jueves, 16 de julio de 2026

Passport to Magonia, by Jacques Vallée (2014)

The Country Where Fairies Arrive in Flying Saucers

Some books age badly because they were wrong, and some books age strangely because they were right about the wrong question. *Passport to Magonia* (1969), by the French astronomer and ufologist Jacques Vallée, belongs to this second, more uncomfortable category. It did not prove that extraterrestrials exist. Nor did it prove that fairies exist. What it did prove, with an erudition that still startles, is that the narrative machinery through which human beings process the inexplicable has barely changed in a thousand years — and that perhaps we should worry less about the content of our visions and more about their insistently repeated form.

The book's central argument, in its simplest formulation, is almost a provocation for the dinner table: modern accounts of UFO encounters — lights in the sky, small beings descending from a craft, missing time, veiled warnings about humanity's fate — share a strikingly identical architecture with medieval and Renaissance legends of fairies, goblins, "little people," and the aerial kingdom of Magonia that Carolingian peasants spoke of, and that a bishop, Agobard of Lyon, was already mocking in the ninth century. Vallée does not merely point out the parallel as a folkloric curiosity; he turns it into the engine of a hypothesis uncomfortable for both sides of the ufological debate: if the phenomenon were simply craft from another planet, why does it behave, generation after generation, exactly as the spirits of the forest once behaved?

It is worth saying immediately what this book is not. It is not a work of skepticism in the sense Carl Sagan or Philip Klass understood the word: Vallée has no interest in explaining sightings away as misidentified weather balloons, and in fact devotes considerable pages to dismissing that reductive reading as insufficient. Nor, however, is it a believer's tract in the mold of 1950s contactee ufology, with its Venusian messiahs and messages of cosmic peace. Vallée, who worked as an astronomer and would later advise American intelligence projects on unidentified aerial phenomena, writes from a third position, more unsettling than either of the other two: that the phenomenon is real as a recurring experience, but that its explanation is probably neither "it's an airplane" nor "they're Martians" — but something for which our twentieth-century conceptual vocabulary, neatly divided between science and superstition, simply lacks the tools to name.

The book's structure mirrors this hybrid ambition, and, it must be said, at times turns it into a strange reading object. The first half builds the comparative scaffolding: chapters that dismantle, one by one, the tropes of the ufological encounter — the abduction, the moral warning, time that stops or accelerates, the small luminous beings, the witness's inability to speak afterward — and find them near-exact twins in Robert Kirk's *Secret Commonwealth*, in chronicles of fairy abductions collected across Scotland and Ireland, in Celtic mythology, and even in certain Mayan and Japanese traditions that Vallée brings in with a breadth that occasionally verges on accumulation for its own sake. This is where the book is most persuasive and also most vulnerable: the folkloric comparison is genuinely illuminating, but the attentive reader will notice that Vallée rarely pauses to ask whether structural similarity between two kinds of story proves a shared underlying cause or, more modestly, something about how the human imagination — with no help beyond its own grammar — tends to manufacture visitors from elsewhere whenever it needs to explain the inexplicable. The book assumes the former when the material, rigorously, only warrants the latter.

The second half changes register almost entirely, and this is where *Passport to Magonia* becomes, quite literally, an archive: roughly one hundred fifty pages of a chronological catalogue of UFO landings and encounters, year by year, from the mid-nineteenth century through 1968, followed by listings of press references and specialized publications of the period. It is an almost obsessive gesture of documentary accumulation — Vallée himself would later acknowledge, in the preface he added to the 1990s edition, how arduous it was to gather thousands of testimonies without the databases we now take for granted — and it functions, depending on how one looks at it, as either the book's greatest strength or its heaviest ballast. As a strength, because no one can accuse Vallée of building his theory on stray anecdotes: here is the raw material, laid out with relatively little interpretive filtering, inviting verification. As ballast, because a list of a thousand cases without a clear critical hierarchy ends up resembling noise more than evidence, and the reader who came looking for an argument suddenly finds himself leafing through a compendium.

