Bitcoin investors are holding approximately $1.2 trillion in unrealized profits, according to recent data from Glassnode. This figure reflects the difference between Bitcoin’s market capitalization of $2.1 trillion and its realized capitalization of $955 billion, with the average investor seeing a paper gain of around 125%. Despite Bitcoin trading near its all-time high of $111,970, investors are showing restraint, with daily realized profits averaging only $872 million, significantly lower than the $2.8–$3.2 billion seen during previous peaks at $73,000 and $107,000.

This HODLing behavior, particularly among long-term holders and institutional investors like ETFs and companies such as MicroStrategy, suggests strong confidence in Bitcoin’s future growth. However, this large pool of unrealized gains could lead to selling pressure if market sentiment shifts. The low daily realized profits ($872M vs. $2.8–$3.2B at prior peaks) indicate strong conviction among long-term holders (LTHs) and institutional investors, like those in Bitcoin ETFs or firms like MicroStrategy. This HODLing suggests expectations of further price appreciation, potentially stabilizing the market in the short term.

The massive unrealized gains represent a latent risk. If market sentiment shifts—due to macroeconomic factors (e.g., interest rate hikes, regulatory crackdowns) or a sharp price correction—investors may rush to lock in profits, triggering volatility or a cascade of sell-offs. Unrealized gains are not evenly distributed, amplifying wealth inequality among Bitcoin holders. Early adopters and large institutional players hold disproportionate shares, which could influence market dynamics if they decide to liquidate.

Significant unrealized gains attract attention from regulators and tax authorities, especially in jurisdictions eyeing crypto as a revenue source. Potential capital gains tax changes could prompt preemptive selling. LTHs, including early adopters and institutions, are sitting on substantial gains and appear reluctant to sell, reflecting a belief in Bitcoin’s long-term value. In contrast, short-term traders or newer retail investors may be more likely to realize profits during price surges, contributing to the modest daily profit-taking.

Institutional players (e.g., ETFs, MicroStrategy) control significant Bitcoin holdings, often with lower cost bases, giving them outsized unrealized gains. Retail investors, especially late entrants, may have higher average purchase prices, limiting their gains and exposing them to greater risk in a downturn. Access to Bitcoin investment varies globally. Investors in wealthier nations with robust crypto infrastructure (e.g., U.S., EU) likely hold a larger share of gains compared to those in regions with limited access or regulatory hostility, exacerbating global wealth disparities.

Some HODLers view Bitcoin as a hedge against fiat inflation or a store of value, prioritizing long-term holding over short-term gains. Others see it as a speculative asset, more likely to sell during rallies, creating tension between ideological and profit-driven motives. When Bitcoin investors take profits, it means they sell their holdings to realize the gains from the difference between their purchase price and the current market price. This can have several effects on the market and broader ecosystem, especially with $1.2 trillion in unrealized gains at play.

Selling to lock in profits increases the supply of Bitcoin on the market. If significant profit-taking occurs, especially in a short period, it can lead to a price drop as demand struggles to absorb the increased supply. Large-scale profit-taking, particularly by whales (large holders) or institutions, can trigger sharp price swings. For example, if a portion of the $1.2T in unrealized gains is realized rapidly, it could spark a cascade of sell-offs, amplifying volatility.

Historical data shows profit-taking often precedes corrections. For instance, Glassnode notes that daily realized profits were $2.8–$3.2B during Bitcoin’s peaks at $73,000 and $107,000, compared to $872M recently, suggesting restrained selling. A surge in profit-taking could push prices closer to those correction levels again. Profit-taking by large players can signal to retail investors that a peak has been reached, potentially triggering fear-driven sales or panic. Conversely, if prices remain stable post-profit-taking, it could reinforce confidence and fuel FOMO (fear of missing out), attracting new buyers.

Long-term holders (LTHs) HODLing through price surges may start selling if they perceive diminishing returns, while short-term traders may exit quickly, deepening the divide between these groups. Profit-taking injects liquidity into the market, as sellers convert Bitcoin into fiat or other assets. This can benefit exchanges and trading platforms but may also strain order books if selling is concentrated. Institutions like ETFs or companies (e.g., MicroStrategy) with large unrealized gains may strategically time profit-taking, influencing market trends. Their actions are often watched closely, amplifying their impact.

Realizing profits triggers taxable events in many jurisdictions. With $1.2T in unrealized gains, widespread profit-taking could lead to significant tax liabilities, potentially prompting some investors to delay selling until tax policies are clearer. Large profit-taking events can draw scrutiny from regulators, especially if they suspect market manipulation or if governments seek to capture revenue from crypto gains. This could lead to tighter regulations, affecting market access.

Profit-taking redistributes wealth from Bitcoin’s ecosystem to other assets or fiat. Early adopters and institutions with low cost bases (e.g., those who bought at $1,000–$10,000) stand to gain the most, widening the wealth gap with newer retail investors. The divide between LTHs and short-term traders grows, as LTHs may hold through volatility, while newer investors sell, potentially locking in smaller gains or losses. Similarly, institutional profit-taking could overshadow retail investors’ impact.

Profits realized from Bitcoin often flow into other markets (stocks, real estate, altcoins), potentially inflating those asset classes or stabilizing them if Bitcoin’s price drops. In a high-profit-taking scenario, a Bitcoin price crash could ripple through crypto markets, impacting leveraged positions and causing liquidations, as seen in past cycles.

Profit-taking with $1.2T in unrealized gains could lead to price volatility, regulatory scrutiny, and wealth redistribution, deepening the divide between long-term holders, short-term traders, institutions, and retail investors. While restrained selling and strong demand may mitigate immediate impacts, a coordinated wave of profit-taking could trigger significant market corrections. Monitoring on-chain data, like Glassnode’s realized profit metrics, and institutional behavior will be key to anticipating these shifts.

