Investing in Ambiguity: A Portfolio Framework from AGI to Climate Hardware

October 6, 2025October 6, 2025 / lbhuston / Leave a comment

Modern deeptech investing often feels like groping in the dark. You’re not simply picking winners — you’re modeling futures, coping with extreme nonlinearity, and forcing structure on chaos. The research I’ve conducted in this area has been revealing. Below, I reflect on it, extend a few ideas, and flesh out how one might operationalize it in a venture or research‑lab context.

MacModeling

A. The Core Logic: Inputs → Levers → Outputs

At the heart of the structure is a clean mapping:

Inputs: budget, time horizon, risk tolerance, domain constraints, and a pipeline of opportunities.
Levers: probability calibration, tranche sizing (how much per bet), stage gating, diversification, optionality.
Outputs: expected value (EV), EV density (time‑adjusted), capital at risk, downside bounds, and the resulting portfolio mix.

That’s beautiful. It forces you to treat capital as fungible, time as a scarce and directional resource, and uncertainty as something you can steer—not ignore.

Two design observations:

Time matters not just via discounting but via the density metric (EV per time), which encourages front‐loading or fast pivots.
Risk budgeting isn’t just “don’t lose everything” — you allocate downside constraints (e.g. CaR95) and concentration caps. That enforces humility.

In practice, you’d want this wired into a rolling dashboard that updates “live” as bets progress or stall.

B. The Rubric: Scoring Ideas Before Modeling

Before you even build outcome models, you triage via a weighted rubric (0–5 scale). The weights:

Dimension	Weight
Team quality	0.15
Problem size / TAM	0.10
Moat / defensibility	0.10
Path to revenue / de-risked endpoint	0.15
Evidence / traction / data / IP	0.15
Regulatory / operational complexity (inverted)	0.10
Time to liquidity / cash generation	0.10
Strategic fit / option value	0.15

You set a gate: proceed only if rubric ≥ 3.5/5.

The beauty: you make tacit heuristics explicit. You prevent chasing “cool but far-fetched” bets without grounding. Also, gating early keeps your modeling burden manageable.

One adjustment: you might allow “strategic fit / option value” to have nonlinear impact (e.g. a bet’s optionality is worth a multiplier above a linear score). That handles bets that act as platform gambles more than standalone projects.

C. Modeling Metrics & Formulas

Here’s how the framework turns score + domain judgment into outputs:

EV (expected value) = ∑[p_i × PV(outcome_i)] − upfront_cost
PV: discount cashflows by rate r. For one‑off outcomes, PV = cashflow × (1+r)^(−t). For annuities, use the standard annuity PV factor, then discount to start.
EV per dollar = EV / upfront_cost
EV density = (EV per dollar) / expected_time_to_liquidity
Capital at Risk (CaR_α) = the loss threshold L such that P(loss ≤ L) ≥ α (e.g. α = 95%)
Tranche sizing (fractional‑Kelly proxy):
With payoff multiple b = (payoff / cost) − 1, and success prob p, failure prob q = 1 − p, the “ideal” fraction f* = (b p − q)/b. Use a conservative scale (25–50% of f*) to avoid overbetting.
Diversification constraints: no more than 20–30% of portfolio EV in any one thesis; target ≥ 6 independent bets if possible.

You also run Monte Carlo simulations: randomly sample outcomes for each bet (across, say, 10,000 portfolio replications) to estimate return distributions, downside percentiles, and verify your CaR95 and concentration caps.

This gives a probabilistic sanity check: even if your point‐model EV is seductive, the tails often bite.

D. The Worked Case Studies

Here are three worked examples (AGI tools, biotech preclinical therapeutic, and climate hardware pilot) to illustrate how this plays out concretely. I’ll briefly recast them with commentary.

1. AGI Tools (Internal SaaS build)

Cost: $200,000
r = 12%
3‑year annuity starting year 1
Outcomes: High / Medium / Low / Fail, with assigned probabilities
You compute PVs, then EV_gross = ~1,285,043; EV_net = ~1,085,043
EV per $ = ~5.425
EV density = ~10.85 / year
Using a fractional Kelly proxy you suggest allocating ~10% of risk budget.

Reflections: This is the kind of “shots on goal” gambit that high EV density encourages. If your pipeline supports multiple parallel AGI tooling bets, you can diversify idiosyncratic risk.

In real life, you’d want more conservative assumptions around traction, CAC payback, or re‑investment risk, but the skeleton is sound.

2. Biotech (Preclinical therapeutic)

Cost: $5,000,000
r = 15%
Long time horizon: first meaningful exit in year 3+
Outcomes: Phase 1 licensing, Phase 2 sale, full approval, or fail
EV_gross ≈ $10.594M → EV_net ≈ $5.594M
EV per $ ≈ 1.119
EV density ≈ 0.224 per year

Here, the low EV density, combined with a long duration and regulatory risk, justifies capping the allocation (e.g., ≤15%). This is consistent with how deep biotech bets behave in real funds: they offer huge upside, but long tails and binary risks dominate.

