What 81,000 People Want from Artificial Intelligence
The largest qualitative study ever conducted: Anthropic interviews half the world to understand real hopes, fears, and expectations about AI.

by Michele Laurelli
The largest qualitative study ever conducted: Anthropic interviews half the world to understand real hopes, fears, and expectations about AI.

Why Data Centers are the True Strategic Infrastructure of the 21st Century

Artificial intelligence approached with scientific rigor, engineering precision, and human depth.

If half of web traffic is generated by bots and content is produced en masse by AI, those working with artificial intelligence have only one path: to stand out from the noise.

Continuous Latent Reasoning: 16x-128x Semantic Compression and End-to-End Optimization for Next-Generation RAG Systems

2025, the year that redefined artificial intelligence.

What if a superintelligent coding AI had emerged in 2012 and declared jQuery and PHP “good enough” for all time? In this alternate history, the web never evolves. No React, no component paradigms, no open-source renaissance: only a perfect stagnation masked by machine efficiency. This essay explores how AI, when trained only to imitate the past, can freeze the future. And it asks a chilling question: what if that future isn’t fiction, but our present unfolding in slow motion?

A technical analysis of why the self-attention architecture makes modern LLMs much more than mere "stochastic parrots."

Why the real productive revolution does not come from LLMs.

Why reliability, not capability, becomes the ultimate bottleneck when AI meets the real world.

How orchestrated AI agents are transforming complex problem-solving through coordinated autonomy and specialized capabilities.

Beyond pattern matching and statistical correlation—exploring what distinguishes true intelligence from sophisticated computation, and why the question matters for how we build AI.

Why industrial automation demands AI that runs on-premise, operates without internet connectivity, and makes millisecond decisions in environments where downtime costs millions.

Building Retrieval-Augmented Generation systems that actually understand your organization's knowledge, not just find semantically similar text snippets.

The loss landscape of deep networks is high-dimensional, non-convex, and full of local minima. Yet gradient descent finds good solutions anyway. Understanding why reveals fundamental insights about deep learning.

At Algoretico, we have developed a framework that does something no Transformer does today: it develops a cognitive personality that evolves with experience.

Artificial intelligence is changing the way we search for information while eliminating the aesthetic experience of the web. For this reason, I believe we will unfortunately replace websites with empty text fields.

The Global AI Report by NTT DATA reveals a clear picture: experimentation gives way to the creation of measurable value.


What happens when your favorite AI coding assistant disappears overnight?

Digital forensics increasingly integrates artificial intelligence to automate the analysis of large volumes of data, enhancing the speed and effectiveness of investigations on texts, audio, images, videos, malware, and IoT devices.


Today, the pre-built management system is no longer just an operational limit, but a cognitive constraint that prevents the company from understanding, governing, and evolving its real complexity.

Between ideological rejection and blind automation, the real distinction lies in directing technology instead of being replaced: history, method, and awareness in the use of artificial intelligence.

Draghi's warning about European stagnation concerns not only the number of artificial intelligence models developed but also the very structure of the economy and how we use (or do not use) AI in production processes.

Apple computers are now widely used in software and AI development, but they lack certain tools that would help standardize work and facilitate migration to platforms better suited for training.

Exploring the critical importance of private AI infrastructure for organizations requiring absolute control, performance, and intellectual property ownership.

Most organizations don't have labeled datasets. They have processes, constraints, and domain expertise. Here's how to build AI systems that learn from structure, not just examples.

Why my students implement backpropagation by hand, build neural networks from NumPy, and learn to architect systems instead of calling APIs.

When AI systems trained on AI-generated content degrade over time, losing diversity and capability. Understanding the mechanics of model collapse and architectural solutions that preserve knowledge.

Creativity doesn't emerge from unlimited freedom—it emerges from intelligent navigation of constraints. What this means for building AI systems that generate novel solutions.