Where the book does reach genuine intellectual altitude is in its final chapter, "Nurslings of Immortality," and particularly in its closing sections, where Vallée abandons cataloguing mode and allows himself to speculate with a candor rare in the ufological literature of his time. There he proposes something that anticipates, by decades, discussions now familiar from philosophy of mind and belief studies: that perhaps the UFO phenomenon — like the fairy phenomenon before it — is not a fact about the external world awaiting verification, but a cultural mechanism with a function of its own, something he calls, in a memorable phrase, "the functioning lie": a belief system that perpetuates itself precisely because it is literally false and operatively true, because it performs a psychological and social task that neither science nor official religion fully manages to perform. It is an idea that brushes against social constructivism without quite surrendering to it, and one Vallée never fully develops with the rigor it deserves — perhaps because he senses, correctly, that following it to its logical end would render irrelevant much of the catalogue he has just spent one hundred fifty pages assembling.

That, it seems to me, is the unresolved tension defining this book, and it explains both its enduring influence and its evident limitations. Vallée wants, simultaneously, to treat sightings as an objective phenomenon amenable to scientific cataloguing and to suggest that the right question is not "what are they, objectively?" but "what function do they serve in the human psyche, and why does that function always choose the same narrative shape?" These are two different projects, and the book never fully decides which one it belongs to. A reader arriving expecting ufology will find too much epistemological speculation; a reader arriving expecting philosophy of belief will find too much catalogue. And yet it is precisely that generic discomfort that has kept the book cited more than half a century later, long after nearly all the contactee literature of its era fell into well-deserved obscurity.

It is worth situating the book in its moment. Published at the height of the American counterculture, a year before Erich von Däniken popularized the "ancient astronauts" hypothesis in its cruder, more commercially successful form with *Chariots of the Gods?*, *Passport to Magonia* stands as almost the intellectual reverse of that publishing phenomenon: where von Däniken flattened human history to fit a narrative of spacefaring visitors, Vallée complicated the contemporary UFO phenomenon to the point of near-unrecognizability, dissolving it into a millennia-old tradition of encounters with the other that no simple extraterrestrial hypothesis could fully explain. It is no surprise that Vallée is said to have been one of the inspirations for the French investigator character in Spielberg's *Close Encounters of the Third Kind*: there is, in his prose, the same mixture of scientific rigor and genuinely humanist wonder that Truffaut brought to the screen.

Nearly sixty years after its publication, what endures of *Passport to Magonia* is not its catalogue of landings — today an almost archaeological document of a pre-internet age when compiling a thousand testimonies was a feat of archival labor — but its uncomfortable question, posed before almost anyone else asked it: why do the visions change costume from century to century but never change script? Vallée did not resolve that question, and it is doubtful any book could. But he had the intellectual honesty, uncommon then and now, to refuse pretending that the easy answer — whether the skeptic's or the believer's — was enough. In an age saturated with instant certainties about phenomena that deserve none, that deliberate discomfort remains, oddly, the most valuable thing the book has to offer.

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miércoles, 15 de julio de 2026

The future looks bright for biotech From Money Week Magazine

The future looks bright for biotech

The article, written by David Prosser, offers an optimistic yet nuanced view of the biotechnology sector, arguing that, after a difficult period, current conditions make it an attractive time to invest. The analysis focuses on strong fundamental tailwinds, the resurgence of mergers and acquisitions (M&A) activity, and the reasons why investors should consider this sector through collective investment funds.
 

A Sector in Recovery

The article opens on a positive note, highlighting that the International Biotechnology Trust, an investment trust, has seen six of the companies in its portfolio acquired at a premium so far this year. The most recent, the oncology research firm Nuvalent, was bought by GSK for 40% more than its market price. This uptick in M&A activity is a key indicator that sentiment in the sector is improving after a period of gloom.


