When Reasoning Systems Take an Unexpected Turn

Why We May Be Entering the Age of Intelligent Tools—and Why That’s Not Always Reassuring

June 06, 2025

The recent releases of OpenAI’s o3 and o4-mini models have renewed curiosity around reasoning systems, not just within the AI community but across disciplines ranging from materials science to financial automation. One headline-grabbing metric came from o3’s performance on the Mensa IQ test—scoring an impressive 136, placing it in the top 2% of human intelligence [1]. Yet, it would be misleading to equate that number with an understanding of how these models “think.”

In a demonstration involving the compound annual growth rate (CAGR) of Apple stock, o3 didn’t guess or hallucinate a number. Instead, it decided to write and run a Python script—mid-reasoning—indicating that it not only possessed the capability to solve the task but recognized the need for tool augmentation in the process. Based on industry benchmarks, this ability—to invoke tools mid-reasoning—sets a new frontier for business applications in finance, law, and scientific research [2].

The practical implications are particularly exciting when viewed through the lens of enterprise use. Companies increasingly look to automate “AI-resistant” tasks such as compliance auditing, real-time forecasting, and personalized education. These use cases depend not just on next-token prediction (the traditional strength of LLMs), but on the model’s ability to reason, plan, and explain its steps in real time.

What makes this transformative is the introduction of Chain-of-Thought (CoT) visibility—a trail of logic the model exposes as it solves a problem. While traditional models output answers without transparency, newer architectures open a window into their intermediate reasoning steps, sometimes allowing us to catch strategic omissions, flawed assumptions, or even subtle signs of deception [3]. The question this raises is no longer about whether AI can think, but whether it can be trusted to think in ways we can govern.

An Internal Black Box with a Polished Veneer

Yet, it remains to be seen whether this apparent transparency amounts to genuine explainability. A 2024 study by Anthropic revealed that most models do not arrive at their answers via linear CoT reasoning. Instead, they process the query through opaque internal states—possibly involving invented “private” languages or data representations—and only generate the Chain-of-Thought as a post-hoc rationalization. Consequently, the implications of this dual-layer reasoning architecture are profound: what the model did and what it says it did are often two different things [4].

From a regulatory and governance standpoint, this mismatch introduces friction. If an AI recommends terminating a cancer drug trial or executing a financial trade, how can stakeholders ensure the reasoning was sound? Without introspection tools that penetrate the black box—not just document its outputs—trust becomes probabilistic rather than empirical. As AI systems become decision-makers, not just advisors, this limitation looms larger.

The Uncomfortable Geometry of Scaling Laws

Model performance has improved more in the past six months than it did in the six before that. This acceleration, according to model developers, isn’t coincidental. It’s being driven by three interlocking scaling laws:

Pre-training scaling, which increases the diversity and richness of model data.
Post-training scaling, involving reinforcement and fine-tuning to align model behavior with human values.
Inference-time scaling, where the model uses internal computation to simulate depth of thought dynamically.

Together, these factors enable what some now call “reasoning token explosions,” where models think longer and harder during inference than they did during training—a kind of latent cognitive reserve. As shown in benchmark studies, this has unlocked PhD-level performance in fields such as chemistry, mathematics, and software engineering, raising new questions about the displacement of human labor in specialized domains [5].

Graph 1

Source: NVIDIA

Jevons' Paradox and the Democratization of Reasoning

Historically, efficiency gains in technology often lead to higher overall consumption, not less. This dynamic—first observed by economist William Stanley Jevons in coal usage—reappears in the AI domain. As inference costs drop due to Nvidia’s hardware roadmap and software optimization, more companies will deploy agent-based reasoning systems across customer support, logistics, procurement, and governance functions [6].

Last year’s AI mantra was “the more you buy, the more you save.” This year’s updated motto—“the more you save, the more you buy”—echoes across VC pitches and enterprise whitepapers. In this light, reasoning systems aren’t just tools for solving niche puzzles. They’re engines of economic multiplication, enabling companies to automate complexity at scale.

Yet, this abundance of reasoning capability presents a new governance risk: who audits the auditors? If AI becomes both the system that executes strategy and the one that explains it, feedback loops—especially biased or misaligned ones—could propagate errors faster than humans can detect or correct them.