One nuance: because biotech outcomes are highly correlated (regulatory climates, volatility in drug approval regimes), you’d probably treat these bets as partially dependent. The diversification constraint must consider correlation, not just EV share.

3. Climate Tech Hardware Pilot

Cost: $1,500,000
r = 12%, expected liquidity ~3 years
Outcomes: major adoption, moderate, small licensing, or fail
EV_gross ≈ $2,614,765 → EV_net ≈ $1,114,765
EV per $ ≈ 0.743
EV density ≈ 0.248 per year

This is a middling bet: lower EV per cost, moderate duration, moderate outcome variance. It might function as a “hedge” or optionality play if you think climate tech valuations will re‑rate. But by itself, it likely wouldn’t dominate allocation unless you believe upside outcomes are undermodeled.

E. Sample Portfolio & Allocation Rationale

Consider the following:

You propose a hypothetical portfolio with $2M budget, moderate risk tolerance:

AGI tools: 6 parallel shots at $200k each = $1.2M
Climate pilot: a $800k first tranche with gate to follow-on
Biotech: monitored, no initial investment yet unless cofunding improves terms

Why this mix?

The AGI bets dominate in EV density and diversification; you spread across six distinct bets (thus reducing idiosyncratic risk).
The climate pilot offers an optional upside and complements your domain exposure (if you believe climate tech is underinvested).
The biotech bet is deferred until you can get more favorable terms or validation.

You respect concentration caps (no single thesis has > 20–30% EV share) while leaning toward bets with the highest time‐adjusted return.

F. Stage‑Gate Logic & Kill Criteria

Crucial to managing this model is a disciplined stage‑gate roadmap:

Gate 0 → 1: due diligence, basic feasibility check
Gate 1 → 2: early milestone (e.g. pilot, LOIs, KPIs)
Thereafter, gates tied to performance, pivot triggers, or partner interest

Kill criteria examples:

Miss two technical milestones in a row
CAC : LTV (or unit economics) fall below threshold
Regulatory slippage > 2 cycles without new positive evidence
Correlated downside shock across multiple bets triggers a pause

By forcing kill decisions rather than letting sunk cost inertia dominate, you preserve optionality to reallocate capital.

G. Reflections & Caveats

Calibration is the weak link. The EV and tranche logic depend heavily on your probability estimates and payoff assumptions. Mistakes in those propagate. Periodic Bayesian updating and calibration should be baked in as a feedback loop.
Correlation & regime risk. Deeptech bets are rarely independent — regulatory cycles, capital markets, macro shocks, or paradigm shifts can hit many bets simultaneously. Make sure your Monte Carlo simulation simulates correlation regime shocks, not just independent draws.
Optionality is more than linear EV. Some bets serve as “platform enablers” (e.g. research spinouts) whose value multiplies in ways not captured in simple discounting. Make sure you allow for a structural “option value” that escapes linear EV.
Time & capital liquidity friction. You may find you must pause follow-ons or reallocate capital midstream; your framework must be tolerant of “liquidity timing mismatch.”
Behavioral failure modes. Decision fatigue, emotional attachment to ideas, or reluctance to kill projects can erode discipline. A formal governance process—perhaps an independent review committee—helps.

H. Suggested Enhancements & Next Steps

Dashboard & real‑time monitoring: build a tool (in Notion, Google Sheets + Python, or custom UI) that ingests actual metrics (KPIs, burn, usage) and compares them to model expectations.
Bayesian updating module: as you observe results, update posterior probabilities and EV estimates.
Scenario overlay for regime risk: e.g. a “recession / capital drought” stress model.
Meta‑portfolio of strategies: e.g. combining “fast bets” (high EV density) with “venture options” (lower density but optional upside).
Decision governance & kill review cycles: schedule quarterly “kill / pivot reviews” where chosen bets are reassessed relative to alternatives.

I. Conclusion

This framework is so much more than a spreadsheet—it’s a philosophically coherent approach to venture investing in environments of radical uncertainty. It treats bets as probabilistic options, forces structure around allocation and kill decisions, and lets time-adjusted return (density) fight for primacy over naive upside.

I’d say the real acid test is: run it live. Drop in your real pipeline, score the opportunities, simulate your portfolio, place small bets, and see what your tail risks and optionalities teach you over five quarters.