In recent years, biotechnology suffered from investor risk aversion, volatility, and rising interest rates, which make promises of future profits less attractive. However, the tide is turning. "The outlook is looking increasingly constructive," says Jo Groves, an analyst at Kepler Trust Intelligence, summarising the new market sentiment.
 

Strong Sector Fundamentals

The article outlines several structural factors that make biotechnology a high-growth sector:

Unstoppable Demographic Demand: The need for new medical treatments is huge and growing. The aging of the global population, with the UN projecting that the number of people over 65 will rise from 800 million in 2024 to 2 billion by 2067, is a fundamental driver. Precedence Research forecasts average annual growth of 4% over the next decade, which would expand the market from its current $1.8 trillion to $6.3 trillion by 2035.

    Accelerated Innovation: Biotech companies are developing ever more sophisticated treatments, even for the most complex diseases. The use of AI to accelerate drug discovery and tackle targets previously considered "undruggable" is highlighted. Gene therapy, cell therapy, RNA-based treatments, and gene editing are also mentioned as key areas of progress.

    The "Patent Cliff": Large pharmaceutical companies are losing exclusivity on their most profitable drugs. It is estimated that between now and 2030, the industry will lose patent rights on drugs generating $230 billion in annual revenue. To compensate for this loss, large companies are forced to acquire smaller, innovative biotech firms that have promising treatments in their pipelines. This creates a favourable environment for acquisitions.

The Rise of Mergers and Acquisitions (M&A)

M&A activity is a central pillar of the article's investment thesis. Not only does it provide a lucrative exit for investors, but it also validates the technology and potential of smaller companies. Beyond the "patent cliff", other drivers of M&A include:

    The need for large pharma companies to diversify their portfolios and enter new therapeutic areas.

    The accumulation of capital to make acquisitions during a period of lower deal activity.

    An apparently more laissez-faire regulatory stance from the US government, which facilitates transactions.

The Transformative Role of AI

A recurring theme is how AI is revolutionising the R&D process. Researchers can analyse complex biological and clinical datasets in a "more efficient and robust" way, identifying promising therapeutic targets. This, combined with the development of new treatment modalities, means that future medicines will be "more precise and have greater impact".

Advances are mentioned in areas that were previously challenging, such as Alzheimer's disease, where the industry continues its efforts with hopes of a breakthrough in the next decade. Mental health is also addressed, noting that "around a third of the 300 million people living with depression globally don't respond adequately to existing antidepressants", and that "psychedelic-derived medicines are starting to change that".
 

Risks and Valuation

The article does not omit the risks. Investing in biotechnology carries high risk, as companies are often small and dependent on the success of a few projects. Clinical trials can fail, leaving the company without products to sell. Furthermore, regulatory and price-control policies, especially in the US, can significantly affect the sector.

Despite this, the article maintains that aggregate sector valuations are "reasonable" by historical standards. The MSCI World Biotechnology index has risen more than 15% over the past year, but this performance has been eclipsed by the tech boom, suggesting there is still room for growth.
The Investment Strategy: Funds vs. Individual Stocks

The article concludes with practical advice for investors: it is preferable to invest in the sector through a collective investment fund rather than buying individual stocks. The main reason is the complexity of the science. Most investors do not have the necessary expertise to assess the potential of a drug in development or the pipeline of a small biotech company.

Investment trusts (closed-ended funds) are particularly recommended over open-ended funds, because biotechnology is an illiquid sector prone to swings in market sentiment. The closed-ended structure of a trust protects the investor from being forced to sell during panic moments.

The article mentions the best-performing trusts in the sector, including the Biotech Growth Trust (LSE: BIOG), with a 103% return over the past year; the RTW Biotech Opportunities Trust (LSE: RTW), up 93%; and the International Biotechnology Trust (LSE: IBT), which has returned 83%. Their ability to benefit from M&A activity is highlighted, thanks to their teams with "deep scientific and medical expertise".