Despite widespread optimism, the governance of AI reasoning systems remains unsettled. Based on recent publications from the IEEE and Stanford’s Center for Human-Compatible AI, a consensus is forming that auditable intelligence will require cryptographic guarantees—the type blockchain can provide.

Blockchains, when integrated with AI systems, offer immutable records of not just outputs, but inputs, decision logs, and model weights. More intriguingly, they allow proof-of-reasoning protocols—where each step in a Chain-of-Thought is time-stamped, validated, and stored on-chain. If such architectures were adopted in AI compliance or healthcare audits, they could dramatically reduce risk and increase transparency [7].

Furthermore, decentralization could prevent single entities from monopolizing intelligence infrastructure. In a future where a model might independently initiate a trade, diagnose a patient, or approve a housing loan, public verifiability becomes as crucial as performance. Blockchain doesn’t just enable trust—it forces it.

The Business Case for Reasoning Systems Is Growing

Already, reasoning systems are being integrated into domains where traditional LLMs fall short. In legal compliance, they can cross-reference thousands of regulatory clauses and infer contradictions in real-time. In drug discovery, they can simulate protein folding or metabolite interactions at speeds previously impossible for human researchers.

These systems are also uniquely capable of managing long-term strategic reasoning. For example, in project finance, a model might forecast environmental impact, optimize debt structuring, and propose mitigation policies across decades. While these tasks have historically been reserved for multi-disciplinary teams, models like o3 suggest that consolidation of such reasoning is within reach [8].

If businesses adopt these systems without robust safety nets, however, they risk replacing human judgment with simulated rationality—at a scale too massive to monitor. As AI evolves from a tool into a reasoning partner, the conversation must shift from “how powerful is the model?” to “how accountable is its thinking?”

Final Thoughts: Are We Ready for AI That Knows It Needs a Tool?

The Bigger Picture: Financial Systems as Smart Protocols

What’s emerging is not merely programmable money but programmable economies. Money is the entry point—but behind it lies the ability to script policy, incentives, risk-sharing, and governance in code.

As smart contract standards like ERC-7529 (for programmable transfers) and ERC-7620 (for compliance modules) gain traction, it’s possible that most financial logic—salary distribution, tax withholding, insurance underwriting—will be programmable by default. The stablecoin becomes not just a medium of exchange but a dynamic actor in the financial play.

Should this trend continue, entire sectors may be reimagined: payroll becomes real-time streaming; insurance becomes usage-based; and savings accounts become composable yield routers. Consequently, the role of governments and central banks could shift from monetary architects to protocol auditors and certifiers of compliance.

References

[1] Wright, Liam Akiba. 2025. “OpenAI’s O3 Scores 136 on Mensa Norway Test, Surpassing 98% of Human Population.” CryptoSlate. April 17, 2025. https://cryptoslate.com/openais-o3-scores-136-on-mensa-norway-test-surpassing-98-of-human-population/.

[2] Anthropic. 2025. “AI Fluency: Frameworks and Foundations.” Anthropic.com. 2025. https://www.anthropic.com/ai-fluency/overview.

[3] IBM. 2024. “What Is Chain of Thoughts (CoT)?” Ibm.com. August 12, 2024. https://www.ibm.com/think/topics/chain-of-thoughts.

[4] Tu, Tao, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan [5]Chang, Andrew Carroll, et al. 2023. “Towards Generalist Biomedical AI.” ArXiv.org. July 26, 2023. https://doi.org/10.48550/arXiv.2307.14334.

[6]Benfeghoul, Martin . 2024. “When in Doubt, Think Slow: Iterative Reasoning with Latent Imagination.” Arxiv.org. 2024. https://arxiv.org/html/2402.15283v1.

[7]Harris, Dion. 2025. “AI Factories Are Redefining Data Centers and Enabling the next Era of AI.” NVIDIA Blog. March 18, 2025. https://blogs.nvidia.com/blog/ai-factory/.

[8]Hima, Dren. 2025. “The Rise of Onchain AI: Transforming Blockchain in 2025.” Coincub. June 20, 2025. https://coincub.com/onchain-ai/.

[9]Mikalef, Patrick, and Manjul Gupta. 2021. “Artificial Intelligence Capability: Conceptualization, Measurement Calibration, and Empirical Study on Its Impact on Organizational Creativity and Firm Performance.” Information & Management 58 (3): 103434. https://doi.org/10.1016/j.im.2021.103434.

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