Emerging Markets: 10 Proven Black Swan Insights That Will Reshape How Elite Fund Managers Invest Globally
Emerging markets are not catching up to Silicon Valley — according to ByteDance’s first investor, they have been leading in AI commercialization for over a decade while most Western fund managers were not paying attention.
Key Takeaways
- Understand how emerging markets, particularly China, have been commercializing AI in social media, e-commerce, and drones years ahead of Western competitors — with TikTok and DJI as documented examples discussed in this episode.
- Learn how Jeremy Kranz believes the tokenization of capital markets may represent a more immediately transformative black swan than AI itself for fund managers operating in alternative assets.
- Discover why emerging markets outside the US are positioned to produce hundreds of capital-efficient, billion-dollar companies as the cost of building technology continues to decline.
- Explore the four-part founder assessment framework used by Sentinel — CEO vision, CFO bean counting, engineering capacity, and product management acumen — to evaluate enterprise-grade technology companies.
- Consider how stable coins and yield-bearing digital instruments may fundamentally change the mechanics of LP capital calls and counterparty risk management for fund managers in the near term.
Emerging Markets Have Been Leading on AI Longer Than Most Investors Realize
| Sector | Emerging Market Leader | Western Counterpart |
|---|---|---|
| Social Media AI | ByteDance / TikTok | Meta / Facebook |
| E-Commerce AI | Temu / Shein | Amazon |
| Consumer Drones | DJI | Multiple VC-backed startups |
| Facial Recognition | Three Red Dragons (pre-2015) | Nascent at same period |
Framework: Jeremy Kranz, Sentinel Global
Emerging markets, and China in particular, represent a case study in AI commercialization that Western investors have systematically underestimated for over a decade. In this episode of Making Billions Podcast, Jeremy Kranz, the original venture capital investor in the company that became TikTok, makes a pointed argument that the evidence has been hiding in plain sight. He explains that ByteDance did not outperform Facebook because of superior marketing or a better understanding of American teenagers; it was the prosecution of AI in social media that set the company apart.
Kranz extends this argument across multiple sectors, pointing to Temu and Shein in e-commerce and DJI in consumer drones as further examples of emerging markets executing AI-driven commercialization before their American counterparts. According to Kranz, DJI’s annual R&D expenditure on a given year was likely greater than all comparable US venture-backed drone companies combined. The commercialization of that research, including collision avoidance, extended flight ranges, and phone-based controllers, represented sophisticated AI implementation that American competitors had not yet achieved.
For fund managers assessing emerging markets as an investment category, Kranz’s framing is an important educational reference point. The Wall Street Journal has documented the growing AI competition between the US and China, but Kranz argues the public narrative still lags behind the commercial reality that sophisticated investors observed years earlier.
Emerging Markets and the Three Red Dragons: What Was Built Before the West Was Watching
Emerging markets received direct, concentrated government support for AI development more than ten years before OpenAI entered the public consciousness in the West. Kranz recounts visiting what the Chinese government designated as the Three Red Dragons, three AI companies created to accelerate specific areas of visual AI technology, prior to 2015. His firsthand account provides institutional investors with a useful historical lens for understanding how state-coordinated innovation in emerging markets can create durable commercial advantages.
The first Red Dragon applied facial recognition technology at population scale, enabling Alipay to create virtual bank locations where a billion users could authenticate transactions without visiting a physical branch. The second was responsible for processing feeds from 320 million cameras deployed across China, with documented capabilities that included identifying a wanted individual within five minutes inside a stadium of 80,000 people. According to Kranz, the implications for surveillance, credit scoring, and social compliance were already operational realities during his visits.
The third Red Dragon, which Kranz addresses more cautiously, focused on synthetic image generation, technology that, with a decade of subsequent development, has become one of the most consequential and contested capabilities in the AI ecosystem. The broader point Kranz makes for investors is that the framing of AI as a recent American innovation ignores a long and well-resourced development arc in emerging markets that predates the Western LLM moment by many years. The Bloomberg analysis of China’s AI ambitions aligns with this longer historical view.