Support My Work

Support the creation of high-impact content and research. Sponsorship opportunities are available for specific topics, whitepapers, tools, or advisory insights. Learn more or contribute here: Buy Me A Coffee

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

n=1: Living as a Person of Your Time

September 24, 2025 / lbhuston / Leave a comment

There’s a strange, powerful truth that often goes unsaid: most of our success, failure, identity, even relevance — is bound to the era in which we’re born.

I was born at a time that happened to align with the rise of the personal computer, the evolution of networking, and the early waves of the Internet. I grew up alongside it. My teenage years were filled with bulletin boards and local area networks, and by the time I entered the workforce, the digital transformation had begun. The timeline fit. The wind was at my back.

Entrepreneurship found me early too. I hit my stride during the explosion of multi-level marketing and the rise of the self-help scene. Those environments — flawed and messy as they were — gave me tools: confidence in public speaking, an understanding of social persuasion, and most of all, a belief that being different could be powerful. Even pro wrestling played its part. It taught me about persona — the value of a character who stands out and leans in.

These experiences weren’t universal. They were specific to my time. My life is a living experiment with a sample size of one — n=1.

ChatGPT Image Sep 24 2025 at 04 14 15 PM

Timeless Wisdom vs. Timely Application

I’ve always had mentors. A supportive family. A spouse who stands by me. And I’ve drawn heavily from Stoicism and spiritual teachings that have endured for centuries. But I don’t mistake timeless wisdom for universal utility.

What worked for Marcus Aurelius or even my own mentors doesn’t always work here, now, for me. That’s why nearly every major move I’ve made — in business, in life — has been driven by experimentation. Scientific method. Trial and error. Observing, adjusting, iterating. Always adjusting for context.

I hunt for asymmetry: small bets with big upsides. And I often use a barbell strategy — thank you, Ray Dalio — allocating the bulk of my resources into stable, known returns while reserving the rest for moonshots. Life, like any investment portfolio, is about managing risk exposure.

And I do it all as asynchronously as possible. Not just in how I work, but in how I think. Time is a tool. I refuse to be trapped by the tyranny of the immediate.

Lessons That Don’t Translate

If I had been born twenty years earlier, I might have missed the digital wave entirely. Or maybe I would have found a different current — maybe mainframes or military networks. If I were born twenty years later, I might have missed the golden age of early web entrepreneurship, but perhaps mobile and app ecosystems would have taken its place.

That’s the point. What worked for me worked because of my timeline. But it might not work for anyone else — even if it looks appealing from the outside.

That’s why I’m cautious about what I try to pass on. I don’t offer a playbook. I offer tools. Mental models. Systems thinking. Frameworks that others can adapt and test for themselves. And I encourage every single person to apply n=1 experimentation to those tools. Because the context in which you live matters just as much — or more — than the tool you use.

Legacy Without Monuments

When my time is up, I don’t need monuments. I’m not chasing statues or street names.

What I do hope for is simpler, quieter. I hope that others see my life as one lived with compassion, generosity, and love. I hope they learn from what I’ve tried, and test those learnings against their own lives. I hope they make better decisions, kinder impacts, smarter plays.

I hope they live their own n=1 experiment, tuned to their time, their truth.

Because the only real legacy is what echoes forward in the lives of others — not through imitation, but through adaptation.

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Advisory in the AI Age: Navigating the “Consulting Crash”

August 11, 2025August 11, 2025 / lbhuston / Leave a comment

The Erosion of Traditional Advisory Models

The age‑old consulting model—anchored in billable hours and labor‑intensive analysis—is cracking under the weight of AI. Automation of repetitive tasks isn’t horizon‑bound; it’s here. Major firms are bracing:

Big Four upheaval — Up to 50% of advisory, audit, and tax roles could vanish in the next few years as AI reshapes margin models and deliverables.
McKinsey’s existential shift — AI now enables data analysis and presentation generation in minutes. The firm has restructured around outcome‑based partnerships, with 25% of work tied to tangible business results.
“Consulting crash” looming — AI efficiencies combined with contracting policy changes are straining consulting profitability across the board.

AI‑Infused Advisory: What Real‑World Looks Like

Consulting is no longer just human‑driven—AI is embedded:

AI agent swarms — Internal use of thousands of AI agents allows smaller teams to deliver more with less.
Generative intelligence at scale — Firm‑specific assistants (knowledge chatbots, slide generators, code copilots) accelerate research, design, and delivery.

Operational AI beats demo AI. The winners aren’t showing prototypes; they’re wiring models into CI/CD, decision flows, controls, and telemetry.

From Billable Hours to Outcome‑Based Value

As AI commoditizes analysis, control shifts to strategic interpretation and execution. That forces a pricing and packaging rethink:

Embed, don’t bolt‑on — Architect AI into core processes and guardrails; avoid one‑off reports that age like produce.
Price to outcomes — Tie a clear portion of fees to measurable impact: cycle time reduced, error rate dropped, revenue lift captured.
Own runbooks — Codify delivery with reference architectures, safety controls, and playbooks clients can operate post‑engagement.

