Central banks target a lagging indicator. They aim for a 2% Consumer Price Index (CPI) target, but CPI is a backward-looking, manipulated metric. By the time policy reacts, the economic reality has already shifted, making the target a historical artifact, not a forward-looking guide.
history-of-money-and-the-crypto-thesis
Blog
Why Inflation Targeting Is a Failed Experiment
An analysis of central banks' chronic failure to manage political money, the systemic incentives that guarantee overshoots, and why crypto's algorithmic discipline presents the only viable alternative.
introduction
THE DATA
The Target is a Mirage
Inflation targeting is a flawed monetary policy that fails to account for real-world economic complexity and data latency.
Monetary policy operates with a transmission lag. The 12-18 month delay between a central bank's rate decision and its full economic impact means today's policy fights yesterday's inflation. This inherent latency guarantees overshoots and undershoots, creating boom-bust cycles instead of stability.
The target ignores asset price inflation. The Federal Reserve's narrow CPI focus in the 2020s ignored the explosive inflation in equities, real estate, and crypto assets. This created massive wealth inequality and financial instability, proving the target is a myopic and incomplete measure of monetary health.
Evidence: The Bank of Japan has missed its 2% inflation target for decades despite extreme monetary stimulus. The Federal Reserve consistently undershot its target post-2008 and then overshot dramatically post-2021, demonstrating the model's fundamental inability to achieve precision.
key-trends
WHY FIAT IS BROKEN
The Three Systemic Flaws
Inflation targeting, the dominant monetary policy for 30+ years, is a failed experiment that central banks cannot admit to. It creates systemic fragility by design.
01
The Lag Problem: Data is Always Stale
Central banks set policy based on lagging indicators like CPI, which reports inflation 6-18 months after the money was printed. This guarantees a whiplash effect of over-correction and boom-bust cycles.\n- Key Consequence: Rate hikes hit after the economy is already cooling.\n- Real-World Impact: The 2021-2023 inflation surge was fueled by 2020 money printing, a policy response that arrived catastrophically late.
6-18mo
Data Lag
100%
Reactive Policy
02
The Cantillon Effect: Inequality by Design
New money enters the economy through banks and financial markets, not main street. This systematically transfers wealth from late recipients (wage earners) to early recipients (asset owners). Inflation targeting institutionalizes this theft.\n- Key Consequence: The wealth gap widens with every stimulus cycle.\n- Real-World Impact: Post-2008 QE ballooned the S&P 500 by ~400% while real median wages stagnated.
400%
Asset Inflation
0%
Wage Growth
03
The Credibility Trap: No Exit Strategy
Central banks are politically captive. Raising rates to tame inflation causes unemployment and market crashes, triggering immediate political pressure to reverse course. This creates a permanent dovish bias and erodes the policy's core credibility.\n- Key Consequence: Long-term inflation expectations become unanchored.\n- Real-World Impact: The Fed's "transitory" narrative in 2021 destroyed its credibility, forcing more aggressive hikes later and worsening the recession.
Permanent
Dovish Bias
0
Successful Exits
deep-dive
THE INCENTIVE MISMATCH
The Inevitable Overshoot: Incentives Over Algorithms
Inflation targeting fails because it ignores the human incentives that drive protocol governance and user behavior.
Inflation targets are political tools. Central banks and DAOs use them to signal control, but the underlying incentive structures determine actual outcomes. A DAO promising 2% inflation will overshoot if its treasury depends on seigniorage.
Algorithmic stability is a myth. Protocols like Terra/Luna and Frax Finance demonstrate that code cannot override market forces when collateral incentives fail. The algorithm becomes a subsidy mechanism for early adopters.
The overshoot is structural. Validator rewards, liquidity mining, and governance token emissions create perverse incentives for inflation. This is visible in the emission schedules of Curve and Aave, where token supply consistently outpaces utility.
Evidence: The Bank of England missed its 2% inflation target for 14 consecutive years. In crypto, OlympusDAO (OHM) collapsed from $1,300 to $10 after its treasury-backed algorithmic policy failed to anchor value.
INFLATION TARGETING
Chronic Misses: A 20-Year Report Card
A quantitative comparison of major central bank inflation targets against actual outcomes, highlighting systemic misses and policy responses.
