Positioning for an AI-Driven Economy with Both Growth Plays and the "AI-Resistant Investment"

If AI fulfills its promise, the economic effects will be profound. This article considers various investment ideas for navigating a theoretical world in which AI actually delivers successfully.

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In Part 1, I explained why Claude Code changed me from an AI skeptic to a believer, and talked about the effect of AI's improvements on computer programming and the AI-enabled builder. In Part 2, we talked about how coding accelerates AI progress, made the case for rapid change versus AI stalling/stopping, and considered, if AI succeeds, what profound scientific advances and/or economic revolution may come. In this article, we assume AI realizes its potential. And we ask the question: "How should an investor position themselves for an economy that's driven by AI?"

Investing to catch the AI wave

There are many investments that could benefit from AI achieving its goals.

1. AI itself:
   

   1. Public stocks: The "Magnificent Seven": Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta Platforms, Tesla

   2. Infrastructure: AI chips and data centers

   3. Venture capital: Stakes in AI startups
      

2. AI-enabled Builders
   

   "Builders" are a brand-new profession in which people are using AI-tools along with their human creativity and domain knowledge to solve business problems. And as we discussed in Part 1 , they are already springing up in some companies. As companies engage in the never-ending journey to stay competitive, be more efficient, and move into new markets, their needs grow as well. So this new sector is poised to fulfill a lot of pent-up demand. Some example are:
   

   1. Custom software creation (discussed in Part 1)

   2. Workflow improvement and automation (including implementing AI agents).

   3. Cybersecurity (AI models such as OpenClaw are already increasing attack risk… )

   There are also specific niches where companies have large needs that match up to the capabilities of AI-enabled Builders:
   

   1. Product design and customer experience (including rapid prototyping of full applications and personalized user experiences)

   2. Sales, marketing & growth (including market analysis, customer segmentation & targeting)

   3. Legal and compliance (including contract analysis, litigation prep and discovery)

   4. Financial modeling & forecasting

   5. Healthcare & medical operations

   6. Education and Training
      

3. AI labor-scarce / physical sectors
   

   If white-collar roles get squeezed, money and talent could shift toward areas that are outside AI's reach, such as hands-on physical work (at least until robots potentially arrive sometime in the future... but I feel that revolution is much further out, beyond our immediate future). The following areas, which I explore further later in this article, are AI "no-go" industries (shown in white on the Anthropic chart):
   

   1. Skilled trades

      1. Installation and Repair

      2. Construction

   2. Infrastructure maintenance

      1. Transportation

      2. Production

      3. Agriculture

      4. Grounds maintenance

   3. Health Services

      1. Personal care

      2. Healthcare support

      3. Childcare
         

4. Education & retraining
   

   When technology reshapes jobs, people need new skills. That tends to create demand for practical training.
   

   1. Vocational training platforms

   2. Trade schools

   3. Certification programs, Apprenticeship models
      

5. Legal / compliance / governance Every big technology shift creates new rules and oversight. AI likely won’t be different:
   

   1. RegTech (Regulatory Technology)

   2. Compliance service providers

   3. Audit infrastructure

   4. AI liability insurance (could see enormous growth)

   
   

6. Energy beyond data centers

   Everyone is already talking about data centers. But if AI keeps growing, the real bottleneck could become power and infrastructure.
   

   1. Grid upgrades

   2. Transformer manufacturing

   3. Nuclear / small modular reactors

   4. Natural gas infrastructure

      
      

   Power could quietly become one of the most important parts of the AI story, and would be a "pick-axes" play (similar to how selling pick-axes and supplies were a great way of making money during the California Gold Rush).

   
   

7. Dislocated white-collar asset plays

   For those with appetite for higher risk/reward. Periods of change usually create both winners and stressed sellers. If certain sectors struggle, there may be opportunity for:
   

   1. Buyouts of businesses slow to adopt AI

   2. Consolidation in service sectors
      

8. Human-centric premium brands

   Sometimes tech growth increases demand for things that feel "human". If AI makes things more automated and generic, people could end up valuing the "human touch" more.
   

   1. Luxury goods

   2. High-touch services

   3. Experiences

   4. Pets

This article series is a work in progress where I'm documenting my thoughts as things develop. So I welcome your feedback.

Please add your comments and suggestions in the Private Investor Club forum discussion on AI investing. (If you're not a member, you can join for free and just have to confirm you have no business connection with any sponsors or their affiliates). Next, let's dive into one that looks interesting. "#3 AI labor-scarce / physical sectors". And we'll look closer at potential pros and cons.

Rise of the "AI-Resistant investment"

The last major technology revolution was the internet. And that created Amazon and in turn caused the "retail apocalypse". Seemingly overnight, retail stores that had been around for decades (including Sears, Toys R Us and others) were wiped out and declaring bankruptcy one after another.

And at first, there was a huge panic and flight from all retail real estate because many people thought it all would completely disappear. But very quickly people realized that they overreacted --and not all retail could be "Amazoned". And by 2017-2019, the big focus in retail was on making sure everything was "Amazon resistant" retail. This was retail that people physically need to visit and included groceries, pharmacies, fitness centers, restaurants, nail salons, medical/dental clinics and more. And ultimately that strategy ended up working very well over the following years -- for many investors:

So I thought... that early panic sounds similar to what I'm hearing from some people right now.