Practical Playbook: Navigating the AI‑Driven Advisory Landscape

Client triage — Segment work into automate (AI‑first), augment (human‑in‑the‑loop), and advise (judgment‑heavy). Push commoditized tasks toward automation; preserve people for interpretation and change‑management.
Infrastructure & readiness audits — Assess data quality, access controls, lineage, model governance, and observability. If the substrate is weak, modernize before strategy.
Outcome‑based offers — Convert packages into fixed‑fee + success components. Define KPIs, timeboxes, and stop‑loss logic up front.
Forward‑Deployed Engineers (FDEs) — Embed build‑capable consultants inside client teams to ship operational AI, not just recommendations.
Lean Rationalism — Apply Lean IT to advisory delivery: remove handoff waste, shorten feedback loops, productize templates, and use automation to erase bureaucratic overhead.

Why This Matters

This isn’t a passing disruption—it’s a structural inflection. Whether you’re solo or running a boutique, the path is clear: dismantle antiquated billing models, anchor on outcomes, and productize AI‑augmented value creation. Otherwise, the market will do the dismantling for you.

Support My Work

References

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Market Intelligence for the Rest of Us: Building a $2K AI for Startup Signals

June 23, 2025 / lbhuston / Leave a comment

It’s a story we hear far too often in tech circles: powerful tools locked behind enterprise price tags. If you’re a solo founder, indie investor, or the kind of person who builds MVPs from a kitchen table, the idea of paying $2,000 a month for market intelligence software sounds like a punchline — not a product. But the tide is shifting. Edge AI is putting institutional-grade analytics within reach of anyone with a soldering iron and some Python chops.

Edge AI: A Quiet Revolution

There’s a fascinating convergence happening right now: the Raspberry Pi 400, an all-in-one keyboard-computer for under $100, is powerful enough to run quantized language models like TinyLLaMA. These aren’t toys. They’re functional tools that can parse financial filings, assess sentiment, and deliver real-time insights from structured and unstructured data.

The performance isn’t mythical either. When you quantize a lightweight LLM to 4-bit precision, you retain 95% of the accuracy while dropping memory usage by up to 70%. That’s a trade-off worth celebrating, especially when you’re paying 5–15 watts to keep the whole thing running. No cloud fees. No vendor lock-in. Just raw, local computation.

The Indie Investor’s Dream Stack

The stack described in this setup is tight, scrappy, and surprisingly effective:

Raspberry Pi 400: Your edge AI hardware base.
TinyLLaMA: A lean, mean 1.1B-parameter model ready for signal extraction.
VADER: Old faithful for quick sentiment reads.
SEC API + Web Scraping: Data collection that doesn’t rely on SaaS vendors.
SQLite or CSV: Because sometimes, the simplest storage works best.

If you’ve ever built anything in a bootstrapped environment, this architecture feels like home. Minimal dependencies. Transparent workflows. And full control of your data.

Real-World Application, Real-Time Signals

From scraping startup news headlines to parsing 10-Ks and 8-Ks from EDGAR, the system functions as a low-latency, always-on market radar. You’re not waiting for quarterly analyst reports or delayed press releases. You’re reading between the lines in real time.

Sentiment scores get calculated. Signals get aggregated. If the filings suggest a risk event while the news sentiment dips negative? You get a notification. Email, Telegram bot, whatever suits your alert style.

The dashboard component rounds it out — historical trends, portfolio-specific signals, and current market sentiment all wrapped in a local web UI. And yes, it works offline too. That’s the beauty of edge.

Why This Matters

It’s not just about saving money — though saving over $46,000 across three years compared to traditional tools is no small feat. It’s about reclaiming autonomy in an industry that’s increasingly centralized and opaque.

The truth is, indie analysts and small investment shops bring valuable diversity to capital markets. They see signals the big firms overlook. But they’ve lacked the tooling. This shifts that balance.

Best Practices From the Trenches

The research set outlines some key lessons worth reiterating:

Quantization is your friend: 4-bit LLMs are the sweet spot.
Redundancy matters: Pull from multiple sources to validate signals.
Modular design scales: You may start with one Pi, but load balancing across a cluster is just a YAML file away.
Encrypt and secure: Edge doesn’t mean exempt from risk. Secure your API keys and harden your stack.

What Comes Next

There’s a roadmap here that could rival a mid-tier SaaS platform. Social media integration. Patent data. Even mobile dashboards. But the most compelling idea is community. Open-source signal strategies. GitHub repos. Tutorials. That’s the long game.

If we can democratize access to investment intelligence, we shift who gets to play — and who gets to win.

