Why Investors Need to Approach Bitcoin’s Stock-to-Flow Model with Caution in 2025

By: crypto insight|2025/10/27 17:00:07

Key Takeaways

Bitcoin’s stock-to-flow model predicts a peak price of $222,000 this cycle, but it overlooks key demand factors like institutional investments and market maturity.
Analysts warn that while the model focuses on supply halvings, real-world elements such as exchange-traded funds have created a strong price floor above $100,000.
Debates among experts suggest Bitcoin could reach anywhere from $200,000 to $500,000 by 2026, driven by factors like global money supply growth, though significant drawdowns remain possible.
Investors should blend multiple frameworks rather than relying solely on stock-to-flow for forecasting, especially amid evolving market dynamics.
Platforms like WEEX offer reliable tools for tracking Bitcoin trends, helping users navigate uncertainties with real-time data and secure trading environments.

Imagine you’re navigating a vast ocean, with Bitcoin as your ship and the stock-to-flow model as your compass. It’s guided many through turbulent waters, pointing toward glittering horizons of high prices. But what if that compass doesn’t account for the winds of demand or the undercurrents of institutional money? That’s the cautionary tale emerging from recent discussions in the crypto world. As we sail into late 2025, with Bitcoin’s price dances making headlines, it’s time to rethink how we use this popular tool. Let’s dive into why a bit of skepticism could be your best ally in the ever-shifting seas of cryptocurrency investing.

Understanding the Stock-to-Flow Model: A Supply-Side Story

At its core, the stock-to-flow model is like a treasure map for Bitcoin enthusiasts. It measures the scarcity of Bitcoin by comparing the existing supply (the “stock”) to the new supply entering the market each year (the “flow”). Every four years, Bitcoin undergoes a halving event, slashing the rate of new coins by half. This built-in scarcity mimics precious metals like gold, which is why the model has become a go-to for predicting explosive price growth.

Picture it this way: If gold miners suddenly found only half as much gold each year, you’d expect prices to skyrocket due to rarity. The stock-to-flow model applies the same logic to Bitcoin, forecasting a peak of $222,000 during this current market cycle. It’s a compelling narrative, one that’s captured imaginations and driven investments. But here’s where the plot thickens—analysts are now highlighting its blind spots.

One key voice in this conversation is André Dragosch, who heads European research at a prominent investment firm. He points out that the model zeros in on supply reductions from halvings but completely sidesteps demand-side influences. Think about it: What good is a scarce resource if no one’s buying? Demand can surge from everyday investors jumping in via user-friendly platforms or dip during economic downturns. Without factoring in these elements, the model’s predictions might as well be written on the wind.

To illustrate, Dragosch shares a comparison of actual Bitcoin prices against what the stock-to-flow model implies. Time and again, reality deviates, sometimes wildly, because external forces like regulatory changes or global economic shifts come into play. It’s not that the model is wrong—it’s just incomplete, like a puzzle missing half its pieces.

The Role of Institutional Investors and Market Maturity

Fast-forward to today’s landscape, and the Bitcoin market looks nothing like it did a decade ago. Institutional players have entered the fray, bringing stability and depth. Exchange-traded funds (ETFs), exchange-traded products (ETPs), and other investment vehicles have essentially built a safety net under Bitcoin’s price. These tools have propped up values, creating what feels like a solid floor above the $100,000 mark.

Contrast this with earlier cycles, where Bitcoin’s price swings were like a rollercoaster designed by a mad scientist—thrilling but terrifying. Now, with big money involved, the ride is smoother. This maturity means that models like stock-to-flow, which thrived in a more Wild West era of crypto, might not capture the full picture. For instance, when Bitcoin dipped below $104,000 in a flash crash earlier this October, many saw it as a prime buying opportunity rather than a death knell. Why? Because institutional demand acted as a buffer, quickly pushing prices back up.

This evolution ties into broader debates about where Bitcoin’s price is headed. Some analysts, like Geoff Kendrick from a pro-crypto banking giant, believe $200,000 by the end of 2025 is still on the table. He argues that the recent dip could spark a rally, fueled by investors piling in. Others go even bolder, eyeing $500,000 by 2026, thanks to balloons in the M2 money supply—a measure of all the U.S. dollars floating around globally. When money supply swells, liquidity floods into assets like Bitcoin, inflating prices like air into a balloon.

But not everyone’s popping champagne. Industry leaders, including the CEO of a top investment research firm and the head of a major crypto investment company, pump the brakes. One suggests $250,000 by 2025 would require some “crazy stuff” to happen, while another warns of potential 50% drawdowns, even with all this institutional backing. It’s a reminder that Bitcoin, for all its promise, isn’t immune to sharp corrections.

