The Death of "True/False"
From deterministic code to the era of the "Maybe" Machine.
“We are trading the binary certainty of code for the statistical likelihood of AI. We aren’t just changing the tools; we are changing the very nature of ‘truth’ in our systems.” — Nadina D. Lisbon
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We are witnessing the quiet end of the ‘Binary Era.’ For decades, computers were trusted because they were exact, relying on 1s and 0s, True or False. But this week, three massive signals from banking, government, and academia suggest we are moving into a ‘Probabilistic Era.’ We are now integrating systems that deal not in facts, but in likelihoods, into the rigid structures of our lives. As MIT students flee traditional coding for ‘Decision Making’ and the FDA deploys ‘reasoning’ agents, the question is no longer ‘Did the code execute correctly?’ The question is now: ‘Do we accept the odds on this decision?’
3 Tech Bites
🏦 HSBC’s Local “Reasoning” Engine
HSBC isn’t just chatting with AI; they are embedding Mistral’s models directly into their infrastructure to handle sensitive workflows like credit analysis and fraud detection. By self-hosting, they solve the privacy issue, but they introduce a new dynamic: banking decisions influenced by generative probability rather than just rigid arithmetic rules [1].
🏛️ The FDA’s “Agentic” Shift
The FDA is deploying ‘agentic AI’ systems capable of planning and executing multi-step tasks to accelerate operations. This moves beyond summarizing text to actively reasoning through regulatory workflows. While ‘human-in-the-loop’ remains the standard, the introduction of autonomous agents into public health decisions marks a massive shift from passive tools to active participants [3].
🎓 MIT Students Bet on “Decision Making”
The most telling signal comes from the students. At MIT, the “Artificial Intelligence and Decision Making” major is exploding, while traditional Computer Science stabilizes. Students realize that the future value isn’t in writing the deterministic code (which AI can do), but in mastering the probabilistic “decision making” required to manage intelligent systems [2].
5-Minute Strategy
🧠 The “Confidence Interval” Check
In a probabilistic world, you must stop treating AI outputs as “answers” and start treating them as “wagers.” Use this quick mental audit for any AI-assisted task:
Identify the “Black Box”: Look at a result provided by AI
(e.g., a drafted email, a data summary, or a code snippet).
Assign a Confidence Score: Ask yourself, “If I had to bet my salary on this being 100% correct, would I?”(AI is rarely 100%; it operates on probability).
Find the “Hallucination Gap”: specifically look for the logic jump. Where did the AI make an assumption to connect point A to point B?
The Human Verification: Your job is no longer to do the work; it is to verify the assumption found in step 3.
1 Big Idea
💡 The Rise of Synthetic Bureaucracy
We typically hate bureaucracy because it is slow. Yet we trust it because it is accountable. If a loan officer denies you, you can argue with them. If a regulator blocks a drug, they must cite a specific law.
But we are currently blending the “Agentic” trends at the FDA with the “Probabilistic” nature of AI. This is creating a new phenomenon: Synthetic Bureaucracy.
This creates a profound tension. Traditional software is deterministic. If you input X, you always get Y. It is binary and auditable. AI, however, is probabilistic. If you input X, you probably get Y based on a statistical model of billions of data points.
When HSBC uses AI for fraud detection or the FDA uses agents for workflows, we are injecting a “degree of uncertainty” into systems that demand absolute precision.
The students at MIT seem to be the first to truly internalize this. Their flight from standard Computer Science to “Decision Making” is an admission that the era of “pure code” is ending. They are preparing for a career where the skill isn’t telling the machine exactly what to do using syntax. Instead, they are learning to navigate the gray areas of what the machine decides to do based on probability. They are not learning to be builders. They are learning to be risk managers.
The danger of Synthetic Bureaucracy is that it offers the illusion of objectivity. An AI agent doesn’t have “bad days,” but it has bias baked into its weights.
We must understand that these systems are guessing. They might be guessing extremely accurately, but they are still guessing. If we allow them to handle our governance and banking without that understanding, we risk building a society where decisions are made not by laws or logic, but by the opaque “weights” of a neural network.
Ultimately, the human role in this new era is to be the “Chief Certainty Officer.” We must be the ones who look at the probabilistic output of an agent and decide if the risk is acceptable. We must ensure that while the bureaucracy becomes synthetic, the accountability remains human.
If an AI agent denied your loan application with 99% confidence, would you accept it? Or would you demand to speak to a human who might be less accurate but more empathetic? Reply and tell me!
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Cheers,
Nadina
Host of TechSips with Nadina | Chief Strategy Architect ☕️🍵