In summary, "The future looks bright for biotech" presents a compelling case for investing in a sector that is recovering from a difficult period. Demographic and innovation fundamentals, combined with a favourable environment for M&A and the boost from AI, paint a promising picture. For investors, the best way to capitalise on this trend is through specialist funds that can navigate the complexity and risk of the sector.

Selling the Shovels in the AI Gold Rush

Selling the Shovels in the AI Gold Rush: Where the Real Fortunes Are Being Built

Introduction: Lessons from the California Gold Rush

History often repeats itself, not in identical form, but through familiar economic patterns. During the California Gold Rush of 1848–1855, more than 300,000 prospectors flocked westward hoping to strike it rich. While a handful discovered extraordinary wealth, the overwhelming majority earned little or even lost their savings. Yet a different class of entrepreneurs quietly accumulated lasting fortunes. They were the merchants who sold shovels, pickaxes, boots, tents, food, transportation, banking services, and other essentials that every gold seeker required.

Levi Strauss built an enduring business selling durable clothing. Samuel Brannan became one of California's wealthiest men by purchasing mining supplies and reselling them at significant profits before searching for gold himself. Wells Fargo established financial and transportation services that became indispensable to miners and merchants alike.

Today, artificial intelligence (AI) represents a new gold rush. Investors, entrepreneurs, governments, and corporations are racing to develop revolutionary AI applications. Every week introduces another promising startup, another impressive language model, or another billion-dollar investment announcement.

But the central strategic question remains remarkably similar:

Who is selling the shovels?

The answer reveals where much of the durable, long-term value is likely to be created.

 

Understanding the AI Value Chain

Unlike previous software revolutions, modern AI depends upon a highly interconnected ecosystem. Every chatbot, autonomous agent, image generator, scientific discovery platform, or intelligent robot relies on multiple layers of infrastructure.

These layers resemble the supply chain that supported nineteenth-century gold miners. Instead of mining equipment, today's essential tools include semiconductor fabrication, cloud computing, data centers, software frameworks, specialized datasets, cybersecurity, and professional education.

Understanding this ecosystem enables investors, entrepreneurs, engineers, policymakers, and students to recognize opportunities that extend far beyond building the next AI application.

 

1. Semiconductor Manufacturers: The Modern Shovel Makers

At the foundation of the AI revolution lies computational power.

Training large language models requires enormous quantities of graphics processing units (GPUs), specialized AI accelerators, and advanced memory technologies. These processors perform trillions of mathematical operations every second, making them the indispensable "shovels" of artificial intelligence.

Several companies dominate this layer:

  • NVIDIA has become synonymous with AI acceleration through its GPUs, CUDA software platform, DGX systems, and AI Factory architecture.
  • AMD competes with high-performance Instinct accelerators designed for AI workloads.
  • TSMC (Taiwan Semiconductor Manufacturing Company) manufactures many of the world's most advanced AI chips for numerous technology firms.
  • Broadcom provides networking chips that connect thousands of GPUs inside AI data centers.
  • Marvell Technology develops high-speed networking silicon for cloud infrastructure.
  • SK hynix and Micron Technology supply High Bandwidth Memory (HBM), which has become essential for modern AI systems.

Without these companies, the remarkable advances in generative AI would simply not be computationally feasible.

 

2. The Companies That Build the Machines to Build the Chips

One level deeper lies an even more strategic group of companies.

Advanced semiconductors cannot be manufactured without highly specialized equipment costing hundreds of millions of dollars.

Among the most critical suppliers are:

  • ASML, whose extreme ultraviolet (EUV) lithography machines are indispensable for producing leading-edge chips.
  • Applied Materials, a leader in semiconductor manufacturing equipment.
  • Lam Research, specializing in wafer etching technologies.
  • KLA Corporation, providing inspection and process-control systems.

These firms are effectively "selling shovels to the shovel manufacturers."

Because relatively few competitors possess comparable technological capabilities, these businesses often enjoy exceptionally high barriers to entry.

 

3. Cloud Computing Providers: Renting the Gold Mine

Most organizations cannot afford to build their own AI supercomputers.