Final Thoughts

I love this project not just for the clever engineering, but for the philosophy behind it. We’ve spent decades building complex, expensive systems that exclude the very people who might use them in the most novel ways. This flips the script.

If you’re a founder watching the winds shift, or an indie VC tired of playing catch-up, this is your chance. Build the tools. Decode the signals. And most importantly, keep your stack weird.

How To:

Build Instructions: DIY Market Intelligence

This system runs best when you treat it like a home lab experiment with a financial twist. Here’s how to get it up and running.

🧰 Hardware Requirements

Raspberry Pi 400 ($90)
128GB MicroSD card ($25)
Heatsink/fan combo (optional, $10)
Reliable internet connection

🔧 Phase 1: System Setup

Install Raspberry Pi OS Desktop
- Download from raspberrypi.com
- Flash with Raspberry Pi Imager and boot it up.

Update and install dependencies

sudo apt update -y && sudo apt upgrade -y
sudo apt install python3-pip -y
pip3 install pandas nltk transformers torch
python3 -c "import nltk; nltk.download('all')"

🌐 Phase 2: Data Collection

News Scraping
- Use requests + BeautifulSoup to parse RSS feeds from financial news outlets.
- Filter by keywords, deduplicate articles, and store structured summaries in SQLite.
SEC Filings
- Install sec-api:
```
pip3 install sec-api
```
- Query recent 10-K/8-Ks and store the content locally.
- Extract XBRL data using Python’s lxml or bs4.

🧠 Phase 3: Sentiment and Signal Detection

Basic Sentiment: VADER

from nltk.sentiment.vader import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
scores = analyzer.polarity_scores(text)

Advanced LLMs: TinyLLaMA via Ollama
- Install Ollama: ollama.com
- Pull and run TinyLLaMA locally:
```
ollama pull tinyllama
ollama run tinyllama
```
- Feed parsed content and use the model for classification, signal extraction, and trend detection.

📊 Phase 4: Output & Monitoring

Dashboard
- Use Flask or Streamlit for a lightweight local dashboard.
- Show:
  - Company-specific alerts
  - Aggregate sentiment trends
  - Regulatory risk events
Alerts
- Integrate with Telegram or email using standard Python libraries (smtplib, python-telegram-bot).
- Send alerts when sentiment dips sharply or key filings appear.

Use Cases That Matter

🕵️ Indie VC Deal Sourcing

Monitor startup mentions in niche publications.
Score sentiment around funding announcements.
Identify unusual filing patterns ahead of new rounds.

🚀 Bootstrapped Startup Intelligence

Track competitors’ regulatory filings.
Stay ahead of shifting sentiment in your vertical.
React faster to macroeconomic events impacting your market.

⚖️ Risk Management

Flag negative filing language or missing disclosures.
Detect regulatory compliance risks.
Get early warning on industry disruptions.

Lessons From the Edge

If you’re already spending $20/month on ChatGPT and juggling half a dozen spreadsheets, consider this your signal. For under $2K over three years, you can build a tool that not only pays for itself, but puts you on competitive footing with firms burning $50K on dashboards and dashboards about dashboards.

There’s poetry in this setup: lean, fast, and local. Like the best tools, it’s not just about what it does — it’s about what it enables. Autonomy. Agility. Insight.

And perhaps most importantly, it’s yours.

Support My Work and Content Like This

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Startups Face Uphill Battle Raising Series B in Challenging Funding Environment

August 20, 2024 / lbhuston / Leave a comment

The latest data paints a concerning picture for startups looking to raise Series B rounds. According to Crunchbase, U.S. startups are facing the longest Series B closure times since 2012, with a median of 28 months between Series A and B funding[1]. Out of 4,400 startups that raised a Series A in 2020-2021, only 1,600 (36%) have gone on to secure a Series B[1].

The Series B Crunch

Raising a Series B has always been a critical and challenging milestone for startups. At the Series B stage, investors expect to see strong business fundamentals, scalable unit economics, and a clear path to profitability[1]. Many startups that looked promising at the Series A stage stumble when it comes time to prove out their business model and show sustainable growth.

In the current environment, with tighter VC budgets and a flight to quality, Series B investors are being even more selective. They are focusing their dollars on startups with exceptional metrics and category-leading potential. Even well-funded Series A startups with strong teams and products are getting caught in the Series B crunch.

Sector Bright Spots

It’s not all doom and gloom though. Some sectors, particularly artificial intelligence, are still attracting large Series B rounds from investors eager to back the next breakout company.

Elon Musk’s xAI, for example, raised a massive $6 billion Series B just 6 months after its prior round[1]. Other hot AI startups like Anthropic and Adept have also raised supersized growth rounds in short order.

