While the tech world obsesses over making AI bigger, faster, and more probabilistic, one professor at Northeastern University is doing something almost revolutionary: building AI that actually reasons.
David de Hilster's blockchain-powered NLP++ project isn't just another academic exercise in distributed computing. It's a methodical dismantling of everything wrong with our current approach to artificial intelligence. And frankly, it's about time someone called out the emperor's new clothes.
The Statistical Mirage We've Been Sold
The privacy landscape is fragmented and driven by state-level laws such as California's CCPA, which mandates greater transparency in AI-powered profiling and decision-making. Yet here we are, doubling down on black-box systems that even their creators can't fully explain. The "black box" problem in AI decision-making limits stakeholders' ability to understand, trust, and verify outcomes, particularly in high-stakes sectors such as healthcare, finance, and autonomous systems.
The blockchain AI market is expected to grow from USD 561.97 million in 2024 to USD 3414.68 million by 2032, at a CAGR of 25.3%. But growth in what, exactly? More sophisticated guesswork? More elaborate statistical parlor tricks?
De Hilster's approach cuts through this noise with surgical precision. His NLP++ programming language operates on logic, rules, and transparency—the very things that make human reasoning trustworthy. Deterministic AI offers clear, predictable outputs, making it ideal for highly regulated fields, such as healthcare, where auditability is essential.
What makes de Hilster's work genuinely remarkable isn't just the technical innovation—it's the philosophical rigor. Twenty-five years ago, he set out to create computers that analyze text the way humans actually think. Not through probability distributions or neural network approximations, but through actual logical reasoning.
This isn't the Silicon Valley version of "move fast and break things." This is academic precision meets real-world application. AI technologies — particularly those based on machine learning — are probabilistic systems. They rely on patterns and probabilities to make decisions rather than exact, deterministic rules. De Hilster's response? Build better rules.
AI transparency is about clearly explaining the reasoning behind the output, making the decision-making process accessible and comprehensible... it's about eliminating the black box mystery of AI and providing insight into the how and why of AI decision-making. This is exactly what blockchain-powered NLP++ delivers: a system where every decision can be traced, audited, and understood.
While crypto bros pumped digital currencies and NFTs, de Hilster quietly identified blockchain's real superpower: decentralized, transparent knowledge management. By leveraging blockchain as an immutable record-keeping system, AI decision-making can become more interpretable, fostering trust among users and regulatory compliance.
The planned NLP coin isn't just another crypto gimmick—it's an incentive system for building something we desperately need: a commons of human-level reasoning algorithms. Imagine Wikipedia, but for logical thinking, backed by blockchain's immutable ledger.
As businesses confront the complexities of dynamic global frameworks, their capacity to align innovation with governance will delineate industry leaders. Here's a professor actually doing that work, creating systems that are governance-ready by design.
Sure, Probabilistic AI is better suited for complex environments with inherent uncertainty, like speech or image recognition. But do we really want uncertain AI making hiring decisions? Medical diagnoses? Legal determinations?
In healthcare, clinical decision support systems (CDSSs) are an example of deterministic, expert AI systems. CDSSs use a set of rules and guidelines based on clinical practice guidelines to provide evidence-based recommendations. These systems work because they're built on logical foundations, not statistical approximations.
De Hilster's vision of digital assistants that "function more like a reasoning human than a predictive machine" isn't just technically superior—it's ethically necessary. When the number of AI-related incidents rose to 233 in 2024—a record high and a 56.4% increase over 2023, we need systems we can actually trust.
What sets de Hilster's approach apart isn't just the technology—it's the patient, methodical process. Twenty-five years of development. Rigorous testing. Transparent methodologies. This is how you build AI that serves humanity rather than replacing it.
AI creators can achieve transparent and trustworthy AI through disclosure. They can document and share the underlying AI algorithm's logic and reasoning, the data inputs used to train the model, the methods used for model evaluation and validation. This isn't just possible with blockchain NLP++—it's built into the architecture.
While venture capitalists chase the next unicorn and tech giants scale probabilistic systems to absurd proportions, de Hilster is building something more valuable: AI we can actually understand and trust. The blockchain isn't just a distributed ledger here—it's a foundation for distributed intelligence that remains fundamentally human.
As we face an AI future where the most expensive model for which they were able to estimate the costs was Google's Gemini 1.0 Ultra, with a breathtaking cost of about US $192 million, perhaps it's time to ask: Are we building AI that's actually intelligent, or just expensive?
De Hilster's NLP++ blockchain project suggests we can do both—but only if we're willing to prioritize reasoning over statistics, transparency over black boxes, and human-level intelligence over human-replacement algorithms.
The question isn't whether this approach will work. It's whether the rest of the AI industry has the intellectual honesty to follow suit.
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