This Bitcoin Cycle Is Different: The ETF and Trump Factor

The 2024-2025 Bitcoin cycle represents a fundamental departure from previous market cycles, driven by two unprecedented catalysts: the January 2024 approval of spot Bitcoin ETFs and Donald Trump's return to the presidency with an explicitly pro-crypto agenda. These factors have transformed Bitcoin from a predominantly retail-driven, speculative asset into an institutionally-accessible investment vehicle backed by regulatory momentum at the highest levels of government.

Historical Context: Previous Bitcoin Cycles

To understand why this cycle differs, we must first examine the patterns of previous cycles:

2011-2013 Cycle: Bitcoin rose from $1 to $1,100, driven primarily by early adopters and libertarian idealists. The collapse of Mt. Gox in 2014 exemplified the infrastructure immaturity.

2015-2017 Cycle: Bitcoin surged from $200 to nearly $20,000, fueled by ICO mania and retail FOMO. Institutional participation remained minimal, with most traditional finance viewing Bitcoin skeptically.

2018-2021 Cycle: Bitcoin recovered to reach $69,000 in November 2021. This cycle saw the first major institutional adopters (MicroStrategy, Tesla) and the emergence of institutional custody solutions. However, investment remained direct purchases, requiring companies to hold Bitcoin on their balance sheets—a regulatory and accounting burden that limited widespread adoption.

Each previous cycle followed a similar pattern: halving event, retail enthusiasm, parabolic rise, 80%+ drawdown, and institutional skepticism. Regulatory hostility or ambiguity characterized the environment, with governments viewing Bitcoin as a threat rather than an opportunity.

The ETF Revolution: Democratizing Institutional Access

The Approval That Changed Everything

On January 10, 2024, the SEC approved 11 spot Bitcoin ETFs simultaneously, including offerings from BlackRock (iShares Bitcoin Trust - IBIT), Fidelity (Wise Origin Bitcoin Fund - FBTC), and Grayscale (converted from GBTC). This approval marked the end of a decade-long regulatory battle and represented the SEC's implicit endorsement of Bitcoin as a legitimate asset class.

Record-Breaking Adoption Metrics

The impact was immediate and unprecedented:

• Fastest ETF Launch in History: BlackRock's IBIT gathered $10 billion in assets within seven weeks, shattering records previously held by gold ETFs that took years to reach similar milestones.

• Total AUM Growth: By mid-2024, Bitcoin ETFs collectively held over $60 billion in assets, with net inflows consistently ranging from $200-500 million daily during strong market periods.

• Institutional Allocation: Financial advisors, pension funds, and wealth management platforms that were previously barred from Bitcoin exposure can now allocate with a simple ticker symbol. A typical portfolio manager can now add BTC exposure alongside SPY and AGG without custody concerns, security risks, or regulatory complications.

Why ETFs Change the Game

The structural advantages of ETFs over direct Bitcoin ownership include:

Regulatory Clarity: ETFs operate within established SEC frameworks, providing legal certainty for institutional investors bound by fiduciary duties.

Operational Simplicity: No need for cold storage, private key management, or specialized custody solutions. Bitcoin exposure trades like any equity.

Tax Efficiency: ETFs offer potential tax advantages over direct ownership, particularly for retirement accounts (401k, IRA) where Bitcoin ETFs can now be held tax-deferred.

Liquidity and Price Discovery: With billions in daily trading volume, ETFs provide deep liquidity and tighter spreads than many cryptocurrency exchanges.

Mainstream Distribution: ETFs are available through every major brokerage—Schwab, Fidelity, Vanguard—bringing Bitcoin to hundreds of millions of investors who would never open a Coinbase account.

The Numbers Don't Lie

Previous cycles saw institutional adoption measured in dozens of companies. This cycle has seen:

• Over 1,000 institutional investors filing 13F forms disclosing Bitcoin ETF holdings
• State pension funds (Wisconsin Investment Board) allocating to Bitcoin ETFs
• Major hedge funds (Millennium Management, Citadel) taking positions totaling billions
• Traditional asset managers recommending 1-5% Bitcoin allocations in balanced portfolios

This represents a 100x increase in institutional accessibility compared to the previous cycle.

The Trump Factor: Political Tailwinds Replace Headwinds

From Hostile to Champion

Previous Bitcoin cycles occurred under administrations ranging from indifferent to hostile toward cryptocurrency:

• The Obama administration's focus on AML/KYC led to aggressive enforcement actions
• The Trump administration (2017-2021) saw Treasury Secretary Mnuchin warn against Bitcoin's use in illicit activities
• The Biden administration's approach through 2024 was characterized by enforcement-heavy regulation via agencies like the SEC under Gary Gensler

Trump's 2024 Pro-Bitcoin Transformation

Donald Trump's 2024 campaign marked an extraordinary reversal. At the Bitcoin 2024 conference in Nashville, Trump pledged to:

• Make the United States "the crypto capital of the planet"
• Fire SEC Chair Gary Gensler (viewed as crypto-hostile)
• End the regulatory "war on crypto"
• Create a strategic Bitcoin reserve for the U.S. government
• Prevent the creation of a Central Bank Digital Currency (CBDC)

Policy Implications

With Trump's January 2025 inauguration, the regulatory environment has shifted dramatically:

Personnel Changes: The appointment of crypto-friendly regulators signals a departure from enforcement-first approaches. Reports suggest potential SEC leadership that understands blockchain technology.

Legislative Momentum: Republican control of Congress increases the likelihood of comprehensive crypto legislation, including stablecoin frameworks and clear custody rules.

