Could you explain what types of tasks lie within this “50%”?
And when you talk about “automating 50%,” does this mean something more like “we all get twice as productive because the tasks we accomplish are faster,” or does it mean “the models can do the relevant tasks end-to-end in a human-replacement way, and we simply no longer need attend to these tasks”?
E.g., Cursor cannot yet replace a coder, but it can enhance her productivity. However, a chatbot can entirely replace a frontline customer service representation.
Some of both, more of the former, but I think that is largely an artifact of how we have historically defined tasks. None of us have ever managed an infinite army of untrained interns before, which is how I think of LLM use (over the past two years they’ve roughly gone from high school student interns to grad student interns), so we’ve never refactored tasks into appropriate chunks for that context.
I’ve been leading my company’s team working on figuring out how to best integrate LLMs into our workflow, and frankly, they’re changing so fast with new releases that it’s not worth attempting end-to-end replacement in most tasks right now. At least, not for a small company. 80⁄20 rule applies on steroids, we’re going to have new and better tools and strategies next week/month/quarter anyway. Like, I literally had a training session planned for this morning, woke up to see the Gemini 2.5 announcement, and had to work it in as “Expect additional guidance soon, please provide feedback if you try it out.” We do have a longer term plan for end-to-end automation of specific tasks, as well, where it is worthwhile. I half-joke that Sam Altman tweets a new feature and we have to adapt our plans to it.
Current LLMs can reduce the time required to get up-to-speed on publicly available info in a space by 50-90%. They can act as a very efficient initial thought partner for sanity checking ideas/hypotheses/conclusions, and teacher for overcoming mundane skill issues of various sorts (“How do I format this formula in Excel?”). They reduce the time required to find and contact people you need to actually talk to by much less, maybe 30%, but that will go way down if and when there’s an agent I can trust to read my Outlook history and log into my LinkedIn and Hunter.io and ZoomInfo and Salesforce accounts and draft outreach emails. Tools like NotebookLM make it much more efficient to transfer knowledge across the team. AI notetakers help ensure we catch key points made in passing in meetings and provide a baseline for record keeping. We gradually spend more time on the things AI can’t yet do well, hopefully adding more value and/or completing more projects in the process.
None of us have ever managed an infinite army of untrained interns before
Its probable that AIs will force us to totally reformat workflows to stay competitive. Even as the tech progresses, it’s likely there will remain things that humans are good at and AIs lag. If intelligence can be represented by some sort of n-th dimensional object, AIs are already super-human at some subset of n, but beating humans at all n seems unlikely in the near-to-mid term.
In this case, we need to segment work, and have a good pipeline for tasking humans with the work that they excel at, and automating the rest with AI. Young zoomers and kids will likely be intuitively good at this, since they are growing up with this tech.
This is also great in a p(doom) scenario, because even if there are a few pesky things that humans can still do, there’s a good reason to keep us around to do them!
Could you explain what types of tasks lie within this “50%”?
And when you talk about “automating 50%,” does this mean something more like “we all get twice as productive because the tasks we accomplish are faster,” or does it mean “the models can do the relevant tasks end-to-end in a human-replacement way, and we simply no longer need attend to these tasks”?
E.g., Cursor cannot yet replace a coder, but it can enhance her productivity. However, a chatbot can entirely replace a frontline customer service representation.
Some of both, more of the former, but I think that is largely an artifact of how we have historically defined tasks. None of us have ever managed an infinite army of untrained interns before, which is how I think of LLM use (over the past two years they’ve roughly gone from high school student interns to grad student interns), so we’ve never refactored tasks into appropriate chunks for that context.
I’ve been leading my company’s team working on figuring out how to best integrate LLMs into our workflow, and frankly, they’re changing so fast with new releases that it’s not worth attempting end-to-end replacement in most tasks right now. At least, not for a small company. 80⁄20 rule applies on steroids, we’re going to have new and better tools and strategies next week/month/quarter anyway. Like, I literally had a training session planned for this morning, woke up to see the Gemini 2.5 announcement, and had to work it in as “Expect additional guidance soon, please provide feedback if you try it out.” We do have a longer term plan for end-to-end automation of specific tasks, as well, where it is worthwhile. I half-joke that Sam Altman tweets a new feature and we have to adapt our plans to it.
Current LLMs can reduce the time required to get up-to-speed on publicly available info in a space by 50-90%. They can act as a very efficient initial thought partner for sanity checking ideas/hypotheses/conclusions, and teacher for overcoming mundane skill issues of various sorts (“How do I format this formula in Excel?”). They reduce the time required to find and contact people you need to actually talk to by much less, maybe 30%, but that will go way down if and when there’s an agent I can trust to read my Outlook history and log into my LinkedIn and Hunter.io and ZoomInfo and Salesforce accounts and draft outreach emails. Tools like NotebookLM make it much more efficient to transfer knowledge across the team. AI notetakers help ensure we catch key points made in passing in meetings and provide a baseline for record keeping. We gradually spend more time on the things AI can’t yet do well, hopefully adding more value and/or completing more projects in the process.
I think this is a great point here:
Its probable that AIs will force us to totally reformat workflows to stay competitive. Even as the tech progresses, it’s likely there will remain things that humans are good at and AIs lag. If intelligence can be represented by some sort of n-th dimensional object, AIs are already super-human at some subset of n, but beating humans at all n seems unlikely in the near-to-mid term.
In this case, we need to segment work, and have a good pipeline for tasking humans with the work that they excel at, and automating the rest with AI. Young zoomers and kids will likely be intuitively good at this, since they are growing up with this tech.
This is also great in a p(doom) scenario, because even if there are a few pesky things that humans can still do, there’s a good reason to keep us around to do them!
There’s an important reason to keep some of us around. This is also an important point.