So while the bar for Series B is higher than ever for most startups, there are certainly exceptions for buzzworthy companies in the right sectors catching investors’ attention.

Advice for Founders

For the majority of startups, the key to navigating the perilous Series B landscape is to plan ahead, be realistic, and explore all options:

– Start early: Given the long Series B closure times, it’s never too early to start building relationships with potential Series B leads. Plant seeds 6-12 months ahead of when you’ll need the capital.

– Shore up insider support: The path of least resistance is often an insider round led by existing investors. Make sure you’re communicating proactively with your Series A investors and getting their buy-in to preempt or at least backstop your Series B.

– Consider alternatives: If traditional Series B funding is proving elusive, look into alternative financing options like debt, revenue-based financing, or even an early exit to a strategic acquirer. The name of the game is extending runway however you can.

– Be scrappy: With funding hard to come by, it’s time to shift into scrappy startup mode. Cut burn, extend cash, and do more with less. Demonstrate to investors that you can execute in a capital-efficient manner.

Raising a Series B in this environment is undoubtedly challenging for most startups. But with foresight, creativity, and grit, savvy founders can still find a way to get it done. After all, constraints breed innovation.

AI tools were used as a research assistant for this content. Written by Brent Huston with aid from Perplexity.

Citations:

[1] https://www.bizjournals.com/sanfrancisco/inno/stories/inno-insights/2024/07/18/series-b-gap-startups-higher-interest-rates.html
[2] https://houston.innovationmap.com/q4-2023-startup-funding-2666870949.html
[3] https://houston.innovationmap.com/2024-q1-funding-houston-startups-2667750361.html

Reduced Capital Returns to VC and PE Firms in 2024

August 19, 2024 / lbhuston / Leave a comment

The private equity (PE) and venture capital (VC) industry has faced challenges in 2024 with
reduced levels of capital returning to firms. Despite some optimism and resilience, the
slowdown in deal activity and exits has impacted the ability of PE and VC firms to return capital
to their investors.

Venture Capital Slowdown

The slowdown in VC deal activity, which began in Q3 2022, has persisted into Q1 2024. In the
first quarter, $36.6 billion was invested across 3,925 deals, comparable to the levels seen in
2023[4]. Factors contributing to this slowdown include high inflation, uncertainty about future
interest rate cuts, and geopolitical fragility.

Exits have been a significant issue for VC firms. Q1 2024 saw exit values of $18.4 billion, only
slightly better than most quarters in 2023. The lack of exits has particularly affected unicorn
companies and their investors, with an average holding period exceeding eight years,
increasing liquidity risk[4].

Private Equity Challenges

Private equity activity saw its strongest quarter in two years in Q2 2024, with firms announcing
122 deals valued at $196 billion[5]. However, the valuation gap between sellers and buyers has
been a primary impediment to deal-making since interest rates began rising in mid-2022.

The lack of liquidity and distributions back to limited partners (LPs) has made them cautious
when allocating capital to PE funds[4]. This has contributed to a slowdown in fundraising, with
only 100 VC funds raising $9.3 billion in Q1 2024.

Impact on Limited Partners

Limited partners investing in private markets have been affected by the reduced capital returns.
In a survey, 61% of LPs reported that they will increase their asset allocation to private credit in
2024[1], potentially seeking alternative investment opportunities.

The fundraising outlook for PE firms has slightly improved, with only 15% of general partner
respondents expecting deteriorating conditions in 2024, compared to 45% at the start of 2023.
However, VC firms still have concerns about LPs reducing their allocation to venture capital[1].

Looking Ahead

Despite the challenges, there are some positive signs for the PE and VC industry. Corporate
investors have signaled plans to increase investment in corporate venture capital funds in
2024[3], expanding the pool of available capital. Additionally, PE firms have accumulated a
record $317 billion in dry powder as of Q1 2024, resulting from strong fundraising in 2021-2022
and a slowdown in capital deployment[4].

As the industry navigates this challenging period, entrepreneurs and fund managers will need
to focus on building resilient, profitable companies and managing capital carefully. Those who
can adapt and demonstrate clear paths to growth will be best positioned to attract investment
and succeed in the current environment[3].

Citations:
[1] https://press.spglobal.com/2024-04-29-Private-Equity-and-Venture-Capital-Industry-
Shows-Resilience-and-Optimism-in-2024-Amidst-Shifting-Market-Dynamics-according-to-S-
P-Global-Market-Intelligence-survey
[2] https://www.cambridgeassociates.com/insight/2024-outlook-private-equity-venture-capital/
[3] https://www.ey.com/en_us/insights/growth/venture-capital-market-to-seek-new-floor-
in-2024
[4] https://www.eisneramper.com/insights/financial-services/venture-capital-q1-vc-blog-2024/
[5] https://www.ey.com/en_gl/insights/private-equity/pulse

* AI tools were used as a research assistant for this content.