In another bold move underscoring its ambitions to dominate the artificial intelligence (AI) frontier, Meta has hired Apple’s head of AI models, Ruoming Pang, marking a high-profile defection that underlines the escalating talent war among Silicon Valley’s tech giants.

Pang’s departure, first reported by Bloomberg, comes at a critical time for Apple as it struggles to keep pace with rivals in developing cutting-edge generative AI systems.

Pang led Apple’s in-house foundation models team, a group of over 100 engineers responsible for training the neural networks powering Apple Intelligence—the company’s newly launched suite of AI features. These include on-device functions such as Genmoji, Priority Notifications, and Mail summarization, all slated to roll out with iOS 18 later this year.

However, sources familiar with Apple’s AI efforts say the company’s models have failed to keep up with rivals like OpenAI, Anthropic, and even Meta itself. Apple has reportedly considered licensing external models to bridge the gap, including options from OpenAI and Anthropic, particularly for its upcoming Siri overhaul.

Pang’s departure may be just the beginning. People with knowledge of Apple’s AI division say other senior engineers could soon follow him out the door, casting further doubt over the company’s internal capacity to lead in the rapidly evolving AI space.

Meta’s Superintelligence Labs: A Magnet for Talent

Pang will now join Meta’s Superintelligence Labs, the elite AI division launched by CEO Mark Zuckerberg to accelerate the company’s long-term AI roadmap. Superintelligence Labs has become a destination for elite researchers and engineers, recruiting talent from OpenAI, Google DeepMind, and now Apple.

Meta’s Superintelligence Labs is helmed by Alexandr Wang, former CEO of Scale AI, a data labeling startup Meta recently acquired through a $14.3 billion investment. Wang now serves as Meta’s Chief AI Officer. Alongside him is Nat Friedman, the former CEO of GitHub, who will oversee Meta’s AI product strategy and applied research. The team also includes Daniel Gross, Friedman’s longtime business partner and the co-founder of Safe Superintelligence, a high-profile startup that rebuffed Meta’s acquisition offer earlier this year.

The team’s goal is to develop next-generation AI systems capable of multimodal reasoning, natural conversation, and even autonomous decision-making across Meta’s ecosystem.

Meta has reportedly offered compensation packages ranging from $10 million to $100 million for key hires, underlining just how serious Zuckerberg is about building what he’s called a “frontier AI platform”—an end-to-end system that will not only power Meta’s social platforms but also lead in enterprise and developer tools.

For Pang, the move represents both a leap into a more advanced AI environment and a chance to help shape the architecture of Meta’s AI offerings from the ground up.

Pang’s exit also reflects broader cracks in Apple’s AI strategy. While the company has prioritized privacy and on-device intelligence, that approach has limited its ability to deliver the kind of rich, real-time generative AI experiences users now expect. Meanwhile, competitors are racing ahead with large-scale cloud-based models, pushing updates at a blistering pace.

Meta’s poaching of Pang is just the latest in a long line of aggressive hires that illustrate the company’s intent to dominate AI. Earlier this year, it recruited top scientists from Google’s DeepMind, secured researchers from Safe Superintelligence (SSI), and added ex-OpenAI contributors to its AI safety and alignment units.

As Meta assembles what some in the industry have described as the “Avengers of AI,” Apple finds itself facing mounting pressure—not just to ship features, but to retain the talent capable of building them.

While Apple Intelligence will debut later this year in a suite of consumer-facing features, the company’s reliance on third-party AI providers for core capabilities suggests it is still playing catch-up. In contrast, Meta’s Superintelligence Labs is becoming a self-contained AI powerhouse, poised to lead innovation and influence the next wave of AI products across platforms and industries.

Pang’s jump from Apple to Meta may appear as a single hire—but it represents a seismic shift in the battle for AI supremacy. And with Meta aggressively stacking its AI roster, more shakeups are likely to follow.

The landscape of global innovation is shifting at an unprecedented pace, and recent trends at Tekedia Capital highlight a critical divergence. Our latest cohort of 18 companies, invested in just three months ago, is demonstrating the fastest revenue growth we’ve ever observed, surpassing even the strong performance of our October 2024 class. This earlier class included a remarkable company that achieved a $10 million Annual Recurring Revenue (ARR) within four months of its inception and is now on track for $100 million within its first year, having recently secured $36 million in funding.

This data leads to a stark conclusion: the innovation gap between developing and developed nations is not merely present, but rapidly widening to an asymmetrical degree. The defining characteristic of leading companies today is “AI-nativity.” These are not just firms using artificial intelligence, but rather businesses fundamentally built upon AI, regardless of their industry. For instance, the most successful insurance companies of tomorrow will likely be AI companies that happen to offer insurance products, not traditional insurance firms merely integrating AI. Similarly, a premier online tutor like ChatGPT isn’t an edtech company that uses AI; it’s an AI company providing educational services.

When we contrast these emerging, AI-native enterprises with many African startups, a troubling pattern emerges. The gap in the pace of innovation and the value delivered to users is expanding. There’s a tangible risk that many African Software-as-a-Service (SaaS) companies could face overnight disintermediation.

This observation is not an alarmist prediction, but a conclusion drawn from reviewing the financials of dozens of companies across five continents in Tekedia Capital. In this rapidly accelerating era of AI, many African startups are struggling to keep pace, and a significant portion of our SaaS companies could potentially disappear by 2027. Urgent action is required. We must fundamentally rethink our approach to building businesses to ensure Africa can compete and win in this new global landscape. I have called the landscape the accelerated society era and we must find our level there productively.

The Accelerated Society: A New Era of Disruption and Integration