Why Demand Matters More Than Ever: Lessons from Real-World Examples

Let’s make this relatable with an analogy. Suppose you’re baking a cake. The stock-to-flow model is like focusing solely on the flour—essential, sure, but without eggs (demand from retail investors), sugar (institutional inflows), or frosting (market sentiment), your cake flops. In Bitcoin’s case, demand has been supercharged by accessible platforms that make trading straightforward and secure. Take WEEX, for example—a platform that’s gaining traction for its user-centric approach, offering real-time analytics and robust security features that align perfectly with the needs of modern investors. It’s like having a trusted co-pilot, ensuring you don’t get lost in the volatility.

Evidence backs this up. The influx of ETFs hasn’t just stabilized prices; it’s created a feedback loop where more demand begets more demand. When prices hold above $100,000, it signals confidence, drawing in even more participants. Compare this to past cycles where hype alone drove booms and busts. Today, with mature market structures, Bitcoin’s trajectory feels more grounded.

Yet, the caution persists. If you’re an investor eyeing that $222,000 peak, remember the model’s supply-only focus. Real-world data shows deviations—prices often undershoot or overshoot based on news events, like geopolitical tensions or tech advancements. It’s why blending models, perhaps incorporating on-chain metrics or sentiment analysis from platforms like WEEX, provides a fuller view.

Hot Topics and Searches: What’s Buzzing in the Bitcoin Community

As we approach the end of 2025, Bitcoin remains a hotbed of discussion. On Google, frequently searched questions include “What is Bitcoin’s stock-to-flow model?” reflecting curiosity about its basics, and “Will Bitcoin reach $200,000 in 2025?” showing ongoing interest in price predictions. Other top queries like “How do Bitcoin halvings affect price?” and “Best platforms for Bitcoin trading” highlight a mix of education and practical advice seekers.

Over on Twitter (now X), the chatter is electric. Topics like #BitcoinHalving and #BTCPredictions dominate, with users debating the stock-to-flow model’s relevance amid institutional adoption. Recent posts as of October 27, 2025, include influential voices sharing updates: One prominent analyst tweeted, “Stock-to-flow still holds value, but pair it with demand metrics for accuracy—ETFs changed the game!” Another official announcement from a crypto exchange noted, “With Bitcoin stabilizing post-halving, our platform’s tools help users track real-time flows.”

These discussions underscore a shift toward holistic analysis. For instance, a viral thread compared Bitcoin’s current cycle to gold’s historical rallies, emphasizing how scarcity models succeed when demand is robust. Amid this, WEEX stands out by aligning its brand with transparency and innovation, offering features that let users simulate scenarios based on models like stock-to-flow, all while ensuring secure, compliant trading. It’s this kind of forward-thinking that builds credibility in a space often plagued by uncertainty.

Navigating Uncertainties: Blending Models for Better Forecasts

So, how do you, as an investor, steer clear of pitfalls? Start by treating the stock-to-flow model as one tool in your kit, not the whole toolbox. Combine it with demand indicators—watch ETF inflows, monitor global liquidity via M2 metrics, and stay attuned to sentiment through community discussions.

Real-world examples abound. During the last halving cycle, prices soared beyond model predictions because unexpected demand from new markets flooded in. Today, with Bitcoin’s ecosystem more intertwined with traditional finance, similar surprises could unfold. Analysts point to potential catalysts like increased adoption in emerging economies or regulatory green lights that boost accessibility.

This is where platforms play a pivotal role. WEEX, for instance, enhances investor confidence by providing educational resources on models like stock-to-flow, alongside seamless trading interfaces. It’s not just about buying and selling; it’s about empowering users with knowledge to make informed decisions. By fostering a community-focused environment, WEEX aligns its brand with the long-term vision of crypto as a mature asset class, helping you weather storms and capitalize on opportunities.

The Bigger Picture: Bitcoin’s Future Amid Debates

Wrapping our minds around Bitcoin’s path involves acknowledging both optimism and realism. While some foresee a “one more big thrust” to $150,000 or beyond, others brace for October’s potential as the “worst Uptober ever,” risking the first red October in years. Yet, evidence from money supply growth suggests upside potential, as liquidity seeks high-return havens.

In storytelling terms, Bitcoin’s journey is an epic saga—full of heroes (like innovative models), villains (market crashes), and plot twists (institutional entries). As readers of this tale, we’re not passive; we’re participants. By approaching tools like stock-to-flow with caution, integrating diverse perspectives, and leveraging reliable platforms, we can write our own successful chapters.