Emerging Markets and 10 Black Swans: The Forces Reshaping Capital Allocation
Framework: Jeremy Kranz, Sentinel Global
Emerging markets are not the only source of disruption that Kranz believes fund managers are underweighting. In this episode, he introduces the concept of ten simultaneous black swans, low-probability, high-impact events that are converging in ways most institutional investors are not yet positioned to consider. These include fusion power, quantum computing, the tokenization of capital markets, the proliferation of AI-generated open source code, and the structural disaggregation of traditional asset classes.
Kranz is careful to distinguish between the exuberance around AI and the equally significant orthogonal forces developing alongside it. He notes that four major technology companies are each spending approximately $100 billion per quarter on AI infrastructure, yet the gap between AI’s perceived capabilities and its actual enterprise implementation remains substantial. His view is that investors who focus exclusively on AI may miss the black swans that are actually closer to inflection, particularly tokenization, which he describes as potentially the most immediately transformative force in capital markets.
The framework Kranz offers is less a prediction than an educational orientation: fund managers in emerging markets and developed markets alike should treat portfolio construction as a multidimensional problem that includes these orthogonal risks. As Kranz notes in the episode, the topics that dominate election debates are rarely the ones that define outcomes, and the same principle applies to investment theses. The SEC has begun addressing related issues around digital asset regulation that intersect with several of these emerging black swans.
Emerging Markets Fund Managers and the Tokenization Black Swan Closest to Inflection
Emerging markets investors and alternative asset managers operating in developed markets both face the same near-term disruption, according to Kranz: the tokenization of capital markets. He describes this as an “internet effect,” analogous to how the internet was built for communication but was not originally designed for finance. Blockchain-based tokenization introduces the possibility of embedding financial transactions into the fabric of digital infrastructure itself. The specific near-term application Kranz identifies is the stable coin, which he describes as taking the most liquid asset on earth, the US dollar, and making it even more liquid.
For fund managers, Kranz walks through a concrete operational example that illustrates the practical stakes. Under current private equity mechanics, a capital call goes out, LPs have two weeks to wire funds, and if the deal delays or falls through, the fund manager must return the capital, often after three months of lost interest for all parties. Kranz’s proposed alternative uses tokenized money market funds with smart contracts attached, allowing LP capital to continue accruing interest until the precise moment of settlement, at which point the transfer executes automatically.
Ryan Miller reinforces the point with a firsthand account of a colleague whose billion-dollar deal nearly collapsed because a single LP representing $20 million was unresponsive to a capital call. The counterparty risk that created that crisis, Kranz argues, would be structurally eliminated in a tokenized settlement environment. Investors interested in the regulatory trajectory of this space should review the SEC’s evolving guidance on digital assets and securities as the Clarity Act and Genius Act work through the legislative process.
Emerging Markets Founder Evaluation: Sentinel’s Four-Part Framework for Enterprise Tech
Credible articulation of where the market is heading and why this team is positioned to lead it
Command of current unit economics and a credible path to improvement even at negative margins
Demonstrated ability to build the technically difficult product the company claims it will deliver
Roadmap sequencing aligned with where enterprise customers actually are, not where the founder wants them to be
Framework: Jeremy Kranz, Sentinel Global
Emerging markets present unique evaluation challenges for venture investors, particularly when assessing whether a technology founder can successfully integrate with enterprise clients outside the US. Kranz explains that Sentinel built its Sentinel Labs practice specifically to address this problem, inverting the traditional VC model by spending the majority of research time with enterprise clients rather than founders. The goal is to understand the practical realities of enterprise adoption so that when founders come in, Sentinel can evaluate them against a grounded model of what actual customers need and where implementation typically fails.
The four-part founder assessment framework Kranz describes in this episode covers: the vision of the CEO, who must articulate a credible direction for where the market is heading; the bean-counting acumen of the CFO, who must understand current unit economics and a credible path to improvement even when those economics are deeply negative at the startup stage; the engineering capacity to actually build the technically difficult thing the company claims it will build; and product management discipline, which determines whether the team is sequencing their roadmap in a way that matches where enterprise customers actually are rather than where the founder wants them to be.
