One actionable step I took was to paper trade a new systematic strategy after building and backtesting it with realistic constraints like transaction costs, slippage, and position sizing. That exercise forced me to learn how the strategy behaved in live market conditions without risking capital. The knowledge changed my decisions by shifting my focus from headline returns to drawdowns and consistency across different market regimes. It also made me more selective about which ideas to fund, since only strategies that held up in both backtesting and forward testing earned an allocation.
Before I bought my first rental, I did one "inspection day" with a local property manager and asked to see their move-in/move-out inspection templates, photo/video documentation standards, and security deposit reconciliation process. That one step taught me what actually damages returns in rentals: not "rent comps," but deferred maintenance, tenant quality, and how well condition is documented. That knowledge changed my buy box immediately: I stopped chasing the prettiest remodel and started underwriting for maintenance response time and process quality (I now run our ops at MVPM around a 48-hour maintenance response target and detailed inspections). It also made me price in realistic turn costs and vacancy, instead of assuming "passive" meant "no friction." Concrete example: I passed on a "great deal" duplex because the seller couldn't produce any condition records and the deferred maintenance was obvious once I walked it like an inspector (not like a buyer). I took a smaller, cleaner single-family instead, and it's exactly why I care so much about documentation and occupancy in my business today (we run ~98% occupancy across our portfolio).
When evaluating a new asset class, I first look at my existing accounts and assess how its tax treatment would differ in a 401(k)/IRA, a Roth, or a taxable brokerage account. I'll typically focus on whether the asset produced regular taxable income or mainly unrealized gains. That review will lead me to place income-producing holdings in tax-advantaged accounts and to keep tax-efficient holdings in taxable accounts. As a result, I can better control the timing and character of taxable income and avoid unnecessary taxes when diversifying.
I like to invest in areas where I feel I have an unfair advantage. That means my work or my network exposes me to the sector or investment opportunity in way that allows me to have a good understanding of where I am putting my money. I like to feel confident in not only the asset class from a knowledge perspective but also must feel that the asset class is currently undervalued because of that knowledge. Before I make an investment, I'm doing my own research as well as talking to people in my network that are closer to it. AI is a great example of something I've continued to build exposure to because my work provides access to many companies that are benefiting from the technology. Investing in the infrastructure to power the technology feels like a good bet backed in data. I always encourage friends looking to diversify to do their own research and lean on experts in their network to get them more comfortable before making the leap.
I ran a 30-day paper trading simulation on a demo account through Kraken before putting real money into crypto. This tested volatility hands-on through a mock cycle, clarifying my risk limits with no cash risk. It led me to cap crypto at 5% of portfolio with stop-losses and dollar-cost averaging, dodging a 60% drop that hit others.
One actionable step I take is a "one-sleeve deep dive" in Altruist: I build a tiny, isolated position in the new asset class inside a test portfolio, then track it weekly against what it's supposed to do (correlation to equities, drawdown behavior, liquidity, and taxes). I started doing this after years at Riverstone/Brightway and now run it in Seek & Find so the learning is real money + real reporting, not theory. Example: when clients asked about Bitcoin as a diversifier, I didn't start with opinions--I watched how it behaved during actual stress. In April 2025, equities were choppy (S&P 500 about -0.8%), gold spiked near $3,500/oz, the 10-year ended around ~4.2%, and Bitcoin dropped with risk assets early, then ripped back and reclaimed $90,000 and finished the month roughly 20% off its low. That changed my investment decisions in a concrete way: I stopped treating it like "digital gold" and started underwriting it as a high-volatility, sentiment-driven risk asset with occasional crisis-hedge moments. So if it's used at all, it's sized small, rebalanced mechanically, and never funded from the client's true stabilizers (cash reserves, short-duration bonds, or tax payments).
One step I took was building a personal weekly briefing where I pushed myself to study the same new asset class from three different angles. I chose one detailed industry report, one source focused on regulation or taxes, and one critical opinion that challenged the trend. Each week I wrote a one page summary and included a short checklist covering risks, liquidity, custody, and realistic return drivers. Over time, this habit changed how I made decisions. I stopped following market stories and began factoring in real world issues like lockups and fees. I also created an entry rule that required at least two independent sources to support the main idea. Because of that, I started with smaller positions and added more only when I could explain the investment clearly and handle tough scenarios with confidence.
I joined an investor community where members share reviews after closing deals, and I committed to reading one review every morning for a month. I took notes on what the investor expected, what actually happened, and which decision created the gap. Then I gathered those lessons into a simple personal red flag list. This routine helped me see patterns that I had missed before and gave structure to my thinking. The impact showed up quickly in how I approached new opportunities. I began asking clearer questions before putting money into any deal. Instead of getting carried away by the narrative, I focused on alignment, reporting habits, and how teams respond when results fall short. I also adjusted my diversification rule and only invested when I saw steady execution across different market conditions.