The Great Vendor Concentration Risk Circus: A Brave New World?

July 22, 2024 / lbhuston / Leave a comment

Hey folks, buckle up because we’re diving into a wild tale that became the talk of the tech town this past weekend—the CrowdStrike and Microsoft outage! As always, I’m here to keep it light on the details but heavy on the takeaways. So grab your popcorn, and let’s roll!

ConcentrationRisk

First up, let’s chat about vendor concentration risk. In simple terms, it’s like putting all your eggs in one basket, or as I like to call it—having one favorite vendor at the carnival. Sure, they may have the greatest cotton candy, but when the vendor runs out, or their machine breaks down, you’re left sad and craving sugar! That’s what this outage highlighted for everyone relying on cloud services and cybersecurity—if that one vendor stumbles, everyone in line ends up feeling it![2][4]

Now, what happened with CrowdStrike and Microsoft? Well, it turns out that a software update on July 18 flung a wrench in the gears of countless IT systems across the globe. Reports came flooding in from big-name institutions—banks, airlines, and even emergency services were caught in the chaos! Over 8.5 million Windows devices were affected, reminding us just how interconnected our tech ecosystems truly are.[3][4]

So, what can we learn from this whole spectacle?

1. Diversify Your Vendors: Don’t just eat at one food stall! Utilize multiple vendors for essential services to reduce the fallout if one faces a hiccup.[1][2]

2. Communicate with Employees: Keep your team informed and calm during hiccups. This situation showed us how vital communication is during a tech mishap.

3. Prepare for Disruptions: Have contingency plans! Know what to do if your vendors experience turbulence.[1][2]

In closing, while tech might have some dramatic glitches now and then, they are vital reminders of our interconnected world. Let’s take this as a fun little lesson in preparedness and resilience! Until next time, keep your systems and vendors varied and safe!

Citations:

[1] https://www.venminder.com/blog/pros-and-cons-of-vendor-concentration-risk

[2] https://mitratech.com/resource-hub/blog/what-is-concentration-risk/

[3] https://edition.cnn.com/2024/07/22/us/microsoft-power-outage-crowdstrike-it/index.html

[4] https://www.usatoday.com/story/money/2024/07/20/how-microsoft-crowdstrike-update-large-impact/74477759007/

[5] https://ncua.gov/regulation-supervision/letters-credit-unions-other-guidance/concentration-risk-0

AI tools were used as a research assistant for this content.

How Running a BBS Shaped My Path in Information Security

June 25, 2024June 25, 2024 / lbhuston / Leave a comment

Today, I want to share with you how my early experiences with Magick Mountain BBS laid the foundation for my career in information security and my role at MicroSolved.

80sIBM

It all started in the late 80s when my fascination with telecommunications and the allure of digital communication systems led me to discover the world of Bulletin Board Systems (BBS). By 1989, I had launched Magick Mountain BBS, a platform that began as a simple operation on an IBM PC clone and evolved into a sophisticated network on an Amiga 500, serving a bustling community interested in everything from programming to hacking.

Running Magick Mountain was like stepping into a new world where information was at our fingertips, albeit not as seamlessly as it is today with the internet. This was a world where modems connected curious minds, and every conversation could spark an idea. The BBS hosted discussions on a myriad of topics, from technology to social issues, and became a central hub for like-minded individuals to connect and share knowledge.

The technical challenges were significant. Setting up and maintaining the BBS required a deep understanding of hardware and software. I juggled DOS systems, dealt with dual floppy setups, and later navigated the complexities of Amiga OS. Each upgrade taught me resilience and the importance of staying current with technological advances, skills that are crucial in the ever-evolving field of cybersecurity.

But what truly shaped my career was the community management aspect. Magick Mountain was more than just a platform; it was a community. Managing this community taught me the delicate balance of fostering open communication while ensuring a safe environment—paralleling the core challenges of modern cybersecurity.

These early experiences honed my skills in handling sensitive information and spotting vulnerabilities, paving the way for my transition into the corporate world. They ingrained in me a hacker’s mindset of curiosity and pragmatism, which later became instrumental in founding MicroSolved in 1992. Here, I applied the lessons learned from BBS days to real-world information security challenges, helping businesses protect themselves against cyber threats.

Reflecting on the evolution from BBS to today’s digital ecosystems, the principles of community building, knowledge exchange, and security management remain as relevant as ever. These principles guide our work at MicroSolved, as we navigate the complexities of protecting enterprise systems in an interconnected world.

To those aspiring to make a mark in cybersecurity, my advice is to nurture your curiosity. Dive deep into technology, join communities, share your knowledge, and keep pushing the boundaries. The digital world is vast, and much like the BBS days, there’s always something new on the horizon.