Strategic Reserve Concept: While controversial, Trump's proposal to establish a U.S. Bitcoin reserve would represent unprecedented sovereign legitimization. Even if not fully implemented, the discussion alone validates Bitcoin's role as a monetary asset.

Global Competition Narrative: Trump's framing of crypto leadership as a national competitiveness issue changes the conversation from "should we allow this?" to "how do we win this?"

Historical Parallel: Gold in 1971

The closest historical parallel may be President Nixon's 1971 decision to end dollar-gold convertibility. While that severed the link to gold, it recognized gold's role in the monetary system. Trump's embrace of Bitcoin could represent a similar inflection point—not returning to a gold standard, but acknowledging Bitcoin's emergence as "digital gold" in a multipolar monetary system.

Quantitative Differences: This Cycle vs. Previous Cycles

Supply Dynamics

Previous Cycles: Bitcoin traded primarily on crypto-native exchanges with limited fiat on-ramps. Supply and demand were driven by retail speculation.

This Cycle:

• ETF inflows absorb 5,000-10,000 BTC weekly during strong periods
• Post-April 2024 halving, only 450 BTC are mined daily (approximately 3,150 weekly)
• Simple math: demand exceeds new supply by 2-3x, creating structural scarcity

Volatility Profile

Historical Bitcoin volatility exceeded 80% annualized during previous cycles. Early data from 2024 suggests:

• 30-day realized volatility averaging 40-50% with ETF flows
• Deeper liquidity cushioning extreme moves
• Institutional participation reducing panic selling

While still volatile by traditional asset standards, the trend toward lower volatility makes Bitcoin more palatable for risk-managed institutional portfolios.

Market Structure

Previous Cycles:

• 90%+ retail participation
• Thin order books
• Weekend/after-hours gaps common
• Manipulation concerns (whale wallets, exchange manipulation)

This Cycle:

• 40-50% institutional participation estimated
• 24/7 crypto markets now integrated with regulated ETF markets
• Improved custody and reporting standards
• Reduced manipulation risk through regulated products

Counter-Arguments and Risks

A balanced thesis must acknowledge significant counter-arguments:

"This Time Is Different" Is Dangerous

Financial history is littered with "this time is different" proclamations preceding crashes. The 2000 dot-com bubble and 2008 housing crisis both featured narratives of fundamental change that proved illusory. Bitcoin's fundamental value proposition—decentralized, censorship-resistant money—hasn't changed. Perhaps the cycle dynamics remain intact, and we're simply experiencing a larger version of previous patterns.

Regulatory Risk Remains

Despite Trump's rhetoric, actual policy implementation is uncertain:

• Congress may not pass meaningful crypto legislation
• Courts could overturn favorable regulatory changes
• A future administration could reverse course
• Global regulatory coordination (EU, China) may constrain U.S. policy

Market Maturation May Mean Lower Returns

ETFs and institutional adoption may signal Bitcoin's maturation from a high-risk/high-reward speculation to a moderate-risk/moderate-reward asset. Previous cycles saw 10x-100x gains. This cycle may see more modest 2x-5x appreciation as Bitcoin's market cap approaches $2-3 trillion, making parabolic gains mathematically harder.

Centralization Concerns

ETFs introduce custodial centralization—the antithesis of Bitcoin's core ethos. BlackRock and Fidelity now control hundreds of thousands of BTC. This concentration creates:

• Regulatory capture risk
• Potential government seizure points
• Dilution of Bitcoin's censorship-resistance properties

Trump Administration Uncertainty

Political promises often diverge from political reality. Trump's attention may shift to other priorities. His administration may face constraints from:

• Federal Reserve independence on monetary policy
• Congressional gridlock
• International pressure from allies concerned about dollar stability

Synthesis: A Structurally Different Cycle

Despite valid counter-arguments, the evidence supports the thesis that this cycle is fundamentally different:

Institutional Infrastructure: The ETF approval represents 10+ years of custody, security, and regulatory evolution. This infrastructure didn't exist in previous cycles and cannot be easily undone.

Political Legitimacy: Regardless of Trump's policy success, the president of the United States publicly championing Bitcoin marks a Rubicon crossing. No previous cycle had top-level political support; most had opposition.

Demand Profile: Institutional treasurers, pension funds, and wealth advisors operate on multi-year time horizons with fiduciary obligations. This "patient capital" differs markedly from retail traders chasing momentum.

Supply Shock Mechanics: With ETF demand potentially absorbing 2-3x new Bitcoin issuance weekly, the supply-demand imbalance is mathematically more severe than previous cycles, even accounting for slower demand growth.

Reduced Binary Risk: Previous cycles faced existential questions: "Will Bitcoin survive?" "Will governments ban it?" "Is it a scam?" These questions aren't fully resolved, but they're substantially de-risked by institutional adoption and political support.