Before I diversified into digital assets and startup equity, the one actionable step that changed everything was committing to spending thirty days consuming only primary source material rather than secondhand opinions. I stopped reading blog posts and hot takes about cryptocurrency and instead read the actual Bitcoin whitepaper, Ethereum documentation, and regulatory filings from the SEC. I studied on-chain analytics tools and learned to read blockchain explorers the same way traditional investors read balance sheets. This deep-dive approach fundamentally changed my investment decisions in three ways. First, it gave me the confidence to invest during periods of extreme market fear because I understood the underlying technology and its long-term potential rather than reacting to headlines. When prices dropped significantly, I could distinguish between a temporary market correction and a fundamental flaw in the asset class. Second, it helped me identify which specific projects within the broader crypto ecosystem had genuine utility versus those built entirely on speculation. This saved me from allocating capital to projects that eventually went to zero. Third, understanding the technical foundation allowed me to recognize how blockchain technology would eventually intersect with the software industry I already understood deeply as a CEO. The broader lesson I took away is that surface-level education creates surface-level conviction. When you only understand an asset class through other people's summaries, you will panic sell at the first downturn because you lack the foundational knowledge to trust your own analysis. Going directly to primary sources builds the kind of informed conviction that lets you hold through volatility and size your positions appropriately rather than gambling based on someone else's enthusiasm.
One practical step I took when exploring a new asset class was something I call "following the lifecycle instead of the hype." Instead of jumping straight into market analysis or price charts, I spent time mapping out how value actually moves through the ecosystem around that asset. For example, when I started looking more closely at alternative investments, I made a habit of reading the operational details that most people skip—how assets are issued, who the intermediaries are, how liquidity actually works, and what happens when someone tries to exit the investment during a downturn. It's the equivalent of looking under the hood rather than admiring the car's paint job. That exercise changed how I approached diversification. I realized that many assets that appear different on the surface are actually driven by the same underlying forces—interest rates, liquidity cycles, or regulatory shifts. Once you see those connections, diversification becomes less about collecting different assets and more about understanding how they behave under the same economic pressure. What surprised me most is that this approach often makes you more patient. Instead of feeling pressure to invest quickly because an asset class is trending, you become comfortable watching from the sidelines until you genuinely understand the mechanics behind it. In my experience, that small shift—from studying returns to studying systems—has probably prevented more bad investment decisions than any financial metric ever could.
Progress compounded when I sought out one person who had already navigated the failures I was about to encounter. Before moving any capital into private credit, an asset class I understood conceptually but had no direct experience with, I spent three months doing what felt like inefficient research. Industry reports, fund prospectuses, and webinars from managers with obvious marketing incentives. I came away with a reasonable surface understanding and a persistent sense that I was missing something important that the material wasn't surfacing. The turn came when I was introduced through a mutual contact to someone who had allocated to private credit funds across two full credit cycles. Not a manager. Not an advisor with a product to sell. Someone who had sat in the investor seat, made real decisions with real capital, and had specific experiences with what went wrong and why. Two conversations with that person were worth more than the three months of formal research because the framing was honest in a way that prepared materials never are. Which covenant structures had actually protected investors when things deteriorated. Where the liquidity representations in fund documents diverged from the practical reality of getting capital back. What questions to ask managers that would reveal whether they had genuinely navigated stress or just operated in favorable conditions. The impact on my decision-making was direct. I passed on two funds that would have looked reasonable on paper but had specific structural characteristics the formal research hadn't flagged as meaningful. I sized my initial position more conservatively than I'd planned, which turned out to be right given how that credit environment developed. For any new asset class, prioritize access to honest retrospective experience over comprehensive forward-looking analysis. The analysis tells you how it's supposed to work. The person who lived through the cycle tells you how it actually does.
I treated "VA benefits as an asset class" before I ever put money toward anything else: I did a one-page eligibility audit using my DD-214 + medical records + service history, then filled VA Form 22-1990 to map out my Montgomery GI Bill path (months of entitlement, time limits, and payment rules). Running USMilitary.com since 2007 forced me to build this habit because our audience expects concrete, not vibes. That single step showed me my real ROI wasn't a new investment account first--it was unlocking benefits I'd already earned, especially education funding and disability-related comp/care options (including Aid & Attendance scenarios for older vets/spouses). Knowing my "benefit cashflow" and timelines let me stop guessing and start planning. Impact on decisions: I delayed higher-risk diversification until I had my benefits plan nailed down and my education funding predictable, then I used that stability to pursue career moves with better upside instead of chasing returns under pressure. It also made me underwrite risk like the military: documents first, assumptions last.
I built a small dashboard to track the asset class weekly for ninety days before investing. It included pricing history, volume, spread, regulatory headlines, and a simple list of catalysts that moved the market. I also documented my predictions each week so I could see where my understanding was weak. The impact was immediate and clear. I realized my initial thesis relied on headlines more than fundamentals. By the time I diversified, I had a cleaner entry plan and fewer surprises. I avoided overpaying during a hype cycle and chose a strategy that matched my time horizon. The bigger win was discipline, as I treated learning like due diligence, not entertainment.
