming services all had to increase their prices, throw in advertising for the lowest subscription tiers, etc.). And this means that access to the full capabilities of AI will be available that those who can pay for it. And those who can't will be left behind in the dust in a world where AI is going to already be impacting the labor market severely. Including the market for marketers. So marketers need to be at the forefront of AI, to understand how it works, so that they can continue to be relevant and offer value to companies in an increasingly competitive market for marketers.

WLAIM

The promise and perils of synthetic data

Here is one of the most relevant read I had this past year, about synthetic data. Check my summary here. And find the full post at the end of this post.


Main topic: Synthetic Data / AI Training Data / Data Quality Challenges


In this previous post, I came across what is synthetic data. 


In a Nutshell: The article explores the growing trend of using synthetic data (AI-generated data) for training AI models, examining both its potential benefits and significant risks, particularly as traditional data sources become more restricted.


The landscape of AI training is undergoing a significant transformation as major tech companies like Anthropic, Meta, and OpenAI increasingly turn to synthetic data for model development. 


This shift is driven by several pressing challenges in the traditional data ecosystem: 


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Aleksandra
Aleksandra
Dec 17, 2024

"- Consider hybrid approaches using both synthetic and real data." This one really speaks to me. As a simple example, I think back to a research project I did back in very early 2023, and I used ChatGPT to come up with information and data on a topic I knew nothing about (this was before tools like Perplexity started providing references behind the answers they provided) I didn't know what I didn't know, so it was hard to tell if what I was reading was correct or not (I had to assume it was). Only as I progressed in my research, looked at online information, spoke to humans, did I then realize that certain nuggets in the initial research were not correct (and also updated my notes and report with the correct information). AI was able to help reduce massively the collection of information and market analysis (reducing the time from something that would take weeks and months of reading and discussions to just a few days/weeks). The challenge is not knowing what one doesn't know. In order to be able to evaluate the quality of synthetic data and to determine how to fit it with real data, one has to be really careful and knowledgeable (or surround oneself with knowledgeable people). And that can be challenging when it is something technical or deeply nested (such as IT data, code, AI models themselves, etc.) that is hard to grasp, even for the most knowledgeable folks, due to inherent complexity in the data.

WLAIM

What is synthetic data from a content marketer's standpoint?

Synthetic data is a term marketers will better get used to in 2025. Here is why.


What is synthetic data?


Let me try to explain synthetic data in a way that's easy to understand.


Imagine you're playing with LEGO blocks. You can build all sorts of things with them, right? Well, synthetic data is kind of like building something new with LEGO blocks, but with information instead!


Let's say you want to practice being a weather reporter, but you don't have real weather information. You could make up (or "synthesize") pretend weather data that looks and acts just like real weather data. You might say "Monday was sunny and 75 degrees, Tuesday was rainy and 68 degrees" and so on.


Here's another example: Think about those video games where you can create your own character. The game lets you make up a person who isn't real but looks and acts…


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Aleksandra
Aleksandra
Dec 17, 2024

Indeed! We could reestablish connection all the while avoiding AI producing crappy content and then learning from that crappy content to produce more crappy content in an endless vicious cycle until collapse takes place.

WLAIM

AI Agents: the current (not next) BIG thing in AI Marketing for 2025...

The Agents are coming openai confirms ai will work without humans in 2025


Main topic: AI Agents / OpenAI Development / Autonomous AI Systems


In a Nutshell:  OpenAI has announced plans to launch AI agents in 2025, representing a significant milestone in AI development where autonomous systems will be capable of performing complex tasks independently without human intervention.


The article details OpenAI's announcement about the upcoming launch of AI agents, including:


- Planned release in 2025 as confirmed by CEO Sam Altman

- Demonstration of an AI voice assistant ordering strawberries independently


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Aleksandra
Aleksandra
Dec 17, 2024

And that is another example of the huge value provided by this community!

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