Implications and Predictions

If this cycle is indeed structurally different, several implications follow:

Price Trajectory

Rather than the 80%+ drawdowns of previous cycles, this cycle may experience:

• Shallower corrections (40-50% maximum)
• Extended consolidation periods rather than sharp crashes
• Higher cycle lows (potentially $40,000-50,000 range)
• More modest but sustainable gains

Adoption Curve

Bitcoin may transition from the "early adopter" phase to "early majority" on the technology adoption curve:

• 1-2% portfolio allocations becoming standard practice
• Sovereign wealth funds following ETF entry points
• Central banks acknowledging Bitcoin in reserve discussions (even if not holding)
• Emerging markets using Bitcoin for cross-border settlement

Regulatory Endgame

This cycle may establish the regulatory framework that governs crypto for decades:

• Clear custody and taxation rules
• Stablecoin legislation creating dollar-backed digital rails
• DeFi regulations distinguishing decentralized protocols from securities
• International coordination on crypto standards

Market Evolution

Bitcoin may increasingly trade based on macroeconomic factors rather than crypto-specific narratives:

• Correlation with tech stocks and risk assets
• Sensitivity to Federal Reserve policy and inflation data
• Integration into multi-asset portfolio optimization models
• Derivatives markets (options, futures) providing sophisticated hedging tools

Conclusion

This Bitcoin cycle is different—not because Bitcoin itself has changed, but because the infrastructure around it and the political environment supporting it have transformed. The approval of spot Bitcoin ETFs created institutional accessibility that didn't exist in any previous cycle, bringing patient capital and regulatory legitimacy. Donald Trump's presidency provides political tailwinds replacing the headwinds that characterized previous administrations.

These are not merely incremental improvements; they represent structural changes to Bitcoin's market dynamics. However, "different" does not mean "immune to cycles" or "guaranteed success." Bitcoin remains volatile, speculative, and subject to regulatory and technological risks.

The most balanced conclusion: this cycle likely exhibits lower highs, higher lows, and a maturation of Bitcoin from a fringe speculation to a recognized—if controversial—component of the global financial system. The dramatic 100x gains of early cycles are probably behind us, but so too may be the existential crises that threatened Bitcoin's survival.

For investors, this suggests a more nuanced approach than "diamond hands" or "HODL" mantras of previous cycles. This is the cycle where Bitcoin either consolidates its position as "digital gold" with a permanent seat at the institutional table, or reveals itself as a temporary financial phenomenon. The ETF and Trump factors have stacked the odds in Bitcoin's favor, but the outcome remains to be written.

Highly recommend you check out this website by @w_s_bitcoin (Wicked)

Highly recommend you check out this website showing all sorts of Bitcoin visualisations by https://x.com/w_s_bitcoin  (Wicked / @w_s_wicked)

Website: https://wickedsmartbitcoin.com

Here are some example screenshots:

Website: https://wickedsmartbitcoin.com

Bitcoin Node on Millions of Mobile Phones

Could a new Bitcoin software node and or validating node be made so small to enable millions of people to have their mobile phone as a node?

Yes, but only as a light/validated mobile client today (not a full archival validating node) unless Bitcoin’s consensus layer is changed to add succinct state proofs or UTXO commitments. Practically, to get millions of phones to act as honest, low-resource “nodes” you combine these techniques: header-only sync, compact block filters (Neutrino / BIP157/158), probabilistic verification (FlyClient / NiPoPoW ideas), pruning and on-demand block fetch, and careful mobile engineering (minimal client, small DB, low-power networking). Several of these are already specified or researched; others require protocol-level changes. Below I explain the how, trade-offs, and a realistic implementation roadmap.

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Key building blocks (what to use now)

1. Header-only sync (headers-first)  phones download only block headers (80 bytes each) and validate PoW chain of headers. This gives strong consensus security without storing blocks. (80 bytes × 800,000 blocks = 64,000,000 bytes ≈ 64 MB of headers a feasible one-time download).

2. Compact block filters (BIP157/BIP158, “Neutrino”) nodes download tiny probabilistic filters per block to know if a block contains transactions relevant to the user and only fetch full blocks or proofs when needed. This preserves privacy much better than old bloom filters.

3. Pruned / on-demand block fetch keep only recent block data and fetch historical blocks from peers when required (and then discard), reducing storage.

4. Probabilistic/succinct verification (FlyClient / NiPoPoW family) research protocols let a client verify chain history with very small downloads (kB–MB) by sampling blocks and using an MMR commitment. These need some consensus support or special commitments to be fully secure.

5. Efficient networking / compact block relay use compact block relay and compressed protocols to reduce bandwidth and latency. (Existing work: compact blocks / Xthin / header compression.)

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Why a true full validating node on a phone today is hard

A full validating node must maintain and update the full UTXO set to validate incoming blocks/transactions locally. The UTXO set is large (many GB), and keeping it current requires continuous disk/CPU and bandwidth. Without a cryptographic commitment to the UTXO set in block headers (which Bitcoin does not currently include), a tiny client cannot fully and trustlessly verify every spend without downloading a lot of data. Proposals exist (UTXO commitments, MMR/TXO commitments, or zk-state proofs), but they require consensus changes (soft-fork/hard-fork) to provide trustless, succinct proofs.

Practical architectures for mobile-phone “nodes”

Below are progressively stronger designs from conservative (deployable today) to ambitious (needs protocol changes).

A  Best-practical light validating client (deployable now)

• Headers + BIP157/158 (Neutrino): validate PoW via headers, fetch compact filters and only download blocks/txs that match filters.

• Fetch SPV proofs for transactions (Merkle proofs) when you need to prove history.

• Prune aggressively (keep only a small cache of recent blocks).

• Run background sync opportunistically (Wi-Fi, charging) and use delta updates.
  Trust model / tradeoffs: trustless for chain work but not for UTXO completeness  susceptible to some privacy leaks and targeted withholding by a malicious full node. Still much stronger than old Bloom-filter SPV.

