# Planning for an Uncertain Future

Source: https://research.auditless.com/p/al-70-planning-for-an-uncertain-future
Kiwi News: https://news.kiwistand.com/stories/Planning-for-an-Uncertain-Future?index=0x66b8e2397644789671444dc824e02682ccb79c84fffdc329a1c2880fe5fee11006b07c24
Submitted by auditless.eth on 2024-08-11 UTC, 7 upvotes, 5 comments

## Summary

The author argues that rapid improvement in large language models makes long-term career and strategy planning hard, and that the most viable response is to look for scarcity in human attention rather than in software itself. The piece says software has been the main bottleneck in producing value, and that LLMs could remove that bottleneck by both boosting engineer efficiency and replacing purpose-built software with AI front-ends. It notes that software is roughly 10% of US GDP but affects nearly every other sector, and that the emergent capabilities of LLMs make 5-10 year scenario prediction unreliable. The author says they have relied on the scarcity approach since a brief "AI retreat" in 2023.

## Article

I recently saw the following tweet from Bryan Johnson which encapsulates the emotional impact of the rapid improvement of Large Language Models.

While he jumped to the conclusion that we all should prioritize longevity, this wasn’t a satisfying answer for me at least on an individual level.

Some of us may be left wondering what to do with our careers in a world where our understanding of what is prestigious, fast-growing or impactful is changing faster than we can adapt to it.

In crypto, we’re already frequently forced to reassess our focus and whether it’s creating anything meaningful beyond short-term value generation.

But before we understand how to operate in this new paradigm we have to examine its characteristics.

One way to interpret what is happening is to understand that we’ve been living in an era where the rate at which we can produce software is the primary bottleneck.

There are a couple different ways of looking at this:

The most competitive and fast-growing companies are software companies. These also have some of the highest revenue multiples (especially SaaS) due to the attractiveness of the business model.

Sam Corcos, the CEO of Levels even argued that software engineers are so important that start-ups should accommodate for them in every way possible

Many of the most “attractive” careers are built around software: software engineer, designer, product manager, venture capitalist

While software is technically hovering at only 10% of US GDP, it arguably penetrates and affects every other area (yes including services) to an extent that makes categorizing things as software and non-software laughable.

What’s special about LLMs is that they contribute to the growth of software in two ways:

Directly by increasing the efficiency of software engineers

Through replacement of software (e.g., making purpose-built software obsolete and replacing it with an AI front-end)

As such they have the potential of massively progressing our current bottleneck potentially to the point where it’s not a bottleneck anymore.

Furthermore while pre-LLM software could be seen as a tool, LLMs are making the separation between labour and technology much more difficult to draw.

The emergent intelligence in LLM construction makes it all but impossible to predict their capabilities at a 5-10 year timeframe so scenario prediction and strategic response, a common way corporate organizations respond to evolving trends, is not viable here.

Instead, I see people exploring several personal policies:

Look for scarcity. While the reach of software is undeniable, software moats relate more to the world software operates in rather than the code itself. Software is not reliably seen as a store of value itself (especially not now). Instead, the best software companies have dominant network or switching cost effects that relate to human limitations as buyers and perceivers of software value. I believe that human attention (distribution) is a clearly scarce resource and one that may remain scarce throughout the first generations of mainstream LLM adoption. The rise of the DevRel role is not accidental and will continue to accelerate as projects realize that their costs of producing software are going down.

Ignore the change and focus on short-term exploitation. Borrowing from machine learning, In any reinforcement learning model, the high-level relationship between rapid change of the objective function and level of action is well understood.

Make bets on which companies will drive the changes. Predicting which software companies will win is becoming increasingly difficult, but this method relies on being a better forecaster. The reward, of course, is greater as the stakes are higher.

After a brief ”AI retreat” in 2023 I’ve been relying on the first approach.

It’s helped me move away from seeing software as inherently valuable and focusing other moats built around human attention (of which network effects are only one example).

How are you dealing with the future both strategically and mentally?

## Comments

**tim**: Really interesting setup, Peteris! 

You also just made me realize that I actually disagree with Bryan Johnson's conclusion, but I do agree somewhat with his premise, which for me is usually the opposite; I agree with conclusions, but I'm upset when people get their premises wrong.

As for how I'm dealing with uncertainty:

1. Creating control: I could have built Kiwi on X Y Z cloud platform. I could have made it dependent on this or that VC, etc. I could have also used the fiat money that I now spent on building the site to invest in Ether or the S&P500. Yet, I find myself uniquely well-positioned to take on uncertainty and have control over it, so I have invested heavily in myself. I love having choices and being independent. I think this is super important for me to deal with uncertainty. It will slow growth though.
2. Trust in the Lindy effect model of the future: I'm not exactly sure where he wrote this, but Taleb has a chapter — maybe it was in Anti - fragile—called "How to predict the future," which has become my go-to model for how the world works. I trust in this model, and it helps me to navigate.
3. Become really good at something, then don't plan: I love it when a plan works. Sometimes, I'm planning a weekend with friends and family, and when a plan works, I end up very happy. But I generally, in life and work, try to be very open-ended regarding plans. For work, for example, I do believe somewhat in determinism and that if I work hard enough, I will get some place some time. Yet, I don't plan to become the top CEO or German chancellor or whatever. During COVID or other scary moments in my life, I asked myself: How can I hedge an uncertain future? To me, the answer always has been that if I find joy in trying to become the best possible person, personally and in my career as a software dev, then in each individual moment, I never have to be fearful about my future because the accumulation of my decisions will always make sure that I end up in places that make me stronger or more proficient or sought-after. Although I gotta say, I have had some doubts about that recently with regard to family planning and high-risk startup work.

Btw. I think I kinda do all the prongs which you have mentioned:

- look for scarcity in creating network effects with software: that's Kiwi
- focus on short-term exploitation: I try to make Kiwi work NOW
- Make bets on companies that'll drive change: I've built Kiwi (which I believe in, obviously) on Ethereum, Farcaster, ENS, etc.

**misha**: Embracing uncertainty is a great attitude I think in approaching life and work. I am trying to do this for years and it has brought me a lot of good things and joy. Also it is quite relaxing. 

Instead of saying “I want to become … in the future.” - you can say “I don’t know what I can become.” It leaves room for discovery and truly creating something new. 

You can only think of stuff you already know. So if you focus on something specific predetermined, you are likely to miss opportunities along the way. Better to give yourself over to the journey and see where it takes you! 

**auditless.eth**: @tim - absolutely believe that patient bootstrapping (especially when activities are only focused on bottlenecks) is one of the best ways of thriving in times of uncertainty

@0xC304 - like this a lot, although I've always debated in my head whether more unique moments lie in depth (spending 10 years on something so you access insights that no other human has accessed) or breadth (traveling 70+ countries).

**macbudkowski.eth**: Interesting post, I think these 3 methods make perfect sense.

My take on LLMs, in particular, is that - before ASI - it will be just a tool. 

Today, it's an army of interns & junior employees. With GPT-5, it might become an army of senior employees. And with GPT-6, maybe staff-level employees. 

So it's still up to us to decide what they should do, which market to choose, what goals to follow, how to build what we want to build, and so on. 

And just like startups can have great teams and tons of cash and burn it all, the same goes for LLMs/AI. The difference is that the costs should be lower, so it might be easier to iterate.

**auditless.eth**: @mac - completely agree on LLM. It's a weird world where anyone has an infinite org below them but one that we can still reason about :).