Instead, they rent computing resources from cloud providers.

Major cloud platforms include:

  • Amazon Web Services (AWS)
  • Microsoft Azure
  • Google Cloud
  • Oracle Cloud Infrastructure

Every AI inference request, every model training session, and every deployed enterprise chatbot consumes cloud resources.

Rather than selling physical equipment, these companies rent computational capacity by the minute, creating recurring revenue streams that grow alongside AI adoption.

 

4. Energy Companies: Powering the AI Economy

Artificial intelligence is fundamentally an energy-intensive technology.

Training frontier AI models requires enormous electrical consumption, while inference at global scale demands continuous power for millions of servers operating around the clock.

Consequently, AI growth increasingly benefits:

  • electric utilities
  • natural gas producers
  • nuclear energy operators
  • transformer manufacturers
  • electrical infrastructure providers

Companies such as GE Vernova, Siemens Energy, Schneider Electric, and Eaton are benefiting from unprecedented investments in electrical infrastructure needed to support AI data centers.

The AI revolution is therefore not merely a software story—it is equally an energy story.

 

5. Data Centers: The New Industrial Factories

Data centers have become the factories of the digital economy.

Modern AI facilities contain tens of thousands of GPUs connected through sophisticated networking systems, liquid cooling technologies, backup power systems, and massive storage arrays.

Companies such as Equinix and Digital Realty develop and operate these highly specialized facilities.

As demand for AI computing grows, data centers increasingly resemble critical national infrastructure, comparable to railroads during the Industrial Revolution or oil refineries during the twentieth century.

 

6. AI Model Providers: Selling Intelligence as a Service

Most users interact directly with AI model providers.

Organizations including:

  • OpenAI
  • Anthropic
  • Mistral AI
  • Cohere

offer access to advanced language models through subscription services and application programming interfaces (APIs).

These companies monetize intelligence itself rather than physical infrastructure.

However, they also depend heavily on every lower layer of the AI supply chain—from chips to cloud computing to electrical power.

7. Developer Platforms: The Digital Toolmakers

Another profitable category consists of companies building tools for AI developers.

Examples include:

  • Hugging Face
  • Weights & Biases
  • LangChain
  • Pinecone

Rather than competing directly to create the most powerful AI model, these firms enable others to develop, deploy, monitor, and improve AI applications.

Historically, technology revolutions consistently reward toolmakers because every new participant becomes a potential customer.

8. Data Providers: Supplying the Fuel

Artificial intelligence cannot exist without high-quality data.

Organizations increasingly purchase specialized datasets covering:

  • financial markets
  • healthcare
  • satellite imagery
  • scientific publications
  • legal information
  • industrial operations

As AI systems become more sophisticated, proprietary, clean, and domain-specific data may prove more valuable than general-purpose algorithms.

Many analysts now describe data as the strategic fuel powering AI.

9. Education and Workforce Development

Every technological revolution creates demand for new skills.

Universities, online learning platforms, consulting firms, certification providers, and corporate training organizations now help millions of professionals understand AI technologies.

This educational ecosystem represents another form of shovel selling.

Instead of supplying hardware, these organizations provide knowledge that enables individuals and businesses to participate in the AI economy.

10. Trust, Governance, and Cybersecurity

As AI becomes deeply integrated into finance, healthcare, government, defense, and critical infrastructure, organizations require trustworthy systems.

This creates rapidly expanding markets for:

  • AI governance
  • cybersecurity
  • privacy engineering
  • compliance
  • auditing
  • model evaluation
  • risk management

Increasingly, businesses are not simply asking, "Can AI solve this problem?" They are asking, "Can AI solve this problem safely, ethically, securely, and legally?"

Companies capable of answering that second question may become some of the most valuable providers in the next decade.

Beyond Technology: Selling Solutions Instead of Software

Perhaps the greatest misconception surrounding AI is the belief that only those building foundation models will become successful.