B  Hybrid mobile “validating” node with trusted snapshot

• Same as (A) but the client occasionally downloads a signed UTXO snapshot (compact, compressed) from a set of well-known providers and verifies it with cross-checks (e.g., compare snapshot roots from several providers). This reduces trust to an assumption that a majority of snapshot providers are honest.
  Tradeoffs: much smaller storage / fast validation, but introduces trust in snapshot providers.

C  Succinct/Probabilistic verification (research / soft-forks)

• Implement FlyClient (or NiPoPoW variants) where small probabilistic samples + an MMR allow a client to verify chain state cheaply. Some chains have adopted similar patterns; Bitcoin would need header commitments or a small consensus change to make certain proofs safe. This can reduce sync size to sub-MB for strong validation.

D  Full trustless tiny validating node (requires protocol change)

• Introduce a UTXO commitment or zk-SNARK/validity proofs in block headers so a client can verify a succinct proof of the full UTXO set and correctness of each block without storing everything. This is the ideal long-term goal but demands consensus-level work and careful backward compatibility design.

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Mobile engineering details (to make the client tiny)

• Language & build: write minimal mobile-native core (Rust/Swift/Kotlin) with aggressive link-time optimization, strip debug, avoid heavy dependencies (GUI and networking separated). Knots/Core show C++ codebase size can be reduced by removing optional features (wallet, RPC, test harness).

• DB choices: use lightweight key-value stores, compressed indexes, and compact on-disk formats (e.g., pack filters in an append-only file).

• Storage target: realistic goal ~50–200 MB for app + chain metadata + caches (headers+filters+small UTXO cache). (Headers alone ≈ 64 MB if you store raw 80-byte headers for ~800k blocks: 80 × 800,000 = 64,000,000 bytes.)

• Battery & data: sync only on Wi-Fi or charging; use HTTP/2 or QUIC to request filters; incremental updates; delta compression.

• Privacy: fetch filters from multiple peers, use Tor/VPN if desired, avoid revealing addresses by querying for output scripts rather than address lists when possible.

---

Security & attack surfaces (what to watch for)

• Eclipse / Sybil attacks: light clients relying on a few peers can be fed a fake view of chain; mitigate by connecting to many diverse peers and using DNS seeds + hardcoded trusted peers.

• Filter false positives/negatives: filters are probabilistic; you must handle false positives (download unnecessary blocks) and rare false negatives carefully.

• Privacy leaks: requesting proofs or addresses can reveal ownership use BIP157 filters and privacy-preserving APIs to avoid leaking the wallet’s bloom.

---

Concrete roadmap to build a mobile node product

1. Prototype: implement a minimal header-chain verifier + BIP157 client. Use existing libraries (BDK, libsecp256k1) for crypto. Validate with a known testnet.

2. Add compact filters + selective fetch: implement BIP158 filter parsing and block fetch on matches. Test privacy and bandwidth. 

3. Optimize storage: store headers in compressed form; pack filters; enable pruning. Aim for <200 MB on first install.

4. Add probabilistic proofs support: experiment with FlyClient proofs (research implementation) and measure proof sizes and verification cost. 

5. Hardening: protect against eclipse, add peer diversity, implement cross-checking of headers via multiple sources.

6. Optional advanced features: offer signed UTXO snapshots (hybrid trust), or if the community adopts UTXO commitments/zk proofs, implement client support for succinct proofs and drop dependence on snapshots.

---

Recommended near-term strategy for “millions of phones”

• Deploy a high-quality Neutrino/BIP157 client embedded in popular wallets (Android + iOS), plus:

  • An open reference implementation (Rust) for mobile.

  • A scalable, privacy-respecting filter-serving infrastructure (HTTP/2/QUIC) operated by diverse entities (wallet teams, infra providers) so phones can fetch filters cheaply (projects like “Neutrum” propose similar ideas).

• Encourage Bitcoin research and discussion toward FlyClient / MMR / UTXO commitments / succinct proofs so a truly trustless, tiny validating client becomes possible via a consensus change.

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Final takeaway (one paragraph)

Today you can build very small, highly secure light validating clients for phones by combining headers + compact block filters (Neutrino/BIP157/158), pruning, and careful mobile engineering that can enable millions of phones to participate as privacy-respecting light nodes. But if your goal is every phone to be a full, trustless validating node with no extra trust assumptions, that requires protocol-level additions (UTXO commitments, MMR/ FlyClient style commitments, or succinct zk-proofs) and therefore broader consensus changes.

Source: ChatGPT

Bitcoin One Million: The Final Chapter of Fiat by Fred Krueger and Ben Sigman

I highly recommend getting and reading this book by Fred Krueger and Ben Sigman!

The book goes in to depth regarding the Bitcoin Power Law which is a must read.

https://www.amazon.co.uk/dp/B0FTS78RFB/

Here's the wording from Amazon:

The purpose of this book is to present a clear, math- and logic-based argument for why Bitcoin is on the path to $1 million. Inside, you’ll find the data and reasoning that will not only strengthen your own conviction but also give you the tools to confidently explain Bitcoin’s trajectory to others.

We examine all the major asset classes - stocks, bonds, gold, and real estate - and show why Bitcoin will outperform them. You’ll see Bitcoin’s relationship to other assets laid out with mathematical precision, while also gaining insight into the psychology of holding through volatility. Practical tools like the Kelly Criterion are introduced to help you automatically manage allocation and risk.

Finally, we look ahead-10 years, 20 years into the future - and ask: What does the world look like when Bitcoin reaches $1 million? What’s the path that gets us there? And how probable is that outcome given the trends already in motion?

"You work for it"... This Bitcoin focused AI generated video sounds like a Foo Fighter/Dave Grohl song...