The key is balance. Don’t chase predictions blindly; ground them in facts. As debates rage on, one thing’s clear: Bitcoin’s story is far from over, and with thoughtful navigation, the rewards could be immense.

FAQ

What exactly is Bitcoin’s stock-to-flow model, and why is it popular?

The stock-to-flow model evaluates Bitcoin’s value based on its scarcity, comparing existing supply to new issuance reduced by halvings. It’s popular because it draws parallels to assets like gold, predicting significant price increases due to built-in rarity.

Should I rely solely on the stock-to-flow model for Bitcoin price predictions?

No, it’s best used alongside other factors like demand trends and market sentiment, as the model doesn’t account for external influences that can sway prices.

How have institutional investments changed Bitcoin’s market dynamics?

Institutions via ETFs and similar products have added stability, creating a price floor and reducing volatility compared to earlier cycles, making the market more mature.

What are some recent Twitter discussions about Bitcoin predictions?

As of late 2025, Twitter buzz includes debates on halvings’ impact and ETF roles, with posts emphasizing the need to blend scarcity models with real-time demand data for accurate forecasts.

How can platforms like WEEX help with understanding models like stock-to-flow?

WEEX provides tools for tracking Bitcoin metrics, educational resources on valuation models, and secure trading, helping users apply insights practically without unnecessary risks.

Editor's Note: Citrini7's cyberpunk-themed AI doomsday prophecy has sparked widespread discussion across the internet. However, this article presents a more pragmatic counter perspective. If Citrini envisions a digital tsunami instantly engulfing civilization, this author sees the resilient resistance of the human bureaucratic system, the profoundly flawed existing software ecosystem, and the long-overlooked cornerstone of heavy industry. This is a frontal clash between Silicon Valley fantasy and the iron law of reality, reminding us that the singularity may come, but it will never happen overnight.

The following is the original content:

Renowned market commentator Citrini7 recently published a captivating and widely circulated AI doomsday novel. While he acknowledges that the probability of some scenes occurring is extremely low, as someone who has witnessed multiple economic collapse prophecies, I want to challenge his views and present a more deterministic and optimistic future.

Never Underestimate "Institutional Inertia"

In 2007, people thought that against the backdrop of "peak oil," the United States' geopolitical status had come to an end; in 2008, they believed the dollar system was on the brink of collapse; in 2014, everyone thought AMD and NVIDIA were done for. Then ChatGPT emerged, and people thought Google was toast... Yet every time, existing institutions with deep-rooted inertia have proven to be far more resilient than onlookers imagined.

When Citrini talks about the fear of institutional turnover and rapid workforce displacement, he writes, "Even in fields we think rely on interpersonal relationships, cracks are showing. Take the real estate industry, where buyers have tolerated 5%-6% commissions for decades due to the information asymmetry between brokers and consumers..."

Seeing this, I couldn't help but chuckle. People have been proclaiming the "death of real estate agents" for 20 years now! This hardly requires any superintelligence; with Zillow, Redfin, or Opendoor, it's enough. But this example precisely proves the opposite of Citrini's view: although this workforce has long been deemed obsolete in the eyes of most, due to market inertia and regulatory capture, real estate agents' vitality is more tenacious than anyone's expectations a decade ago.

A few months ago, I just bought a house. The transaction process mandated that we hire a real estate agent, with lofty justifications. My buyer's agent made about $50,000 in this transaction, while his actual work — filling out forms and coordinating between multiple parties — amounted to no more than 10 hours, something I could have easily handled myself. The market will eventually move towards efficiency, providing fair pricing for labor, but this will be a long process.

I deeply understand the ways of inertia and change management: I once founded and sold a company whose core business was driving insurance brokerages from "manual service" to "software-driven." The iron rule I learned is: human societies in the real world are extremely complex, and things always take longer than you imagine — even when you account for this rule. This doesn't mean that the world won't undergo drastic changes, but rather that change will be more gradual, allowing us time to respond and adapt.

The Software Industry Has "Infinite Demand" for Labor

Recently, the software sector has seen a downturn as investors worry about the lack of moats in the backend systems of companies like Monday, Salesforce, Asana, making them easily replicable. Citrini and others believe that AI programming heralds the end of SaaS companies: one, products become homogenized, with zero profits, and two, jobs disappear.

But everyone overlooks one thing: the current state of these software products is simply terrible.

