Daily Self Improvement Journey - December 30, 2025

Every day, I strive to get better and learn something new. As I pursue a career in the commercial real estate industry, it is important I am keeping updated and constantly learning. My daily goals start by quickly going over the 10-point from the WSJ.

Then they are to read one real estate focused article and one broader economy article. I will write about each article – what I learned, my opinion or a takeaway.

Next, I will shift away from finance, building off of “three-minute takeaways.” I will summarize one podcast where I will learn something that improves my life. Whether its communication, mindset, etc.

Lastly, I will master one random question from a finance Interview Guide.

Real Estate

             I learned a few things about Copper from this article by Bisnow. The first being how important copper is in new asset classes like data centers. With very few substitutes to copper, the demand is extremely high. So, the first concern is the supply risk. Copper mining hasn’t been able to keep up with supply and that is one factor in this price surge. Another factor is the tariffs imposed by the Trump administration. Trump imposed a 50% tariff on copper products that would be used for U.S. building projects. The article concluded by saying that the hard costs of copper that are built into construction and development are just something that developers are going to have to pencil into their underwriting.

My opinion is that although raising copper prices are going to add to the already expensive construction cost, that since data centers have the highest demand of copper, it won’t slow down development in the short term. The amount of investment and demand being put into data centers will not be affecting by raising copper prices. I believe firms like TSMC, Amazon, Meta, OpenAI, etc are willing to deal with extremely expensive costs in order to meet this incredible demand for data centers. Another thing to consider is that this will have an indirect negative effect on any other asset class that also requires copper in their construction. So, it will be interesting to see how residential and retail development manages the increasing cost of construction.

Source: https://www.bisnow.com/national/news/construction-development/copper-hits-decade-highs-as-tariffs-continue-to-pile-onto-us-builders-132528

Economy & Finance

             After reading the article titled: A Dealmaking Frenzy is Reshaping the Booming Wealth-Management Business, I took a few things away. First, from a bigger picture outlook, wealth has grown tremendously since 2020, and this is giving more work to wealth management firms as their portfolios are much greater. I learned from this article that having a bigger portfolio to work with can offer the opportunity to invest in harder to come by things. These wealth management firms can have a seat at the table on some very exciting investment opportunities. The next thing I learned from this article is how these wealth management firms are being bought out because of their valuation growth and consistent pipeline. Private Equity is leading the way on this trend. They see the appeal of wealth management because of its low cost, low risk, but consistent returns and continued growth of wealth among clientele. There are other examples of wealth managers who are just buying out some of their competitors.

Source: https://www.wsj.com/finance/investing/a-dealmaking-frenzy-is-reshaping-the-booming-wealth-management-business-6ae921d6?mod=finance_lead_pos3

Three Minute Takeaway

2026 AI Disruption: Why Your Business Needs a Pilot, not a Science Project 

In a recent episode of the Moonshots podcast, host Peter Diamandis sat down with Matt Fitzpatrick, the CEO of Invisible Technologies and former head of Quantum Black Labs at McKinsey. They took a deep dive into why 2026 is set to be the "year of the largest disruption ever" and what businesses—from small startups to massive enterprises—need to do right now to avoid being left behind.

Why 2026 is the Deadline:

The conversation kicked off with a startling prediction: by 2026, companies that haven't fundamentally changed their relationship with AI are going to be "cooked". But Fitzpatrick offered a nuanced take. He isn't saying that all companies will disappear; rather, knowledge work as we currently know it is what’s on the chopping block.

If your business relies heavily on producing large amounts of documentation or standard "business process outsourcing" (BPO), the structure of your industry is already shifting. Sectors like legal services, media, and accounting are the front lines of this change. However, industries like oil, gas, or real estate might see more consistency in their core functions, even if their back-office admin gets a serious AI makeover.

The "A Thousand Flowers Bloom" Trap

One of the most relatable parts of the podcast was Fitzpatrick’s advice for CEOs who are currently feeling the pressure from their boards to "do something with AI". Most companies make the mistake of letting "a thousand flowers bloom," where they fund dozens of tiny, uncoordinated AI projects.

Fitzpatrick calls this a recipe for a "science project" that never makes it to production. Instead, his advice is refreshingly simple: pick two or three things that will materially move the needle for your business, whether that’s customer service, inventory management, or digital marketing, and focus entirely on getting those to a pilot stage.

Data Doesn't Have to Be a Five-Year Project

We’ve all heard that "data is the new oil," but many companies get paralyzed trying to clean up every single database before they start. Fitzpatrick argues that you don't need a five-year "data lake" project. You just need the specific data required for your chosen use case.

For example, if you're automating credit underwriting, you only need five or six core variables to be accurate, not the entire bank’s historical repository. He also pointed out that the most valuable data moving forward isn't just in spreadsheets, it's the "non-structured" data like images, videos, and the "trade secrets" of how your experts actually do their jobs.

The Human Element: A Feature, not a Bug

Perhaps the most surprising takeaway was the emphasis on "human-in-the-loop" systems. The podcast discussed the famous "Klarna (CLA) situation," where a company proudly replaced 700 agents with AI in month one, only to face complications later.

Fitzpatrick believes that for a long time, human expertise will be a requirement, not an optional extra. AI models are trained on precedent, but humans are needed for the "non-standard" cases where there is no historical data. He suggests that the most successful companies will use multi-agent teams, a mix of task-specific AI agents orchestrated by a human or a larger model, to ensure accuracy and "human equivalence".

What’s Next: Recursive Learning and Beyond

As we head toward 2026, we are entering the era of recursive self-improvement, where AI begins to help build better AI. We are moving away from broad, public benchmarks (like how well a model can code) toward hyper specific, custom benchmarks.

Whether it's the US Navy using AI for underwater drone swarms or the Charlotte Hornets using computer vision to scout the next basketball star, the message is clear: the technology is ready. The only thing missing for most companies is the operational grit to bake it into their business.

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Easy Takeaway: Stop trying to fix everything at once. Pick two or three high-value tasks in your business, ensure the specific data for those tasks is clean, and run a proof-of-concept that focuses on outcomes and measurable KPIs rather than just "playing" with the tech.

Favorite Quote: "I think the question is which parts of your business can really change with AI? It's not all of them... some sectors it'll be more or less".

A Question to Consider: If you were starting an "AI-native" version of your own company today from scratch, how many of your current manual processes would you actually keep?

Source: Diamandis, P. H. (Producer). (2024). Which Industries Survive AI, The New AI Benchmarks, and the 2026 Recursive Learning Timeline | #218 [Video]. YouTube. https://www.youtube.com/watch?v=your-link-here

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Random CRE Question

If I paid $100M for a building and it has 75% leverage, how much do I need to sell it for to double my equity?

First, find debt and equity.

-       Initial Debt = $100M * 75% = $75M

-       Initial Equity = $100M * 25% = $25M

Double Equity  $50MSo, the required sale price is $125M. Because then I would have $75M in debt and $50M in equity.

General Formula for multiples = [original equity * desired multiple + loan amount = required sale price]

·      These calculations assume that the lenders are capped by appreciation, and there are no cash flows that would contribute to the equity.