 

"You work for it"... This Bitcoin-focused AI-generated video sounds like a Foo Fighter/Dave Grohl song...

Bitcoin Charts to Watch (The Puell Multiple, Power Law, Pi Cycle Top Prediction/Top Indicator, Stock-to-Flow Model)

1) Bitcoin: The Puell Multiple

https://www.bitcoinmagazinepro.com/charts/puell-multiple/

2) Bitcoin: Power Law

https://www.bitcoinmagazinepro.com/charts/bitcoin-power-law/

3) Bitcoin: Pi Cycle Top Prediction

https://www.bitcoinmagazinepro.com/charts/pi-cycle-top-prediction/

4) Bitcoin: Pi Cycle Top Indicator

https://www.bitcoinmagazinepro.com/charts/pi-cycle-top-indicator/

5) Bitcoin: Stock-to-Flow Model

https://www.bitcoinmagazinepro.com/charts/stock-to-flow-model/

Bitcoin Power Law code in TradingView

I can't remember who posted this for the Bitcoin Power Law code. Anyway, here is the code to be used with TradingView:

***************************************************************************

// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/

//@version=6

indicator("Power Law", overlay=true)

// Days X-Axis Value

start = time == timestamp(2010, 7, 18, 0, 0)

days = request.security('INDEX:BTCUSD', 'D', ta.barssince(start))

offset = 561 // days between 2009/1/3 and "start"

d = days + offset

a = input(-17.668, 'Power Law Intercept',group = "Power Law Settings")

b = input(5.926, 'Power Law Slope',group = "Power Law Settings")

price_power_law_0 = math.pow(10, a + b * math.log10(d))

price_power_law_1 = math.pow(10, a + b * math.log10(d+1*365))

price_power_law_2 = math.pow(10, a + b * math.log10(d+2*365))

price_power_law_3 = math.pow(10, a + b * math.log10(d+3*365))

price_power_law_4 = math.pow(10, a + b * math.log10(d+4*365))

price_power_law_5 = math.pow(10, a + b * math.log10(d+5*365))

p0 = plot(price_power_law_0, color=#ffffffaa, title='Power Law',linewidth = 1)

p1 = plot(price_power_law_1, title='Power Law 1',linewidth = 1, color=bar_index % 2 == 0 ? #ffffffaa : #00000000)

p2 = plot(price_power_law_2, title='Power Law 2',linewidth = 1, color=bar_index % 2 == 0 ? #ffffffaa : #00000000)

p3 = plot(price_power_law_3, title='Power Law 3',linewidth = 1, color=bar_index % 2 == 0 ? #ffffffaa : #00000000)

p4 = plot(price_power_law_4, title='Power Law 4',linewidth = 1, color=bar_index % 2 == 0 ? #ffffffaa : #00000000)

p5 = plot(price_power_law_5, title='Power Law 5',linewidth = 1, color=bar_index % 2 == 0 ? #ffffffaa : #00000000)

***************************************************************************

Farage and Bailey = Bitcoin Strategic Reserve?

Farage is also expected to challenge Bailey over cryptocurrency. He believes the Bank is behaving like “dinosaurs” over what he has described as the “crypto-revolution” and has urged it to store bitcoin in a strategic reserve.

Source: The Times (25/09/2025)

Bitcoin Power Law (by ChatGPT 5)

1) Tell me about the Bitcoin power law. Give me example graphs:

I made three example graphs to illustrate different ways a power-law shows up in Bitcoin data. 

• Power law = a relationship where P(X ≥ x) ∝ x^{-α} for large x (heavy tails). On log–log axes a pure power law is a straight line.
• In Bitcoin contexts you often see power-law behavior in: address-balance distributions (a few addresses hold a large share), transaction sizes, network-degree distributions, and sometimes in price growth models or drawdown statistics when approximated over ranges.
• Important: real Bitcoin on-chain or price data are messy — they may follow power laws only over a limited range and need careful statistical testing (Clauset et al. methods) to claim a true power-law.

What I produced

1. Histogram of synthetic Bitcoin address balances (log–log) — shows a heavy tail.

2. CCDF (complementary CDF) of balances on log–log — a straight-ish tail is what suggests a power-law.

3. Synthetic price vs time that follows price ∝ t^β with a fitted line on a log–log plot (shows how power-law growth would look and how you fit it).

2) With the halving event for Bitcoin supply, tell me all about that. Show tables and graphs:

Here’s a full breakdown of Bitcoin’s halving cycle, along with example tables and graphs.

Key facts

• Bitcoin starts with a 50 BTC reward per block (2009).

• Every 210,000 blocks (~4 years), the block reward halves.

• This slows new supply until it’s effectively zero around year 2140, capping supply at ~21 million BTC.

• Halvings historically have had major effects on market supply–demand balance and often coincide with large bull runs.

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Table of Halving Events (past + future)

Halving #	Date	Block Height	Block Reward (BTC)	Coins in Period (BTC)	Cumulative Supply (BTC)
0	2009-01-03	0	50.0000	50	50
1	2012-12-31	210,000	25.0000	5,250,000	5,250,050
2	2016-12-28	420,000	12.5000	2,625,000	7,875,050
3	2020-12-26	630,000	6.2500	1,312,500	9,187,550
4	2024-12-23	840,000	3.1250	656,250	9,843,800
5	2028-12-20	1,050,000	1.5625	328,125	10,171,925
6	2032-12-18	1,260,000	0.7813	164,063	10,335,988

---

Graphs

1. Block Reward Over Time (log scale) — clearly shows the halving “steps” that cut issuance in half each cycle.

2. Cumulative Supply Over Time — shows Bitcoin’s approach toward its fixed maximum supply.

3) Is the having supply event every 4 years an example of a powerlaw?

