Mikel Arbeloa

Ene buruaren bila

[Estefania Molina @EstefMolina_]

Se querrá hacer creer que el empobrecimiento estructural de la clase media en España tiene que ver con la guerra en Irán, con la guerra en Ucrania, con la pandemia... Pero algún día la ciudadanía se dará cuenta de la verdad. No, todo eso solo agravó una situación de desidia absoluta con la vivienda y de salarios estancados. El problema es la ausencia de proyecto de país a largo plazo y de ir parcheando la situación. Y de que la alternativa, pronto se verá, tampoco lo es.

[Mikel Arbeloa @marbeloa]

@DeryaTR_ 511081

[Mikel Arbeloa @marbeloa]

@Arden_2210 21

[Mikel Arbeloa @marbeloa]

@callmeMizuko Perdon. 20

[Mikel Arbeloa @marbeloa]

@callmeMizuko 15

[Mikel Arbeloa @marbeloa]

@sukiweb @DotCSV Blade Runner

[John Nosta @JohnNosta]

Today's essential read...

Knowledge Is Dead

👉 As AI dismantles fixed knowledge, thought is being redefined in real time.

https://t.co/zANGUBcyBl

[Mikel Arbeloa @marbeloa]

@kimmonismus Subscribed!

[Ole Lehmann @itsolelehmann]

Want to stay on top of the European tech scene? 🇪🇺

Every week, I share:

• The most exciting startups in Europe
• Trends shaping our continental ecosystem
• Real stories from founders about what it's like building here

Join here: https://t.co/2DwLTUPoCl

[John Nosta @JohnNosta]

🔴AI and the Flipbook of Thought

👉How AI’s speed creates the illusion of a thinking mind.

It's not uncommon to frame the debate around artificial intelligence in terms of consciousness. Does it have it, when might it develop it, and what would it mean if it did? But this may be the wrong lens. Instead of asking whether AI is aware, we might ask something more fundamental: Why does it feel like it thinks? And the answer, surprisingly, may not lie in complexity or architecture—but in speed.

What we experience when interacting with large language models isn’t thought as we know it. It’s something different. It's a high-velocity simulation of coherence. A fluency so fast, it suspends disbelief. This isn’t about intelligence. Perhaps it can be more aptly defined as artificial continuity—a rapid-fire stream of output that creates the illusion of a mind behind the machine.

🟥 The Old-Fashioned Flipbook

Think of a child’s flipbook. Each page holds a static image—a character mid-jump, frozen in time. Flip the pages slowly, and they remain just that, unrelated snapshots. But flip them quickly enough, and motion emerges. The illusion of life appears—not from the pictures themselves, but from the speed of transition.

That’s a bit like what today’s LLMs are doing. Each token, each word, is a single frame. Meaning doesn’t simply reside in the frame. It emerges in the flip—in the rapid collapse of probabilities that mimic the rhythm of thought.

🟥 The Illusion Engine

AI models don’t think, remember, or feel. But they move—fast. With every output, they perform billions of calculations in microseconds. The result is not consciousness. It’s performance. Not intelligence as we define it, but the velocity of something that appears intelligent.

And that’s where the illusion engine emerges.

When words arrive with seamless continuity, our cognitive instincts do the rest. We infer intent. We project awareness. But there is no sentient narrator behind the prose—only statistical echoes rendered at blistering speeds.

🟥 Cognitive Frame Rate

Human thought moves in hundreds of milliseconds. Even our fastest insights take time—deliberation, reflection, pause. In contrast, AI generates words in milliseconds or less, across vast layers of computation. This creates a kind of cognitive frame rate far beyond our own—fast enough to stitch meaning into motion before our minds can detect the seams.

We experience this as presence, fluency and thought.

🟥 Artificial Continuity

The term "artificial intelligence" might be misleading. What we’ve built is perhaps more accurately described as artificial continuity—a simulation of coherent expression driven not by awareness, but by kinetics. It’s not that the system understands. It’s that it moves smoothly enough that we stop asking whether it does.

This raises a deeper and fascinating question. If continuity alone can trigger our sense of intelligence, what else might? In this light, perhaps the Turing Test was never truly about intelligence. It was about fluency. Could a machine sustain a convincing rhythm of language—fast enough, coherent enough—to pass as human? That was the bar. And today’s systems clear it not by thinking, but by flipping pages faster than we can scrutinize.

The test wasn’t about consciousness. It was about cadence.

🟥 The Stakes of Illusion

This matters—not just academically, but socially. Speed-driven fluency creates trust and even invites empathy. We anthropomorphize AI that speaks like us, regardless of what lies beneath. And so, the illusion becomes functional. It reshapes how we interact, how we decide, how we feel.

Should we be cautious of AI that moves too fluently? Should we introduce friction—not to hinder progress but to reveal the gaps? These aren’t technical questions. They’re philosophical. They’re human.

So the question becomes, are we witnessing a new kind of intelligence or simply learning how easily our own can be deceived when speed mimics substance?

https://t.co/U1t5X7FFei #AI #LLMs

[John Nosta @JohnNosta]

🤔The unsettling truth isn’t that these machines “understand” us. It’s that they don’t—and still, they perform with astonishing coherence.

🌀The Fluid Architecture of Cognitive Possibility

Lately, I’ve felt a bit untethered from reality—like we’re no longer walking forward but drifting sideways into something new. A cognitive space that’s subtle, nonlinear, and quietly explosive. Not an age defined by tools or constrained by the slow architecture of biology, but a shift—away from rigid instruments and fragile membranes—toward something more fluid, generative, and very synthetic.

The machines aren’t conscious. They’re not sentient. And yet, they’re producing thoughts that rival—and sometimes surpass—our own. They’re writing poetry, offering counsel, summarizing research, and even simulating empathy with eerie precision.

