Generative AI and The Future of Work

ChatGPT help me solve a refactoring bug today. I had spent hours messing around trying to figure out what the issue was until I realized, via asking ChatGPT, that I had misunderstood a piece of the code and the docs. It was able to answer and provide examples (until it had error and crashed) in a way a senior engineer might have been able to.<p>The funny thing is I had tried just pasting in code and saying &quot;find the bug&quot; and it wasn&#x27;t helpful at all, but when I posted in a portion and asked it to explain what the code was doing I was able to work backwards and solve the issue.<p>Its nice anecdote where the AI felt additive instead of existentially destructive which has been a overbearing anxiety for me this last month.

GPT3 has shown how ML can be trained on multiple unstructured data sources to produce structured information on demand.<p>Iterate a few more versions from here, so that the models are stronger at producing the correct structured data, and the impact on every office job will be profound.<p>I.e. instead of training a generative model on text from the internet, train it on every single excel file, sql database, word document and email your company stores. Then query this model asking it to generate Report X showing Y and Z.<p>When you step back and consider it, 99% of office jobs are about producing structured data from unstructured data sources. The implications of this are being hugely underestimated.

&gt; Widespread adoption of generative AI will act as a lubricant between systems,<p>I largely agree with this article, but I feel like you have to be careful with these general predictions. Many technologies have purported themselves to be this &quot;business lubricant&quot; tech (ever since the spreadsheet), but the actual number of novel spreadsheet applications remains small. It feels like the same can be said for generative AI, too. Almost every day I feel the need to explain that &quot;generation&quot; and &quot;abstract thought&quot; are distinct concepts, because conflating the two leads to <i>so much</i> misconception around AI. Stable Diffusion has no concept of artistic significance, just art. Similarly, ChatGPT can only predict what happens next, which doesn&#x27;t bestow it heuristic thought. Our collective awe-struck-ness has left us vulnerable to the fact that AI generation is, generally speaking, hollow and indirect.<p>AI will certainly change the future, and along with it the future of work, but we&#x27;ve all heard idyllic interpretations of benign tech before. Framing the topic around content rather than capability is a good start, but you easily get lost in the weeds again when you start claiming it will change <i>everything</i>.

I don&#x27;t have a problem with the main point of the article, but there is a huge terminology confusion that is rapidly gathering force to confuse people. The key breakthroughs of GPT3 et al are not primarily about generative AI. People had been building generative models long before GPT3, and it was generally found that discriminative models had better performance.<p>They key to the power of GPT3 is that it has billions of parameters, AND those parameters are well-justified because it was trained on billions of documents. So the term should be something like &quot;gigaparam AI&quot; or something like that. Maybe GIGAI as a parallel to GOFAI. If you could somehow build a gigaparam discrimative model, you would get better performance on the task it was trained on than GPT3.

ChatGPT and generative AI will not who write these things for a living will definitely have fewer clients. But this is a small percentage of paid writing, and not the most lucrative or desirable.<p>I do not think that the world is changing because of large language models. That seems to be a controversial opinion so I won&#x27;t get into it here. But these are powerful new tools, no question. The way I work has changed and I&#x27;m very glad to have ChatGPT.<p>I do believe that in the coming years knowing how to use ChatGPT or similar products will be as important as knowing how to use Google is now. People that know how to leverage LLMs going forward will simply have an advantage over those who don&#x27;t. It won&#x27;t be long before it isn&#x27;t optional for executives and knowledge workers. This <i>will</i> be a big change for many people. But we adapted to Google in the early 2000s and people will adapt to this as well.

So this article doesn&#x27;t go into &#x27;Unexpected ways generative AI will change how you work forever&#x27; instead is just an advert.

&gt; The results are often wildly creative and spookily accurate, giving these models a human-like feel.<p>or wildly inaccurate, particularly in fields such as programming

Does anyone else feel like the crypto crowd just migrated to AI?

If social media resulted in a deluge of low quality crap, now you can expect that same phenomenon to the power of infinity.

I used ChatGPT for my work as a writer, and it&#x27;s pretty nice.<p>I wouldn&#x27;t let it write a whole article, but it can really save time at research. Just needs a bit of fact checking in the end.

I paid for Tabnine pro since it was 50% off for a year but I won&#x27;t renew it unless it massively improves.<p>I mean, it does give good completions sometimes but the time saved isn&#x27;t that great imho. Maybe chatgpt is better but it feels like AI still have some way to go to actually be so useful you would be less sucessful without it.

Their product MaestroAI is marketed as “for teams” (and of course with the obligatory fading-color call-to-action buttons) presumably to attract VC $$$ but I would love something like this (powered by LLMs) to extract info from all my documents.<p>Maybe something like this exists? Please no DEVONThink suggestions :)

The article is purely &quot;common sense&quot; for the purpose of marketing new service ? Nothing new at all.

Unexpected way: I can’t get my work done because ChatGPT is overloaded

Report generator, yes please. Have it hook into Salesforce and Moodle, and I&#x27;ll be set.

On the topic of Content is King, I have a different view than the author. I think in the case of these trained AIs, &#x27;content&#x27; refers to the training datasets and not the generated outputs.<p>Trained AIs are in something like the early digital streaming days where there was only one provider in town, so that provider aggregated All The Content. Over the next decade we would see the content owners claw their content back from Netflix, and onto competitor platforms -- which takes us to where we are today. Netflix&#x27;s third party content has dwindled and forced them to focus on creating their own first party content which can not be clawed away.<p>When these generative AIs start to produce income, it will be at the expense of the artists whose art was in the training dataset nonconsensually. This triggers the same content clawback we saw in digital streaming. Training datasets will be heavily scrutinized and monetized because the algorithms powering generative AIs aren&#x27;t actually carrying much water. What is DALL-E without its dataset? Content is King.