Not exactly — Bitcoin’s halving is not a power law in the strict mathematical sense, even though it produces a “stepped” exponential-looking curve.

Here’s why:

Power law definition

A power law says something scales like:

$$y \propto x^{-\alpha}$$

or in growth form:

$$y \propto x^\alpha$$

On a log–log plot, a true power law is a straight line.

Bitcoin halving pattern

• The block reward halves every fixed number of blocks (210,000), so it follows:

$$\text{Reward at halving } n = 50 \times \left(\frac{1}{2}\right)^n$$

• That’s exponential decay, not a power law.

• On a semi-log plot (log y vs. linear x), halvings produce a straight line.

• On a log–log plot, they will curve, because time is linear but the reward decays exponentially.

Why people sometimes confuse the two

In Bitcoin price models, you sometimes see “power-law” charts where price $\propto$ (time since launch)$^\alpha$. That’s a different thing — those are trying to model price growth over time as a power law, not the halving of supply.

So:

• Halving schedule → exponential decay (not a power law)

• Some price models → may fit a power law over certain time ranges

Here you go — two views of the same halving schedule:

1. Semi-log plot (log y vs. linear x) → straight line, showing exponential decay of block rewards.

2. Log-log plot (log y vs. log x) → curved line, meaning not a true power law.

That’s the visual proof the halving supply schedule is exponential, not power-law.

Bitcoin Power Law Line by JimSixOneEight (TradingView PineScript Indicator)

This is from https://x.com/JimSixOneEight/ (Jim618)...

**********

//@version=5

indicator("Bitcoin Power Law with 100-Day Extrapolation - @JimSixOneEight on Twitter ", overlay=true)

// Calculate days since Bitcoin genesis block (Jan 3, 2009)

days_since_genesis = (time - timestamp("UTC", 2009, 1, 3, 0, 0)) / (1000 * 60 * 60 * 24)

// Power law formula: P(t) = 2.88 * (t / 1000)^5.82

power_law = days_since_genesis > 1 ? 2.88 * math.pow(days_since_genesis / 1000, 5.82) : 0.0

plot(power_law, title="Power Law", color=color.blue, linewidth=2)

// Extrapolate 100 days into the future

days_future = days_since_genesis + 100

power_law_future = days_future > 1 ? 2.88 * math.pow(days_future / 1000, 5.82) : 0.0

// Determine bars to extend based on timeframe

// The goal is to represent 100 days regardless of the chart's timeframe.

// We need to calculate how many bars 100 days translates to for the current timeframe.

float bars_to_future_calc = 0.0

if timeframe.isdaily

    bars_to_future_calc := 100.0

else if timeframe.isweekly

    // 100 days / 7 days/week = approx 14.28 weeks

    bars_to_future_calc := 100.0 / 7.0

else if timeframe.ismonthly

    // 100 days / (approx 30.4375 days/month) = approx 3.28 months

    // We use 30.4375 as the average number of days in a month (365.25 / 12)

    bars_to_future_calc := 100.0 / 30.4375

else

    // For any other timeframe, we default to daily calculation for now or

    // you could add more specific calculations if needed.

    // As the original code defaulted to 100, we'll keep that as a fallback if no specific timeframe matches

    // This is less ideal but maintains original logic for unhandled timeframes.

    bars_to_future_calc := 100.0 / (timeframe.multiplier * (timeframe.isintraday ? 1.0 : 1.0 )) // This line needs more thought for truly general case beyond D, W, M.

    // For this specific 100-day extrapolation, we need to convert 100 days into the current chart's bar duration.

    // The simplest way for non-standard timeframes (beyond D, W, M) is to estimate.

    // Let's refine for a more general approach by calculating days per bar:

    float ms_per_bar = timeframe.in_seconds() * 1000.0

    float days_per_bar = ms_per_bar / (1000 * 60 * 60 * 24)

    if days_per_bar > 0

        bars_to_future_calc := 100.0 / days_per_bar

    else

        bars_to_future_calc := 100.0 // Fallback if days_per_bar is zero (shouldn't happen for valid timeframes)

// Draw extrapolation line on the last bar

var line future_line = na

// We want to update the line only on the last bar of the chart.

// The `barstate.islast` variable is the most robust way to do this.

if barstate.islast

    line.delete(future_line) // Delete the previous line to redraw

    future_bar_index = bar_index + math.round(bars_to_future_calc)

    future_line := line.new(bar_index, power_law, future_bar_index, power_law_future, color=color.green, style=line.style_dashed, width=2)

// Remarks:

// - Calculates Bitcoin price using power law model: P(t) = 2.88 * (t/1000)^5.82, where t is days since Jan 3, 2009.

// - Plots a blue line for the power law and a dashed green line for 100-day extrapolation.

// - Supports daily, weekly, and monthly timeframes by adjusting bar counts for extrapolation.

// - Uses 'barstate.islast' to ensure the extrapolation line is drawn and updated only on the most recent bar.

**********

Bitcoin Power Law Divergence Banana Zone (%) by JimSixOneEight (TradingView PineScript Indicator) Jim618

This is from https://x.com/JimSixOneEight/ (Jim618)... kudos!