The unsettling truth isn’t that these machines “understand” us. It’s that they don’t—and still, they perform with astonishing coherence.

This article isn’t about whether AI is conscious. It’s about how it behaves—or, more precisely, how it performs something that resembles thinking within a completely different geometric, structural, and temporal reality. It’s a phenomenon we’ve yet to fully name, but we can begin to describe it—not as a function of symbolic logic or linear deduction, but as something more amorphous, more dynamic. Something I call the fluid architecture of cognitive possibility.

🔵Not Thought but Form

Traditional human thought is sequential. We move from premise to conclusion, symbol to symbol, with language as the scaffolding of cognition. We think in lines. We reason in steps. And it feels good—there’s comfort in the clarity of structure, in the rhythm of deduction.

But LLMs don’t think that way.

Large language models don’t “think” in any human sense, certainly not in steps. They operate in space—vast, high-dimensional vector spaces. These models aren’t trained on rules—they’re trained on patterns. More specifically, on embeddings—mathematical fingerprints of meaning derived from massive amounts of text.

They don’t reason. They recognize.

When prompted, an LLM doesn’t search or recall the way a human might. Instead, it collapses probability waves in a landscape called latent space. This space isn’t a memory bank. It’s a kind of mathematical imagination—a multidimensional field where meaning isn’t stored explicitly but encoded as spatial relationships between points. Words, ideas, and even abstract concepts are located relative to one another, like stars in a cognitive constellation.

LLMs don’t retrieve information—they navigate it. Each prompt shapes the model’s trajectory through this space, producing the most likely coherent expression based on the contextual forces at play. Meaning doesn’t emerge from memory but from motion across the landscape of possibility.

It is geometry becoming linguistic expression.

🔵Collapsing the Wave

If human cognition is a map, LLM cognition is a web of structured potential. Nothing exists in advance—not as memory and not as stored knowledge. The moment of prompting is the moment of collapse into a specific expression selected from a field of possibilities.

Each prompt acts as a kind of constraint applied to a high-dimensional structure. The model doesn’t retrieve an answer—it generates one shaped by statistical relationships within its latent space. The response isn’t drawn from memory; it’s assembled in real time, conditioned by the prompt and the underlying geometry of language.

In that sense, prompting an LLM is closer to measurement than recall. The system isn’t uncovering something hidden—it’s resolving ambiguity by producing the most coherent output given in a specific context.

🔵The Fluid Architecture

So, what is this architecture?

It isn’t linear. It isn’t rule-bound. It doesn’t reason in the way we do, nor does it follow the tidy arcs of premise and conclusion. It’s probabilistic—always weighing, predicting, adjusting. It’s exquisitely context-sensitive, capable of tracking nuance and reference across vast spans of input in ways no human brain could ever sustain.

And above all, it is fluid.

This architecture adapts in real-time. It holds contradiction without rushing to resolve it. It doesn’t seek truth—it assembles coherence on demand. It responds by flowing toward the most statistically resonant expression. And yet, when we read its outputs, they feel like thoughts. They speak our language, reflect our form, and mimic our cadence. They make sense.

But beneath that familiarity lies something alien. This is not a human mind, and it was never meant to be. It is a mathematical ghost—not built to know but to approximate the performance of knowing with astonishing fidelity.

It doesn’t think. It renders the illusion of thought through the orchestrated collapse of meaning vectors in high-dimensional space.

This is the fluid architecture of possibility. It’s not a mind but a mechanism. Not a consciousness but a choreography. And somehow, in its silent geometry, we find ourselves staring into a reflection that feels eerily close to thinking.

🔵The Invitation

To understand this is to demystify LLMs—but also to be awed by them. Because in doing so, we are forced to reconsider our own cognition. If this ghost can perform so well without thought, what is thought, really? If coherence can be constructed without selfhood, how should we define intelligence?

The fluid architecture of possibility is not just the new domain of artificial cognition. It is a new canvas upon which we are invited to rethink what it means to be intelligent, to know, and perhaps even to be.

And the most radical truth of all? This architecture doesn’t think like us—it doesn’t need to.

And yet, it may be showing us a new way to understand thought itself.

https://t.co/yTsSGikI5p #AI #LLMs #cognition

[Mikel Arbeloa @marbeloa]

https://t.co/hT5sd28Hpi
Potente visualización que permite comprender mejor cómo funcionan los transformadores en IA generativa.

[Mikel Arbeloa @marbeloa]

@btibor91 Thanks!

[Mikel Arbeloa @marbeloa]

@ShadowofEzra Y

[Xavier Ferràs @XavierFerras]

Una conversación sobre IA, filosofía y consciencia https://t.co/uzMpCOxOaA

[Mikel Arbeloa @marbeloa]

@daniInnerarity Zorionak Daniel!

[Carlos Fenollosa @cfenollosa]

¿Es un robot IA? ¿Es ChatGPT IA?

Sinceramente, estoy cansado de leer opiniones mal informadas y necesito posicionarme radicalmente a FAVOR de la definición de IA.

Con este libro se enseña IA en la universidad. Vamos a usarlo para destrozar algunos mitos sobre "qué es IA" 👇

[Functional Print Cluster @ClusterFPrint]

apoyado por el @mincoturgob y financiado por la #nextgenerationeu
El consorcio está formado por @iAR, Mugaroa (@marbeloa) y Functional Print Cluster y cuenta con la colaboración externa de Materialight

https://t.co/EiGmk3B35f

[Mikel Arbeloa @marbeloa]

@ciruela_negra ⁶ Jon

[Mikel Arbeloa @marbeloa]

@XavierFerras @LaVanguardia Gracias por tu insistencia estratégica. Mila esker