And what would an "AI-resistant" investment fund look like?

One idea is targeting any of the AI no-go zones" (which are beyond even the theoretical capabilities of AI). I've highlighted this area in green (with interior in white) below:

There's a lot of fertile ground there and a lot of ways to do this. Here's one example of that kind of thinking: targeting skilled trades, healthcare services, education, and hands-on service jobs that are among the least vulnerable to AI automation:

Home services

• Plumbing

• HVAC

• Roofing

• Electrical

• Landscaping

• Construction
  

Property maintenance

• Fire safety inspections

• Elevator maintenance

• Pest control

• Parking lot maintenance
  

Local service providers

• Childcare centers

• Assisted living

• Physical therapy

• Veterinary clinics

• Spa/Beauty/Hair Salons
  

Industrial services

• Equipment repair

• Specialty manufacturing

• Waste handling
  

Why these work:

• Local monopolies

• Skilled labor shortages

• Hard to automate

• Hard to scale nationally

  
  

These are often called “small boring businesses that print money.”

Counter-Arguments and Rebuttals

Here are some counter arguments to the thesis of the AI-resistant investment, along with my rebuttals:

1) Flood of workers

One counter argument is that while today these are lucrative niches, they might not stay that way. One of the potential AI scenarios is that of wide-spread white collar job loss. If that happens, then there could be a rush of white-collar workers into these jobs. First, a mass-job loss and a rush of all workers into these jobs is not guaranteed (and if AI succeeds, a rise in new jobs like builders and others could slow this down, to much less extreme scenarios than Cintrini research envisioned in their "thought experiment" / "potential scenario"). And yes, if it does happen, it would definitely be bad for the workers, because it would reduce wages. However, that could actually be positive for a company owner (because wages are a major cost). Ultimately, what matters most for an owner is profit margin (revenue - cost). And so in this scenario, profitability wouldn't necessarily go down. Still, if people are concerned about wage-drops affecting investments, there are ways to address this. It's important to remember that different industries would be affected differently. Lots of people could become Uber drivers quickly and easily. Fewer would take the four years to apprentice as an electrician and pay for an expensive license. So an investor might focus on industries with larger barriers to entry or which require lengthy apprenticeships (or time-consuming / expensive licenses).

In fact, certain jobs with a large barrier to entry, like the electrician who makes a turn into robotics repairs, may produce a bump up in return on investment.

2) Robots

Another counter-argument to the thesis is that robots will soon be everywhere, ultimately taking over all the areas where AI can't currently go. I personally agree with the concept and feel it's likely to happen in the long run.

But again, while this is bad for employees, it could easily be a positive for the company owners, and, by extension, for investors. We may have a multitude of successful small businesses with owners making a good profit by ownership of robots that do the physical labor for them. And there could also be bigger companies purchasing these small businesses. Radical changes often bring about radical new business models. Also, the timing for worrying about robots (in regards to investing) might be quite far off. Quite a few robotics companies have been guilty of performing deceptive tricks to make robotics look much further along to the general public than they actually are:

Humanoid robots are riding a wave of hype ... However, most humanoid robots have not yet been deployed in real-world environments. Many humanoid robot demos are staged in tightly controlled settings, and some use teleoperation, where human operators pilot the robots remotely. Loosararian called teleoperation the "dirty little secret" of robotics, adding that robotics companies had a responsibility to inform customers about whether their humanoid robots are operating ... with human assistance. ... For the majority of the cases ... it's learning in the environment, and it has to do that with teleoperation," Loosararian said.

And in the short- to medium-term, many experts feel there are enormous challenges with that kind of ubiquitous robot use (much more difficult than what AI faces). This paper from Cognative explains the huge disconnect between the hype and public perception versus the actual state of the robotics technology:

Humanoid robots are technologically impressive but commercially premature. After years of promises and countless investor presentations showcasing sleek, agile humanoids, the industry faces a harder truth: .. ambition has outpaced practical readiness. ... critical gaps remain in dexterity, energy efficiency, and real-world applications...Regulatory, ethical, and infrastructural readiness still lags behind technical prototypes. As Rodney Brooks — one of the most respected figures in robotics — cautioned, “The world is just not quite ready for humanoids yet.” This statement is less about skepticism and more about engineering and commercial reality catching up to vision.

For example, it's currently very difficult to create a moving, humanoid robot that does not accidentally injure or kill people nearby -- and this is why most robots in real-world use are kept in an area separated far away from humans. Per the 2025 research paper from the Lincoln Centre for Autonomous Systems called "Human-robot interaction in unstructured settings":

Robots working near or alongside people in a shared space need behavior that humans can interpret, anticipate and coordinate with. ... safety ... remains difficult. ... real-world deployments inevitably encounter situations in which these capabilities prove insufficient.