I made an appointment with a fee-only financial advisor to develop a retirement plan and to determine which investments would help me achieve my objectives. The fee-only advisor had us begin at our life milestones and work backward to define a specific monthly expense goal and a buffer or "safety margin" that we could fall back on in case of an unexpected event. Based on this advice, I set up automated contributions and prioritized the discipline of steady savings (as opposed to trying to time short term market movements) as my diversification choices were influenced by the need to maintain enough diversified assets to cover my monthly expenses and provide myself with a sufficient buffer against the unexpected so that I would be less anxious about my ability to make long-term progress.
When I enter into a new asset class, my first step is always to build my own 'failure-mode' model from scratch. I do this so I can perform my own stress test on the investment to see where there will be a lack of liquidity. I don't work from some broker's forecast, instead, I perform my due diligence by running a stress test at a 30% market decline to see exactly where the liquidity is going to dry up. This way I have completely changed my approach to diversification, and instead of chasing the trends, I now employ a staged entry method. If I cannot identify the 'break point' in my numbers, I have not done enough research to risk capital in this asset class. This level of due diligence is essential, as the gap between perceived risk and actual liquidity is where most portfolios fail. By quantifying the downside first, I have been able to eliminate the syndrome of 'shiny object syndrome,' and I have been able to improve my ability to manage capital efficiently. The industry standard supports my opinion that due diligence is the primary means of risk management among alternative asset classes. Investing in a new asset class requires managing both the psychological aspect and physical aspect of investing. It can be easy to become excited about a new market and lose your way. Having a cold, hard model to test your actions against, keeps you grounded and does not allow you to gamble on the hype of the moment, but rather to take calculated steps toward long-term stability.
One actionable step I took was preparing a decision brief that summarized the asset class’s structure, regulatory context, liquidity considerations, and key counterparties. I used that brief to identify gaps in disclosure and to formulate targeted questions for subject-matter experts. The structured review made potential governance and compliance risks clear. As a result, I limited my initial allocation, set explicit entry criteria, and scheduled regular reviews before increasing exposure.
I educated myself by running a tactical, hands-on test: during the turbulent months of 2020 I temporarily reallocated 25 percent of client portfolios from high-growth to value dividend-paying stocks to observe their behavior. That purposeful reallocation let me see how dividend-paying value equities performed under stress compared with high-growth positions. The direct observation confirmed that those positions acted as a stabilizing component and helped preserve gains. As a result, I incorporated tactical asset allocation that balances flexibility with long-term objectives into client plans.
As a Founder & CTO with 15 years in SaaS, I saw many peers who tank portfolios by chasing crypto hype without a foundation. I knew early on that 70% of retail investors lost money because they missed the homework. I almost fell for the same trap with tokenized real estate until I hit the brakes to study the existing volatility. I focus on deep diving using MIT's Blockchain and Money course to get know the tokenisation fundamentals before spending. I put a hold on guessing and begin allocating exactly 10% of my portfolio as per structured data other than Twitter trends. This change shifted my strategy from random bets to consistent 20%annual returns with lower drawdowns. By simulating trades and cross-referencing Deloitte's asset reports, I've managed my research time in half and moved my average win rate from a coin flip to a solid 82% success rate. Currently my capital is stable and diversified enough which help me to fund my new tech ventures without stress.
One actionable step I took was to immerse myself directly in a new environment by joining a small startup and taking on multiple roles. That hands-on work let me learn product, strategy, and operations quickly rather than relying solely on theory. I used the same approach when evaluating a new asset class: prioritize practical exposure and direct observation before committing significant resources. This experience made me more comfortable assessing operational risks and led me to scale exposure only after I understood how the asset behaved in real settings.
One actionable step I take before touching a new asset class is a structured "allocator interview sprint": 10-15 short calls with family office principals/CIOs who actively deploy in that space, using the same 12-question checklist (return drivers, liquidity terms, fee stack, worst drawdown period, gating history, manager selection filters). Hosting Jets & Capital (where ~85% of attendees are verified allocators) makes this fast because I can pressure-test answers across multiple allocators in the same week, not just hear a manager's pitch. Example: before increasing exposure to private credit, I asked allocators what broke in 2020/2022 and what they changed (documentation, covenants, borrower concentration, leverage caps, warehouse lines, and how "monthly liquidity" actually behaved). The repeated pattern I heard was "avoid yield tourism," prioritize tighter covenants and simpler structures, and be skeptical of anything promising liquid-like terms on illiquid collateral. That knowledge directly changed my decisions: I stopped screening by headline yield and started screening by downside controls (covenant packages, lender rights, and portfolio concentration) and by whether the manager had real workout experience. I also sized the position smaller at first and required clearer liquidity expectations, which kept me from chasing higher-return profiles that were really just higher hidden leverage.