Can biology answer questions that once belonged only to philosophy?
Alex Rosenberg argues that Darwinian biology transformed not only science but also our understanding of morality, meaning, mind, and human purpose, bringing traditionally philosophical questions into the scientific domain.
0:00 What Is the Philosophy of Biology 1:14 How Darwin Changed the Nature of Inquiry 4:27 How Philosophers Help Biologists 6:48 Biology and the Philosophy of Mind 9:43 Can Biology Answer Philosophy’s Biggest Questions.
Alexander Rosenberg is an American philosopher and novelist. He is the R. Taylor Cole Professor of Philosophy at Duke University, well known for contributions to philosophy of biology and philosophy of economics. Rosenberg describes himself as a \.
Open any device built in the past five years, look inside its memory subsystem, and the chips you find were almost certainly fabricated in one of three South Korean industrial cities — Hwaseong, Pyeongtaek, or Icheon — by one of two companies whose combined market capitalisation now exceeds $700 billion. The historical improbability of this situation is not a matter of degree but of category. Korea in 1953 did not have a semiconductor industry, a precision manufacturing tradition, an advanced engineering workforce, or the kind of capital markets that could finance industrial development. It had a per-capita income lower than essentially every other country whose subsequent economic trajectory has been studied by development economists, a primarily agricultural economy substantially destroyed by three years of active warfare, and a small population (~20 million) whose adult literacy rate stood at approximately 20 percent. The proposition that, 72 years later, two companies headquartered in the same country would manufacture the memory chips inside Apple’s iPhones, Google’s Pixel devices, Microsoft’s data centres, Nvidia’s AI accelerators, Tesla’s autonomous-driving computers, and essentially every other major piece of digital hardware sold globally — would have been considered, by any reasonable observer in 1953, structurally impossible.
The Borg were never terrifying because they had advanced technology. They were terrifying because they erased individuality itself.
As brain-computer interfaces move from science fiction into reality, humanity may be approaching a question once reserved for Star Trek: What happens when technology no longer just helps us… but changes what it means to be human?
In this video, we explore the unsettling possibility that artificial intelligence, neural implants, and human enhancement technologies could eventually create something disturbingly similar to the Borg Collective.
🔹 Brain-computer interfaces and neural implants. 🔹 Human enhancement and transhumanism. 🔹 AI integration with the human mind. 🔹 Social and economic pressure to augment. 🔹 The loss of individuality and autonomy. 🔹 Whether technological evolution can be resisted.
If humanity could become smarter, faster, stronger, and more connected than ever before… would we resist? Or would we choose to become something else?
Resistance… may not be futile, but history suggests that enhancement rarely remains optional for long.
The race toward an imminent intelligence explosion has escalated from a sci-fi thought experiment into a high-stakes global debate.
Accelerating progress across model reasoning and compute infrastructure forces a critical question: is Artificial General Intelligence already arriving?
Silicon Valley insiders frequently claim human-level AI has passed us by, though critics warn these declarations are heavily warped by financial incentives.
If an AI system successfully achieves recursive self-improvement, the resulting technological singularity could compress centuries of human progress into mere hours.
A best-case takeoff promises staggering rewards like clean fusion energy, automated economic abundance, and radical medical breakthroughs that extend human lifespans indefinitely.
Photonic chips are no longer just a lab experiment, and in this video, we break down why a new photonic NPU could become one of the biggest shifts in AI hardware, data centers, and supercomputing. Instead of using electricity and transistors like a traditional GPU, this new class of processor uses light to perform computation, opening the door to dramatically faster matrix math, far lower energy use, and almost no on-chip heat. From the growing power crisis in AI infrastructure to the limits of silicon, Moore’s Law, and the memory wall, this story explores why photonic computing is suddenly becoming one of the most important technologies to watch. If you’re interested in photonic chips, optical computing, AI chips, NPUs, GPUs, data center efficiency, and the future of semiconductor technology, this video gives you the full picture. We also explore what makes these chips different from conventional silicon. The video covers photons instead of electrons, wavelength-division multiplexing, optical interference, thin-film lithium niobate, and why companies like Q.ANT are now deploying photonic processors in real supercomputing environments instead of just talking about them on research slides. We look at Q.ANT’s Native Processing Unit at the Leibniz Supercomputing Centre in Germany, the jump from first-generation to second-generation performance, and why benchmarks showing huge gains in throughput, AI inference, and energy efficiency are making people take photonic hardware much more seriously. More importantly, this is not just another faster chip story. It is about whether the AI industry can keep scaling without running straight into an energy wall. With GPUs demanding more power, more cooling, and more data movement every year, photonic co-processors may be the first real alternative that changes the economics of compute itself. The technology still has serious challenges, especially memory and optical-electrical conversion, but this may be the moment when computing with light stopped sounding like science fiction and started becoming real infrastructure.
The generative AI boom is fueled by staggering investments (including OpenAI’s multibillion-dollar chip deals), but for many companies, profitability as a result of these investments has remained elusive, leading some economists to warn of an AI bubble. In this Q&A, Harvard Business School’s Andy Wu wades through the potential and hype of the new technology. In particular, he highlights structural challenges facing most companies and warns of inevitable expiration dates on current legacy subscription models. He says that the industry’s future will depend on sustainable economics and business models that are able to capture value.
It is awe-inspiring to reflect on the velocity of this generational shift. In an incredibly compressed timeline, AI has transitioned from a boardroom novelty into the underlying infrastructure of global enterprise labor.
We are living through a historic economic anomaly: even as raw capability scales exponentially, the unit cost of intelligence continues to plummet toward zero. The future of corporate margin expansion will not belong to those who consume the most compute, but to the strategic architects who best optimize this collapsing cost.
Yet, beneath this cognitive abundance lies a stark paradox. While token unit prices have plunged 99.7% over the last 24 months, actual enterprise AI invoices are soaring—with average budgets expanding from $1.2M to over $7M. This is the structural reality of moving from simple, episodic chatbots to multi-step, autonomous agentic workflows that incur heavy context taxes and recursive reasoning loops.
To help technology and financial leaders navigate this landscape, we just released our latest research and report: The Macroeconomics of the Hyperscale AI Market and the New Enterprise Frontier.
Stop projecting AI margins using outdated software frameworks. Read the full report at the link below to master the new rules of token economics. Let us know in the comments: Are your teams experiencing bill shock, or have you already cracked the code on dynamic model routing?
The macroeconomics of the hyperscale AI market and the new enterprise frontier.
