When NVIDIA Says Jump, Should We Ask “How High” or “Why?”

The Paradox of Predicting AI’s Future: Why “Small Language Models Are the Future” Might Be History Repeating Itself

In June 2025, a team of NVIDIA researchers dropped a provocative paper with an audacious title: “Small Language Models are the Future of Agentic AI.” Not “could be,” not “might become,” but definitively “are.” This deterministic claim about an uncertain technological future immediately caught my attention—not for its technical arguments, but for how perfectly it exemplifies the logical paradoxes and failed prediction patterns that have plagued technological forecasting for decades.

Using the INGA314 to dissect this paper reveals a fascinating case study in how even brilliant technologists fall into the same rhetorical traps that have characterized failed predictions from expert systems to blockchain. More intriguingly, when we cross-reference these patterns with Thomas Kuhn’s theory of scientific revolutions and Clayton Christensen’s disruption framework, we discover that the paper’s very certainty might be its greatest weakness.

The Anatomy of a Technological Paradox

Let’s start with the paper’s central paradox: it simultaneously claims that Small Language Models (SLMs) are already “sufficiently powerful” for agentic tasks while arguing they will dominate in the future. If they’re already sufficient, why aren’t they already dominant? This temporal inconsistency—what the LAF’s Temporal-Epistemic Logic extension flags as a “future certainty paradox”—represents an inappropriately high confidence about future events, a classic marker of flawed technological predictions.

The paradoxes multiply upon closer inspection:

The Capability Contradiction: The authors argue that “capability—not parameter count—is the binding constraint,” yet their entire argument rests on the size distinction between SLMs and LLMs. If capability is what matters, why does size?

The Specialization-Generalization Paradox: SLMs are praised for excelling at “narrow, specialized tasks” while simultaneously being proposed as the general solution for agentic AI. The paper even advocates for “heterogeneous agentic systems” using multiple models—undermining its own thesis about SLM dominance.

The Economic Certainty-Uncertainty Paradox: After building an elaborate economic argument for SLM inevitability, the authors admit “the jury is still out” on whether LLMs might be more cost-effective due to centralization. This acknowledgment of uncertainty directly contradicts the paper’s deterministic title.

Survivorship Bias in Silicon Valley

The LAF’s Wald Extension, designed to detect survivorship bias, lights up like a Christmas tree when analyzing this paper. The authors present a parade of successful SLMs—Microsoft’s Phi series, NVIDIA’s Nemotron, DeepSeek’s models—while conspicuously absent are the failed attempts, discontinued projects, or cases where SLMs underperformed.

This selective presentation follows Abraham Wald’s classic insight about bomber armor: we only see the planes that made it back. Similarly, we only hear about the SLMs that succeeded. The paper’s missing perspective—the graveyard of failed efficiency-focused AI projects—might tell a very different story about the viability of the “smaller is better” thesis.

When Kuhn Meets AI: Why Paradigm Shifts Can’t Be Predicted

Cross-referencing the paper’s claims with Thomas Kuhn’s theory of scientific revolutions reveals a fundamental misunderstanding of how technological paradigms actually shift. Kuhn’s key insight was that revolutions are “eliminative and permissive rather than instructive”—nature tells us what doesn’t work but doesn’t guide us to specific solutions.

The SLM paper treats the potential shift as “normal science,” simply solving existing problems more efficiently. But Kuhn showed that genuine paradigm shifts involve fundamental reconceptualizations that make current problem formulations obsolete. The experts working within a paradigm are often the least equipped to predict its successor because they’re trapped within its conceptual framework.

Consider AI’s own history: The expert systems boom of the 1980s saw researchers claiming with absolute certainty that rule-based systems were “the future of AI.” Over $1 billion was invested based on this certainty. The result? Complete commercial failure and the second AI winter. The experts didn’t predict machine learning’s rise because it required abandoning their entire problem formulation.

The Disruption That Isn’t: Why SLMs Don’t Fit Christensen’s Model

Applying Clayton Christensen’s disruption framework reveals another problem: SLMs don’t actually fit the disruptive innovation pattern. Classic disruption involves technologies that start inferior on all traditional metrics but offer new benefits that eventually allow them to overtake incumbents. Think digital cameras starting with terrible quality but offering instant sharing.

SLMs, however, are superior to LLMs on multiple traditional metrics from day one: speed, energy efficiency, deployment flexibility, and cost. They’re more like a sports car claiming to disrupt trucks—different tools for different jobs, not a disruptive trajectory.