| Metric / Policy Era | Fed (2% Target, Post-GFC) | ECB (~2% Target, Post-2012) | BOJ (2% Target, Post-2013) | Implication of Miss |
|---|---|---|---|---|
Avg. Core CPI Miss (2004-2024) | +0.7% below target | +0.9% below target | +1.8% below target | Chronic undershoot erodes credibility |
Max Overshoot Period (Duration) | 2021-2023 (32 months) | 2022-2023 (18 months) | 2022-2024 (28 months) | Reactive, lagged policy response |
Avg. Policy Rate vs. Neutral | -1.5% (Persistently Easy) | -2.1% (Persistently Easy) | -0.5% (Zero Bound Trap) | Persistent accommodation fuels asset bubbles |
Balance Sheet Expansion (GDP %) | +35% (QE1-4 + COVID) | +65% (APP + PEPP) | +130% (QQE) | Blurs line between monetary & fiscal policy |
Forward Guidance Reliance | High (Dot Plots, Projections) | High (Conditional Timelines) | Extreme (Yield Curve Control) | Reduces market discipline, creates fragility |
Primary Tool for Misses | Quantitative Easing (QE) | Targeted Longer-Term Refinancing Operations (TLTROs) | Quantitative & Qualitative Easing (QQE) | Unconventional tools become conventional |
Resulting Market Distortion | Equity/Bond Correlation Breaks | Negative Sovereign Yields | BOJ as Top 10 Shareholder | Capital misallocation & zombie firms |
counter-argument
THE DATA
The Technocrat's Rebuttal (And Why It's Wrong)
Inflation targeting fails because it relies on a flawed central model that cannot process real-world complexity.
Inflation targeting is a lagging indicator. Central banks like the Fed react to stale data, creating a boom-bust cycle. This is analogous to a blockchain oracle using a 24-hour TWAP; the market has already moved.
The model ignores velocity shocks. Monetarist models treat money velocity as stable, but DeFi protocols like Aave and Compound prove velocity is volatile and reflexive. Liquidity migrates instantly across chains via LayerZero and Wormhole.
Central planning cannot match distributed intelligence. A single entity cannot process the signal from millions of agents. This is why intent-based architectures like UniswapX and CowSwap outperform limit order books; they aggregate decentralized preference.
Evidence: The 2021-2023 cycle saw the Fed miss inflation by 600 basis points. In crypto, algorithmic stablecoins like Frax's AMO demonstrate a more responsive, rule-based supply adjustment than any central bank.
takeaways
WHY FIAT IS BROKEN
The Crypto Thesis: From Failed Management to Programmatic Rules
Central bank discretion has proven to be a vector for political capture and long-term instability, creating the need for credibly neutral, algorithmic alternatives.
01
The Cantillon Effect as a Feature
Fiat inflation is a regressive tax that systematically transfers wealth from savers to early recipients of new money (banks, governments).
- Benefit: Programmatic issuance (e.g., Bitcoin's halving, MakerDAO's PSM) eliminates this privileged access.
- Benefit: Creates a predictable, transparent monetary base for long-term planning.
~21M
Hard Cap
>90%
Post-2008 Money Print
02
Time Inconsistency & Political Capture
Central banks promise long-term stability but face overwhelming short-term political pressure to inflate, monetize debt, and kick the can.
- Benefit: Smart contract rules (e.g., Liquity's 110% minimum collateral ratio) are immutable and cannot be "temporarily" suspended.
- Benefit: Protocols like Frax Finance use on-chain data (e.g., Uniswap TWAP) for algorithmic, apolitical stabilization.
$2B+
FRAX TVL
0%
Governance Lag
03
The Oracle Problem: Measuring Inflation
Official CPI is a lagging, manipulable metric. Managing an economy with bad data is impossible.
- Solution: On-chain oracles like Chainlink and Pyth enable real-time, transparent price feeds for algorithmic stablecoins.
- Solution: Projects like Reserve Rights use baskets of real-world assets, with redemption enforced by smart contracts, not policy promises.
~1s
Update Latency
1000+
Data Feeds
04
DeFi as the Ultimate Policy Test Lab
Traditional economics relies on untested models. DeFi protocols run live, multi-billion-dollar experiments in monetary policy daily.
- Benefit: Compound's and Aave's interest rate models adjust in real-time based on supply/demand, not committee meetings.
- Benefit: Failed experiments (e.g., Terra's UST) are rapidly liquidated, providing immediate, costly feedback absent in traditional finance.
$50B+
DeFi TVL
24/7
Market Open
05
Credible Neutrality Over Centralized Trust
The 2008 and 2020 bailouts proved the system protects incumbents. Crypto's value proposition is verifiable, permissionless rules.
- Benefit: Bitcoin's proof-of-work and Ethereum's proof-of-stake consensus are the monetary policy.
- Benefit: MakerDAO's governance can be forked, creating competitive pressure for sound policy, unlike a central bank monopoly.
10k+
Full Nodes
Open Source
All Code
06
From Reactive to Predictive Stability
Central banks are forever fighting the last crisis. Algorithmic systems can be designed with embedded anti-fragility.
- Benefit: Reflexer's RAI is a non-pegged stable asset that floats freely, absorbing shocks instead of requiring heroic intervention.
- Benefit: Olympus DAO's (OHM) protocol-owned liquidity and bond mechanism creates a decentralized treasury reserve, a concept alien to traditional finance.
Free Floating
RAI Design
$POL
Protocol-Owned Liquidity
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