Kranz illustrates the product management point with an example from his work in the drone sector. Multiple US drone companies he evaluated in emerging categories were pitching roadmaps built around transforming insurance underwriting immediately, a vision so far ahead of the customer’s actual procurement process that it disqualified them in his assessment. By contrast, DJI’s approach was to start with a simple, demonstrable capability and build from there. The Harvard Business Review’s research on startup failure supports the emphasis on product-market sequencing as a critical variable for fund managers evaluating emerging markets founders.
Emerging Markets and Open Source: The Most Overlooked Investment Opportunity in Computing Today
Emerging markets and open source computing have intersected in ways that Kranz believes most investors have not yet fully priced. He traces the role of open source through every major technology super cycle, including WordPress powering approximately 40 percent of corporate websites, Chrome and Android built on Chromium, and Apple’s iOS stability rooted in a Linux kernel. His argument is that investors who dismissed open source in the 1990s and early 2000s missed the foundational layer of multiple trillion-dollar technology cycles.
The new development that Kranz identifies as particularly significant is that AI can now write open source code, creating a compounding effect that he believes is dramatically underappreciated by the broader investment community. In his view, the combination of falling costs to build software, AI-generated open source contributions, and global distribution infrastructure means the next wave of value creation will not come from finding one $100 billion company. It will come from identifying hundreds of capital-efficient, point-solution businesses that each reach $1 billion in value with minimal capital and talent requirements.
Kranz’s observation about emerging markets is particularly relevant here: outside the US, founders are already moving toward this model because they cannot compete on talent costs or capital access in the traditional venture paradigm. The implication for fund managers is that portfolio construction frameworks built around chasing large-cap unicorns may be less optimal than frameworks designed to capture a diversified basket of highly efficient, globally distributed businesses. The Forbes analysis of open source as an investment category provides additional context for this emerging markets thesis.
Emerging Markets Investing and the Three C’s: Sentinel’s Decision-Making Philosophy
Emerging markets investing introduces elevated complexity at the decision-making level, and Kranz describes a specific psychological framework Sentinel uses to govern how investment decisions are made internally. The three C’s, confidence, competence, and courage, function as a self-policing mechanism to ensure that no single psychological bias dominates an investment committee’s orientation toward a given deal. According to Kranz, a decision made on courage alone risks the WeWork outcome, where conviction in a narrative substituted for rigorous unit economics analysis.
The process at Sentinel requires that all team members be present for every decision, that every question and vote be recorded, and that deal leads be graded on their balance across all three dimensions. This creates accountability that Kranz believes is critical for training junior investors to develop healthy analytical habits rather than learning to advocate politically for deals. He references the work of author Jon Levy on team orientation and notes that the research increasingly supports diversity of thought and experience, what he and Ryan Miller discuss as neurodiversity, as a stronger predictor of decision quality than surface-level demographic similarity.
Kranz shares a striking academic example from Professor Adam Galinsky’s research at Northwestern, in which deliberately diverse and linguistically challenged teams consistently produced superior analytical output compared to cohesive, homogeneous teams, despite rating their own experience as painful and assuming they had performed poorly. The emerging markets application is direct: investment teams operating in unfamiliar geographies benefit from the same complementary diversity that produced better papers in the Galinsky study. The Harvard Business Review’s research on diverse teams and decision quality provides independent support for this framework.
Emerging Markets Fund Managers as Solution Architects: Building Deeper LP Relationships
Emerging markets fund managers and domestic fund managers alike face the same retention challenge: investors who feel they are only being pitched deals rather than genuinely served are less likely to remain committed LPs over the long term. Kranz describes Sentinel’s approach to LP relationships as solution architecting, a practice in which the firm provides personalized, timely research to investors that addresses their specific portfolio concerns rather than publishing generic market commentary for a mass audience. When one of Sentinel’s LPs with significant data centers exposure expressed concern about the launch of DeepSeek, Sentinel assembled a tailored research report specific to that investor’s holdings and published a redacted version publicly.
The philosophy behind this approach, as Kranz explains it, is that the traditional venture model of publishing blog posts for general audiences creates minimal differentiated value for any individual LP. By contrast, knowing the specific context of an LP’s portfolio and concerns allows for research that compounds trust over time. Kranz notes that Sentinel has also hosted conferences to share lessons from investments that did not work, including modular building construction, as a way of demonstrating intellectual honesty and building credibility even around outcomes that were disappointing.