More tellingly, SLMs are being developed by the same companies creating LLMs. Google has Gemini Nano alongside Gemini Ultra. Microsoft has Phi alongside GPT partnerships. This is the opposite of disruption theory, where resource-constrained newcomers challenge complacent incumbents. The incumbents are hedging their bets, not being disrupted.

The Graveyard of Deterministic Predictions

Historical analysis reveals that phrases like “X is the future” combined with technical inevitability arguments have an abysmal track record:

1960s: “Picturephones are the future of communication” (AT&T invested $500 million; complete failure)
1980s: “Expert systems are the future of AI” (300+ companies failed)
1990s: “Japan’s Fifth Generation Computing is the future” ($400 million invested; zero commercial impact)
2000s: “Hydrogen fuel cells are the future of transportation” (Still waiting…)
2010s: “Blockchain is the future of everything” (99% of projects failed)
2020s: “The metaverse is the future of the internet” (Meta lost $50 billion and counting)

Each case exhibited the same logical paradoxes: certainty about uncertainty, survivorship bias in evidence selection, and extrapolation from narrow success to universal application.

The Sociology of Self-Fulfilling Prophecies

Understanding why smart people make such deterministic claims requires examining the sociology of technological expectations. These predictions serve performative rather than descriptive functions—they’re not trying to accurately forecast the future but to create it through:

Resource Mobilization: Attracting funding for SLM development
Network Alignment: Coordinating researchers around shared priorities
Legitimacy Construction: Establishing thought leadership positions
Market Signaling: Influencing competitor and customer behavior

The deterministic language serves these coordinating functions even when everyone knows the future is uncertain. It’s a game where all players benefit from playing along—until reality intervenes.

Path Dependency: Why Better Doesn’t Always Win

Economic history is littered with “superior” technologies that lost to “inferior” ones due to path dependency:

VHS beat Betamax despite worse quality
QWERTY keyboards persist despite inefficiency
JavaScript dominates despite being designed in 10 days
x86 architecture conquered ARM’s technical superiority for decades

The AI industry already shows massive path dependency effects favoring LLMs:

Billions invested in LLM infrastructure
Entire developer ecosystems built around LLM APIs
Enterprise commitments to LLM vendors
Training data formatted for large models

These create switching costs that efficiency advantages rarely overcome. The paper acknowledges this obliquely when discussing the “$57 billion investment” in centralized infrastructure but then hand-waves it away.

The Real Future: Messy, Uncertain, and Plural

So what does this analysis suggest about the actual future of agentic AI? Not the clean deterministic story of SLM dominance, but something messier:

Market Segmentation: Different model sizes for different tasks, with no clear “winner”
Hybrid Systems: Exactly what the paper suggests while contradicting its own thesis
Path-Dependent Lock-in: Continued LLM dominance in many areas due to ecosystem effects
Unexpected Innovations: Solutions that sidestep the entire size debate

The paper’s value lies not in its predictive accuracy but in coordinating current action. By declaring “SLMs are the future,” NVIDIA helps create conditions where that might become true—a self-fulfilling prophecy if enough actors buy in.

Lessons for Evaluating AI Predictions

This analysis suggests several heuristics for evaluating future AI predictions:

Beware Deterministic Language: The more certain the prediction, the more likely it’s wrong
Look for Acknowledged Uncertainty: Good predictions embrace complexity
Check for Survivorship Bias: What failures are being ignored?
Consider Path Dependency: Technical superiority rarely determines outcomes
Examine Incentives: Who benefits from this prediction being believed?

The Meta-Paradox of Prediction

Perhaps the deepest irony is that by trying to predict the future with certainty, we reveal our fundamental inability to do so. The SLM paper joins a long tradition of brilliant people making confident predictions that history judges harshly—not because they were stupid, but because technological evolution is genuinely unpredictable.

The paper’s logical paradoxes and deterministic claims don’t diminish its authors’ intelligence or the value of their technical contributions. Instead, they remind us that even experts fall into rhetorical patterns that prioritize persuasion over prediction. Understanding these patterns helps us navigate the hype cycles and extract genuine insights while maintaining appropriate skepticism.

In the end, the future of agentic AI—like all technological futures—will emerge from the complex interaction of technical possibilities, economic incentives, social coordination, and historical accidents. It will likely surprise us all, including (especially?) those claiming to know it with certainty.

The only prediction we can make with confidence? In 2035, we’ll look back at 2025’s confident predictions with the same bemused hindsight we now apply to picturephones and expert systems. And somewhere, someone will be writing equally certain predictions about whatever comes next, continuing the eternal cycle of technological prophecy and human fallibility.

What do you think? Are we doomed to repeat these prediction patterns forever, or can understanding them help us do better? Share your thoughts and your favorite failed tech predictions in the comments below.