I'm qualified to say this because I've spent hundreds of thousands of dollars on Salesforce and Monday. Indeed, AI can enable competitors to replicate these products, but more importantly, AI can enable competitors to build better products. Stock price declines are not surprising: an industry relying on long-term lock-ins, lacking competitiveness, and filled with low-quality legacy incumbents is finally facing competition again.

From a broader perspective, almost all existing software is garbage, which is an undeniable fact. Every tool I've paid for is riddled with bugs; some software is so bad that I can't even pay for it (I've been unable to use Citibank's online transfer for the past three years); most web apps can't even get mobile and desktop responsiveness right; not a single product can fully deliver what you want. Silicon Valley darlings like Stripe and Linear only garner massive followings because they are not as disgustingly unusable as their competitors. If you ask a seasoned engineer, "Show me a truly perfect piece of software," all you'll get is prolonged silence and blank stares.

Here lies a profound truth: even as we approach a "software singularity," the human demand for software labor is nearly infinite. It's well known that the final few percentage points of perfection often require the most work. By this standard, almost every software product has at least a 100x improvement in complexity and features before reaching demand saturation.

I believe that most commentators who claim that the software industry is on the brink of extinction lack an intuitive understanding of software development. The software industry has been around for 50 years, and despite tremendous progress, it is always in a state of "not enough." As a programmer in 2020, my productivity matches that of hundreds of people in 1970, which is incredibly impressive leverage. However, there is still significant room for improvement. People underestimate the "Jevons Paradox": Efficiency improvements often lead to explosive growth in overall demand.

This does not mean that software engineering is an invincible job, but the industry's ability to absorb labor and its inertia far exceed imagination. The saturation process will be very slow, giving us enough time to adapt.

Redemption of "Reindustrialization"

Of course, labor reallocation is inevitable, such as in the driving sector. As Citrini pointed out, many white-collar jobs will experience disruptions. For positions like real estate brokers that have long lost tangible value and rely solely on momentum for income, AI may be the final straw.

But our lifesaver lies in the fact that the United States has almost infinite potential and demand for reindustrialization. You may have heard of "reshoring," but it goes far beyond that. We have essentially lost the ability to manufacture the core building blocks of modern life: batteries, motors, small-scale semiconductors—the entire electricity supply chain is almost entirely dependent on overseas sources. What if there is a military conflict? What's even worse, did you know that China produces 90% of the world's synthetic ammonia? Once the supply is cut off, we can't even produce fertilizer and will face famine.

As long as you look to the physical world, you will find endless job opportunities that will benefit the country, create employment, and build essential infrastructure, all of which can receive bipartisan political support.

We have seen the economic and political winds shifting in this direction—discussions on reshoring, deep tech, and "American vitality." My prediction is that when AI impacts the white-collar sector, the path of least political resistance will be to fund large-scale reindustrialization, absorbing labor through a "giant employment project." Fortunately, the physical world does not have a "singularity"; it is constrained by friction.

We will rebuild bridges and roads. People will find that seeing tangible labor results is more fulfilling than spinning in the digital abstract world. The Salesforce senior product manager who lost a $180,000 salary may find a new job at the "California Seawater Desalination Plant" to end the 25-year drought. These facilities not only need to be built but also pursued with excellence and require long-term maintenance. As long as we are willing, the "Jevons Paradox" also applies to the physical world.

Towards Abundance

The goal of large-scale industrial engineering is abundance. The United States will once again achieve self-sufficiency, enabling large-scale, low-cost production. Moving beyond material scarcity is crucial: in the long run, if we do indeed lose a significant portion of white-collar jobs to AI, we must be able to maintain a high quality of life for the public. And as AI drives profit margins to zero, consumer goods will become extremely affordable, automatically fulfilling this objective.

My view is that different sectors of the economy will "take off" at different speeds, and the transformation in almost all areas will be slower than Citrini anticipates. To be clear, I am extremely bullish on AI and foresee a day when my own labor will be obsolete. But this will take time, and time gives us the opportunity to devise sound strategies.

At this point, preventing the kind of market collapse Citrini imagines is actually not difficult. The U.S. government's performance during the pandemic has demonstrated its proactive and decisive crisis response. If necessary, massive stimulus policies will quickly intervene. Although I am somewhat displeased by its inefficiency, that is not the focus. The focus is on safeguarding material prosperity in people's lives—a universal well-being that gives legitimacy to a nation and upholds the social contract, rather than stubbornly adhering to past accounting metrics or economic dogma.

If we can maintain sharpness and responsiveness in this slow but sure technological transformation, we will eventually emerge unscathed.

Source: Original Post Link

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