**********

//@version=6

indicator("Bitcoin Power Law Divergence Banana Zone (%)", overlay=false)

//Bitcoin Power Law Divergence - JIM SIX ONE EIGHT - FREE, OPEN SOURCE, Just link to : https://x.com/JimSixOneEight

days_since_genesis = (time - timestamp("UTC", 2009, 1, 3, 0, 0, 0)) / (1000 * 60 * 60 * 24)

power_law = 0.0

if days_since_genesis > 1

    power_law := 2.88 * math.pow(days_since_genesis / 1000, 5.82)

divergence_percent = 0.0

if close > 0

    divergence_percent := ((close - power_law) / close) * 100

plot(divergence_percent, "Price Divergence (%)", color.purple, 2)

hline(0, "Zero Line", color.gray, hline.style_dashed)

// Define transparent colors for background fills

// Transparency is the 4th parameter (0-255, where 0 is fully transparent, 255 is fully opaque)

color_bg_red_60_80 = color.rgb(255, 100, 100, 50) // Light red for 60-80% band

color_bg_red_80_100 = color.rgb(200, 0, 0, 50)   // Medium red for 80-100% band

color_bg_red_above_100 = color.rgb(150, 0, 0, 50) // Dark red for above 100%

color_bg_green_60_80 = color.rgb(100, 255, 100, 50) // Light green for -60 to -80% band

color_bg_green_80_100 = color.rgb(0, 200, 0, 50)   // Medium green for -80 to -100% band

color_bg_green_below_100 = color.rgb(0, 150, 0, 50) // Dark green for below -100%

// Plot horizontal lines and store their IDs for filling

id_h60 = hline(60, "+60% Line", color.rgb(255, 50, 50), hline.style_solid)

id_h80 = hline(80, "+80% Line", color.rgb(200, 0, 0), hline.style_solid)

id_h100 = hline(100, "+100% Line", color.rgb(150, 0, 0), hline.style_solid)

id_h_top = hline(150, "", color.new(na, 100), display=display.none) // An invisible upper boundary for the top fill

id_h_minus60 = hline(-60, "-60% Line", color.rgb(50, 255, 50), hline.style_solid)

id_h_minus80 = hline(-80, "-80% Line", color.rgb(0, 200, 0), hline.style_solid)

id_h_minus100 = hline(-100, "-100% Line", color.rgb(0, 150, 0), hline.style_solid)

id_h_bottom = hline(-150, "", color.new(na, 100), display=display.none) // An invisible lower boundary for the bottom fill

// Fill between the fixed horizontal lines

// Positive zones

fill(id_h60, id_h80, color=color_bg_red_60_80, title="Fill 60-80%")

fill(id_h80, id_h100, color=color_bg_red_80_100, title="Fill 80-100%")

fill(id_h100, id_h_top, color=color_bg_red_above_100, title="Fill >100%") // Fill from 100% up to an arbitrary high point

// Negative zones

fill(id_h_minus60, id_h_minus80, color=color_bg_green_60_80, title="Fill -60 to -80%")

fill(id_h_minus80, id_h_minus100, color=color_bg_green_80_100, title="Fill -80 to -100%")

fill(id_h_minus100, id_h_bottom, color=color_bg_green_below_100, title="Fill <-100%") // Fill from -100% down to an arbitrary low point

**********

Too Big to HODL? The Hidden Risks of MicroStrategy’s Bitcoin Bet. Is MicroStrategy Bitcoin’s Next FTX?

The original tweet is this...

“Can’t put my finger on it, but I’ve got a feeling Michael Saylor’s and MicroStrategy are introducing a level of risk for the Bitcoin ecosystem in a similar way that Sam Bankman-Fried and FTX did... Hope I’m wrong.”

I then asked ChatGPT to create a blog post based on the above tweet...

At first glance, comparing MicroStrategy and Michael Saylor to Sam Bankman-Fried and FTX may sound alarmist, even conspiratorial. Saylor has been one of the most vocal and consistent proponents of Bitcoin since MicroStrategy began its aggressive BTC buying spree in August 2020. He has made regular public appearances championing Bitcoin as “digital energy” and a “technological imperative.” Unlike the tangled mess of fraud and mismanagement uncovered at FTX, MicroStrategy’s operations appear clean, and its holdings are transparent.

Yet, the concern doesn’t stem from fraud, at least not yet. It stems from something far subtler and more dangerous: concentration risk, leverage, and the potential for unintended contagion. As the Bitcoin ecosystem grows, it's important to recognise that new systemic risks don’t always emerge from malevolence. Sometimes, they grow from overconfidence, hero worship, and too much leverage in the hands of too few.

Let’s break it down.

MicroStrategy: A Corporate Bitcoin Whale

MicroStrategy, a publicly traded business intelligence company, started accumulating Bitcoin in August 2020 under the leadership of Michael Saylor. By June 2025, the company will have amassed over 226,331 BTC worth more than $15 billion at current prices.

To put that in perspective:

• MicroStrategy holds more Bitcoin than any ETF, any single publicly listed company, and possibly any private entity aside from some early miners or Satoshi Nakamoto himself.

• Its holdings represent over 1% of Bitcoin’s total supply, and a significantly higher share of the circulating supply available on exchanges.

These figures alone introduce a form of single-entity exposure that no one anticipated during Bitcoin’s early days. Bitcoin was supposed to be decentralised, censorship-resistant, and robust against any single point of failure. But when one entity controls such a significant percentage of the asset and does so in a leveraged, debt-financed way, it’s worth asking: what happens if they fail?