And there are even more challenges to the making of a multi-purpose robot that can do the hundreds or thousands of tasks required of a job (which humans do very easily without thinking about them):

Per a January 2026 Business Insider article even a single-task robot is very challenging:

Daniela Rus, the director of MIT's Computer Science and Artificial Intelligence Laboratory, said there was a gap between robots' ability to perform tasks in a laboratory and their ability to take on jobs traditionally done by humans in the real world. "I can give you a robot that will fold your laundry and load your dishwasher, but it might cost you half a billion dollars," she said.

So there's lots of work left to do before robots are rolled out broadly into human-facing industries and interactions. And most feel that robots are more likely to comprise a much later wave (hopefully powered by the AI wave)... rather than joining this upcoming one.

However, if a person is concerned about robots causing lower profits, they could also concentrate on investments that are both AI- and robot-resistant (like many in the earlier list that depend on the human touch and/or the reassurance of a human's reputation and malpractice insurance, etc.).

3) Economic Recession

Many people worry that mass layoffs, especially if they're truly happening at an epic scale, could lead to a hollowing out of consumer demand for goods and services, resulting in a recession or even depression. Under normal conditions, this is the nature of economic supply and demand.

First of all, layoffs will only happen at a mass scale in areas where AI is very successful.

And if AI is successful, the conditions will not be normal, and that could be to our advantage.

As ________ explains in _______, the extremely cheap replacement of a large number of expensive human jobs by more highly skilled AI would mean two things simultaneously. (1) Companies would save a massive amount of money. And (2) total output would increase dramatically.

Just as with the invention of mass production, unemployment would be paired with a higher GDP. And, like in the days of the Industrial Revolution, making more products at much lower cost would mean companies can drop prices (cushioning layoffs) and yet still make a high profit.

Moreover, because it's not possible for AI to take all jobs at once (per the circle chart shown above in this article), the odds would probably be high that purchasing power would continue amongst those who've kept their jobs and/or are wealthy independently of jobs.

And as discussed in Part 2 of this article series, there are actually likely to be new jobs created by AI ("Builders," regulators, etc.), as well as innovative new forms of financial help for the jobless.

Movers and shakers have been brainstorming many ideas for how to support a middle class that, through no fault of its own, becomes jobless. And these include paying AI dividend payments tied to productivity gains; giving public ownership stakes in AI infrastructure; and distributing a Universal Basic Income (UBI) funded by some portion of the AI-created profits.

Former Governor of Rhode Island and former Secretary of Commerce, Gina Raimondo, proposes a new, multi-faceted public–private “grand bargain” that would also transform universities. Employers would define the skills they are needing and provide real-time demand and hiring data (possibly via AI). Government would fund new educational programs to retrain unemployed workers with needed skills, paying schools only upon actual job outcomes (not merely enrollment), and using tax credits to reward employers for effective training, hiring, and retention,

With funding dependent on job placement, education would shift focus from multi-year degrees (producing obsolete/non-employable skills) to short, job/employment-linked credentials. The idea would be to emphasize speed, modularity, and lifelong learning. 

Raimondo also recommends the "workforce model upgrade" of scale apprenticeships (“earn while you learn”) for fast-changing fields.

Taken together, these proposals provide a credible path for navigating large-scale job displacement. And this gives reason to hope that any economic discomfort will be temporary and soon canceled out by a small-business-led economic boom.

This idea is a work in progress and your input on this idea is welcomed. And you can discuss it (and other AI-resistant investments further in the Private Investor Club forum discussion on AI investing. (If you're not a member, you can join for free and just have to confirm you have no business connection with any sponsors or their affiliates).

What’s up next

I am in the middle of a deep-dive on AI (in this is only the beginning). And I’ll share what I learn via more articles.

The next article will probably focus on the revolutionary new development of desktop AI agents. These systems move beyond using AI in a chat in the cloud and instead operate directly on your computer to perform real tasks (and make AI much more usable). One example is OpenClaw, an open-source project created by an independent developer.

In just three weeks, it became the most downloaded open-source project in history.  And this is a record that took the former champion (Linux) three decades to achieve.

As Jensen Huang (founder and CEO of NVIDIA) said a few days ago:

Mac and Windows are the operating systems for the personal computer. OpenClaw is the operating system for personal AI. This is ... the beginning of a new renaissance in software.”

So there are lots of interesting and exciting developments to explore.

Conclusion

For the past several years, I’ve been skeptical of the AI hype.

And a lot of that skepticism is still justified. The industry has spent extraordinary amounts of money, most companies have seen very little real-world value from the product, and alot of the hype has been over-the-top.

But my experience with Claude Code changed my perspective.

For the first time, I saw a clear example of AI performing complex professional work at a level that would have seemed entirely impossible only a few months ago. And it did so far faster, cheaper, and better than any human developer could have managed.

If AI fails to overcome it's many challenges, then it may still end up following the path of many past overhyped technologies , and be delayed for a long time (or completely fall off the tracks).

Either way: what it's already accomplished will have a lasting affect on the economy and jobs. And if it's succeeds, then the affect on both and our society will be enormous.

And if exponential growth is sustainable, then — similar to what we saw with genome sequencing, computing power, and the internet — the future will be here sooner than most expect.

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