Ryan Miller frames this as a distinction between being a pitch man and being a solution architect, a contrast that has direct implications for how fund managers position their value proposition to prospective and existing LPs. For alternative asset managers operating in emerging markets where LPs may have limited firsthand knowledge of local conditions, this kind of curated intelligence function becomes an even more differentiated service offering. The Investopedia overview of LP dynamics in private funds provides useful context for readers newer to alternative asset structures.
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About the Guest
Jeremy Kranz is the founder of Sentinel Global, a venture capital firm focused on investing in the infrastructure and innovation catalyzing change in capital markets and enterprise technology. He was the original venture investor in the company that became ByteDance, the parent of TikTok, and has been an early investor in DJI, the consumer drone company. Kranz previously worked at GIC, the Singaporean sovereign wealth fund, and has spent approximately 25 years traveling extensively throughout Asia, spending 10 to 15 weeks per year in the region prior to the pandemic.
Kranz studied fusion power in college and has maintained a focus on deep technology, open source, tokenization, and quantum computing throughout his career. Sentinel Global operates Sentinel Labs, a research practice that produces personalized, timely intelligence for LPs and founders on topics including AI infrastructure, stable coins, supply chain innovation, and cybersecurity. Readers and fund managers interested in connecting with Jeremy Kranz or accessing Sentinel Labs research can reach the firm at info@sentinelglobal.xyz or visit sentinelglobal.xyz.
Questions Answered in This Article
What are the 10 black swan events impacting institutional investors right now?
Ten converging black swan events are simultaneously reshaping capital allocation for institutional investors, creating compounding risk that traditional portfolio models were not designed to absorb. These concurrent shocks span geopolitical realignment, AI disruption, quantum computing advancement, digital asset adoption, and emerging market volatility, among others. Fund managers who fail to account for the convergence of these events risk significant structural exposure across their holdings.
Hear the full breakdown on Making Billions with Ryan Miller — and fund managers ready to implement join the Fund Raise Capital community of fund managers and deal syndicators learning first-hand from Ryan Miller, The Wolf of Alt Street.
How did ByteDance’s first investor identify disruptive potential so early?
ByteDance’s first investor recognized disruptive potential by focusing on the founder’s ability to combine algorithmic content distribution with mobile-first consumer behavior at a scale that incumbents were not pursuing. The investment thesis centered on identifying a structural gap in how media was consumed rather than how it was produced. This disciplined focus on behavioral infrastructure over surface-level product features is what separated early conviction from the broader market’s skepticism.
Hear the full breakdown on Making Billions with Ryan Miller — and fund managers ready to implement join the Fund Raise Capital community of fund managers and deal syndicators learning first-hand from Ryan Miller, The Wolf of Alt Street.
Will ByteDance go IPO and what does that mean for early investors?
A ByteDance IPO remains one of the most closely watched liquidity events in global venture capital, given the company’s scale and the geopolitical complexity surrounding its ownership structure. Early investors face a unique position where regulatory friction in both the United States and China adds timing uncertainty to an otherwise substantial paper position. The outcome of any public offering would carry significant implications for how emerging market VC exits are structured in future fund cycles.
Hear the full breakdown on Making Billions with Ryan Miller — and fund managers ready to implement join the Fund Raise Capital community of fund managers and deal syndicators learning first-hand from Ryan Miller, The Wolf of Alt Street.
How is China’s AI adoption outpacing Silicon Valley in commercial applications?
China’s AI adoption is advancing faster in commercial applications because state-aligned capital deployment removes many of the regulatory and market-adoption friction points that slow Silicon Valley’s go-to-market timelines. Domestic technology companies in China are integrating AI into manufacturing, logistics, and consumer platforms at a velocity that Western counterparts are still piloting at smaller scale. This structural advantage in implementation speed is producing real-world data advantages that compound over time, widening the gap in applied AI performance.
Hear the full breakdown on Making Billions with Ryan Miller — and fund managers ready to implement join the Fund Raise Capital community of fund managers and deal syndicators learning first-hand from Ryan Miller, The Wolf of Alt Street.