Thank you for reading. I hope my journey from running a BBS to leading a cybersecurity firm inspires you to pursue your passions and explore the endless possibilities in the digital realm.

*AI was used in the creation of this content. It created the final draft based on a series of interviews and Q&A sessions with an AI engine. All content is true and based on my words and ideas in those interviews and Q&A sessions.

The Entrepreneur’s Guide to Overcoming Fear of Failure

June 18, 2024 / lbhuston / Leave a comment

Fear of failure is a common barrier that holds back many aspiring entrepreneurs. It’s a natural response to the uncertainty and risks involved in starting and running a business. However, overcoming this fear is crucial for success. Here are some insightful strategies and real-world examples to help you embrace risk and failure on your entrepreneurial journey.

Thinking

1. Understand That Failure is a Learning Opportunity

– Embrace a Growth Mindset: Entrepreneurs need to see failure not as a dead-end but as a stepping stone to success. Adopting a growth mindset helps you learn from mistakes and continuously improve.
– Example: Thomas Edison famously failed thousands of times before inventing the light bulb. He saw each failure as a lesson, saying, “I have not failed. I’ve just found 10,000 ways that won’t work.”

2. Set Realistic Goals and Manage Expectations

– Break Down Big Goals: Setting smaller, achievable goals can reduce the fear of failure. It makes the larger objective seem more manageable and provides a sense of accomplishment along the way.
– Example: When launching a new product, start with a pilot project or a small market test. This approach allows you to gather feedback, make adjustments, and reduce the financial risk.

3. Build a Support Network

– Seek Mentorship and Advice: Surround yourself with experienced entrepreneurs who can provide guidance and share their experiences of overcoming failure.
– Example: Sara Blakely, the founder of Spanx, credits much of her success to the advice and support she received from mentors. Their encouragement helped her persevere through the challenges of building her business.

4. Prepare for Failure

– Have a Contingency Plan: Being prepared for potential setbacks can alleviate the fear of failure. A well-thought-out contingency plan can help you navigate difficulties with confidence.
– Example: Dropbox initially faced significant challenges with its business model and competition. By having backup strategies and being flexible, they were able to pivot and refine their product, eventually achieving massive success.

5. Embrace Calculated Risks

– Evaluate Risks and Rewards: It’s essential to take risks, but they should be calculated. Assess the potential impact and benefits before making a decision.
– Example: Jeff Bezos took a calculated risk when he left his secure job to start Amazon. He weighed the potential rewards against the risks, deciding that the opportunity was worth pursuing despite the uncertainty.

6. Focus on What You Can Control

– Control Your Effort and Attitude: While you can’t control every outcome, you can control your response and the effort you put in. This focus can reduce anxiety and increase resilience.
– Example: Elon Musk has faced numerous failures with SpaceX and Tesla. His relentless work ethic and positive attitude have helped him persist and ultimately succeed, despite setbacks. (Of course, he might also be insane…)

7. Learn from Others’ Mistakes

– Study Successful Entrepreneurs: Understanding how others have navigated failure can provide valuable insights and strategies.
– Example: Richard Branson, founder of the Virgin Group, openly shares his business failures. By learning from his experiences, aspiring entrepreneurs can avoid similar pitfalls and adopt effective strategies.

8. Reframe Failure as Feedback

– View Failure as Constructive Criticism: Instead of seeing failure as a negative outcome, treat it as feedback on what needs improvement.
– Example: The creators of Angry Birds, Rovio Entertainment, developed 51 unsuccessful games before achieving global success. Each failure provided valuable feedback that led to the creation of their hit game.

9. Develop Resilience and Perseverance

– Cultivate a Resilient Mindset: Building mental toughness helps you bounce back from setbacks and stay committed to your goals.
– Example: J.K. Rowling faced numerous rejections before finding a publisher for Harry Potter. Her resilience and perseverance paid off, leading to one of the most successful book series in history. (Note: I am not a fan of Ms. Rowling, but she is a good example here…)

10. Celebrate Small Wins

– Acknowledge Progress: Recognizing and celebrating small achievements can boost your confidence and motivation.
– Example: When launching a startup, celebrate milestones such as securing initial funding, launching a website, or gaining the first 100 customers. These small wins can keep you motivated through challenging times.

Conclusion

Overcoming the fear of failure is essential for entrepreneurial success. By understanding that failure is part of the journey, setting realistic goals, building a support network, and embracing calculated risks, you can navigate the uncertainties of entrepreneurship with confidence. Learn from others, reframe failure as feedback, and develop resilience to keep moving forward. Remember, every successful entrepreneur has faced failure—what sets them apart is their ability to learn, adapt, and persist.

* AI tools were used as a research assistant for this content.