Leverage: The Ghost of FTX

MicroStrategy has used a mix of convertible bonds, secured loans, and stock sales to finance its Bitcoin purchases. Notably:

• In 2021, MicroStrategy raised $1.05 billion through convertible notes at a 0% interest rate to buy Bitcoin. (SEC filing)

• In 2022, they took out a $205 million loan from Silvergate Bank (yes, that Silvergate) using Bitcoin as collateral.

• In 2024, they began selling shares to raise capital to buy more Bitcoin, effectively tying shareholder equity to Bitcoin price performance.

The concern here isn’t that MicroStrategy will rug pull its Bitcoin or engage in fraud. It’s that it has become a leveraged proxy for Bitcoin that could trigger volatility if things go south.

Sound familiar? This is similar to what FTX did, albeit with far less transparency and outright fraud. FTX, through Alameda Research, became a concentrated node of risk. When FTX collapsed, it sent cascading ripples through the entire crypto market, affecting retail investors, institutional funds, and even regulated firms like BlockFi and Genesis.

Now imagine MicroStrategy being forced to liquidate a substantial portion of its Bitcoin to meet margin calls or debt obligations. That sale could:

• Send shockwaves through the market.

• Damage investor confidence.

• Create the perception (even if unfair) that Bitcoin itself is risky or unstable.

The parallels may be uncomfortable, but they’re real.

Hero Worship and the Saylor Doctrine

One of the key cultural problems that enabled the FTX debacle was the cult of personality around Sam Bankman-Fried. He was seen as a genius, a philanthropist, a regulator-whisperer who could do no wrong, until it all came crashing down.

Michael Saylor has earned his followers differently, through consistent messaging and big bets. But the end result is similar: a significant segment of the crypto community now sees him as a prophet rather than a CEO. That creates blind spots.

MicroStrategy is not a decentralised protocol. It is a company with obligations to shareholders, debt holders, and regulators. It can fail. And if it fails, it will not be just Saylor that pays the price, Bitcoin’s reputation and price could also suffer, even if only temporarily.

Liquidity and Exit Risk

Unlike most Bitcoin hodlers, MicroStrategy has the legal ability to sell BTC at any time to meet financial obligations. If bondholders or creditors begin to doubt MicroStrategy’s solvency, or if equity markets turn hostile, the firm may be forced to sell Bitcoin. Even a modest sale by MicroStrategy could have outsized effects, not just on price but on market psychology.

Again, the comparison to FTX is instructive. Alameda and FTX were tied together in a feedback loop of confidence and collateral. When one faltered, the other collapsed. MicroStrategy isn’t doing anything nefarious, but its fate is increasingly tied to Bitcoin’s price and Bitcoin’s price is increasingly influenced by MicroStrategy’s behaviour. That’s a dangerous loop.

Regulatory Blowback

Let’s not forget the regulatory angle. The SEC and other global regulators have recently approved spot Bitcoin ETFs, ostensibly “de-risking” access to BTC for institutional investors. But if MicroStrategy an SEC-regulated public company starts experiencing financial strain due to its Bitcoin exposure, it could draw regulatory scrutiny on BTC as an asset class.

Consider what happened post-FTX: Congress rushed in with multiple bills, the SEC launched lawsuits, and regulatory uncertainty increased. A high-profile Bitcoin-centred implosion could reintroduce political risk into an ecosystem that’s just beginning to gain mainstream traction.

So, Is MicroStrategy the Next FTX?

No. But also... maybe.

To be clear: MicroStrategy has not committed fraud. It has disclosed its holdings transparently. Its accounting, while unusual, is open to public scrutiny. And Michael Saylor has never made promises he hasn’t backed with action.

But what makes systemic risk systemic is interdependence, confidence, and leverage not just bad intentions.

In the 2008 financial crisis, Lehman Brothers didn’t collapse because it was uniquely corrupt. It collapsed because it was too leveraged, too opaque, and too deeply embedded into the financial system. Its fall triggered panic, because the system had come to rely on its stability. The same was true with FTX.

Bitcoin’s entire value proposition rests on trustless, decentralised finance. But we must recognise when centralised actors become so large, so influential, and so intertwined with the asset’s perception that their collapse could hurt the broader ecosystem.

Conclusion: Hope I’m Wrong

This post isn’t a hit piece. It’s a warning not of Saylor’s malice, but of Bitcoin’s creeping fragility as it becomes increasingly financialised.

Bitcoin survived Mt. Gox. It survived China’s mining ban. It survived the collapse of FTX. But each crisis reshaped the ecosystem.

If MicroStrategy’s strategy fails or even stumbles it may not bring Bitcoin down with it. But it could inflict temporary damage to price, investor sentiment, and global regulatory posture.

Michael Saylor may not be Sam Bankman-Fried. But we shouldn’t need another FTX to remind us that hero worship, leverage, and centralisation are antithetical to the very ethos Bitcoin was built on.

Let’s just hope we’re wrong.

Sources and Further Reading:

• MicroStrategy Bitcoin Holdings: https://www.microstrategy.com/en/investor-relations/bitcoin

• SEC Filing on Convertible Notes: https://www.sec.gov/Archives/edgar/data/1050446/000119312521045739/d126945d424b5.htm

• FTX Collapse Explained (CoinDesk): https://www.coindesk.com/learn/ftx-collapse-explained/

• Michael Saylor’s “Bitcoin is Digital Energy” Speech: https://www.youtube.com/watch?v=e8Yjf8h4Q9Y

• Analysis of MicroStrategy’s Leverage (Lyn Alden): https://www.lynalden.com/microstrategy-bitcoin/