What emerging market VC opportunities offer trillion-dollar return potential today?
Emerging market VC opportunities with the highest return potential are concentrated in sectors where large underserved populations intersect with rapidly scaling digital infrastructure, including fintech, health technology, and AI-driven logistics. The same pattern that produced outsized returns in China’s early mobile internet era is now visible across Southeast Asia, Africa, and Latin America. Fund managers with the due diligence frameworks to assess political risk alongside market size are positioned to access these opportunities before institutional capital fully arrives.
Hear the full breakdown on Making Billions with Ryan Miller — and fund managers ready to implement join the Fund Raise Capital community of fund managers and deal syndicators learning first-hand from Ryan Miller, The Wolf of Alt Street.
How should fund managers build resilient portfolios against converging black swan events?
Resilient portfolios are built by deliberately allocating across asset classes and geographies that are not correlated to the same shock vectors, rather than optimizing solely for return in a stable environment. Fund managers are advised to stress-test holdings against multiple simultaneous disruptions rather than single-event scenarios, which is the conventional but insufficient approach. Incorporating asymmetric positions in sectors like digital assets, AI infrastructure, and real assets provides a structural buffer when traditional equity and credit markets move in tandem during systemic stress.
Hear the full breakdown on Making Billions with Ryan Miller — and fund managers ready to implement join the Fund Raise Capital community of fund managers and deal syndicators learning first-hand from Ryan Miller, The Wolf of Alt Street.
What role do quantum computing and tokenization play in future VC portfolios?
Quantum computing and tokenization represent two distinct but compounding forces that are beginning to appear in forward-looking VC portfolio construction as foundational infrastructure plays rather than speculative bets. Quantum computing threatens to disrupt current encryption standards while simultaneously enabling computational breakthroughs in drug discovery, materials science, and financial modeling that create entirely new investable categories. Tokenization, in parallel, is restructuring how ownership and liquidity are represented across real assets, private equity, and credit, giving fund managers new instruments for portfolio construction and investor access.
Hear the full breakdown on Making Billions with Ryan Miller — and fund managers ready to implement join the Fund Raise Capital community of fund managers and deal syndicators learning first-hand from Ryan Miller, The Wolf of Alt Street.
How are digital assets and AI reshaping high-return institutional investment strategies?
Digital assets and AI are reshaping institutional investment strategies by introducing new alpha generation mechanisms that operate outside of traditional market cycles, forcing allocators to reconsider how return profiles are modeled. AI is being applied to deal sourcing, risk assessment, and portfolio monitoring in ways that compress the information asymmetry advantage that large institutions historically held over smaller managers. Digital assets, including tokenized real-world assets and decentralized finance infrastructure, are creating liquidity and yield structures that are attracting institutional capital previously restricted to conventional fixed income and equity mandates.
Hear the full breakdown on Making Billions with Ryan Miller — and fund managers ready to implement join the Fund Raise Capital community of fund managers and deal syndicators learning first-hand from Ryan Miller, The Wolf of Alt Street.
Topics Covered in This Article
- How emerging markets, particularly China, led AI commercialization in social media, e-commerce, and drones years ahead of Western competitors
- The Three Red Dragons: China’s government-coordinated visual AI program and its implications for emerging markets investors
- The 10 black swans converging simultaneously, including fusion, quantum computing, and tokenization, that fund managers should consider monitoring
- How tokenization of capital markets may restructure LP capital call mechanics, counterparty risk, and fund liquidity
- Sentinel’s four-part founder evaluation framework: CEO vision, CFO bean counting, engineering capacity, and product management sequencing
- Why emerging markets outside the US are positioned to produce a new wave of capital-efficient, billion-dollar point-solution businesses
- Open source computing as an underappreciated emerging markets investment theme, amplified by AI-generated code contributions
- The three C’s decision-making framework, confidence, competence, and courage, as a psychological self-policing tool for investment committees
- How solution architecting builds deeper LP relationships in emerging markets and alternative asset fund management
- The research on complementary versus cohesive teams and its implications for building high-performing venture and fund management organizations
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