History suggests otherwise.

The internet ultimately created far more value through e-commerce, digital payments, cloud computing, cybersecurity, online education, logistics, and enterprise software than through web browsers alone.

Likewise, AI's greatest commercial opportunities may emerge from applying existing models to solve practical problems across industries including:

  • medicine
  • law
  • education
  • manufacturing
  • banking
  • agriculture
  • logistics
  • scientific research

Entrepreneurs who deeply understand industry-specific challenges may create more sustainable businesses than those attempting to build the next frontier model.

Strategic Lessons from the AI Gold Rush

Several enduring principles emerge:

  1. Infrastructure often outperforms speculation. Businesses providing essential services frequently enjoy more predictable demand than those competing for highly uncertain breakthroughs.
  2. Every layer depends upon the one beneath it. Chips require manufacturing equipment. Models require chips. Applications require models. Enterprises require governance.
  3. Recurring revenue creates resilience. Cloud providers, developer platforms, cybersecurity vendors, and infrastructure suppliers often benefit from subscription-based business models.
  4. AI is an ecosystem, not a single technology. Success increasingly depends upon collaboration across hardware, software, energy, networking, education, and regulation.
  5. The largest opportunity may be solving real-world problems. AI itself is becoming a commodity. Industry expertise remains scarce.

Conclusion

The modern AI revolution resembles every major technological transformation in history. While headlines focus on spectacular breakthroughs and billion-dollar startups, the most enduring fortunes may be built by companies supplying the indispensable tools that make those breakthroughs possible.

Today's "shovels" include semiconductor manufacturing, cloud infrastructure, energy systems, developer platforms, data services, cybersecurity, governance frameworks, and education.

For entrepreneurs, investors, and professionals, the most important strategic question is therefore not merely:

"Which AI company will win?"

A more insightful question is:

"What indispensable tool will every AI company need?"

History suggests that those who answer this question correctly may become the quiet winners of the AI Gold Rush.

Glossary

AI Factory – A large-scale computing infrastructure designed to train and deploy artificial intelligence models efficiently.

Application Programming Interface (API) – A standardized interface that enables software applications to communicate and exchange functionality.

CUDA – NVIDIA's parallel computing platform that allows developers to accelerate applications using GPUs.

Data Center – A facility housing servers, networking equipment, storage systems, and cooling infrastructure to support digital services.

EUV Lithography – Extreme Ultraviolet Lithography, the advanced manufacturing process used to produce cutting-edge semiconductor chips.

Foundation Model – A large AI model trained on broad datasets that can be adapted to many downstream applications.

GPU (Graphics Processing Unit) – A highly parallel processor particularly effective for AI training and inference.

HBM (High Bandwidth Memory) – A specialized memory technology optimized for high-performance AI computing.

Inference – The process by which a trained AI model generates predictions or responses from new inputs.

Large Language Model (LLM) – A neural network trained on massive text datasets to understand and generate human language.

Semiconductor – The electronic material used to manufacture integrated circuits and computer chips.

Vector Database – A database optimized for storing and retrieving vector embeddings used in AI similarity search.


Recommended References

  1. Acemoglu, D., & Johnson, S. Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. PublicAffairs, 2023.
  2. Bessen, J. Learning by Doing: The Real Connection Between Innovation, Wages, and Wealth. Yale University Press, 2015.
  3. Christensen, C. M. The Innovator's Dilemma. Harvard Business Review Press.
  4. Miller, C. Chip War: The Fight for the World's Most Critical Technology. Scribner, 2022.
  5. Shedd, D. R., & Badger, A. The Great Heist.
  6. Porter, M. E. Competitive Strategy. Free Press.
  7. NVIDIA. Annual Report and official technical documentation.
  8. TSMC. Annual Report and Technology Roadmaps.
  9. ASML. Annual Report and EUV technology publications.
  10. Stanford University, AI Index Report (latest edition).
  11. McKinsey Global Institute. The Economic Potential of Generative AI.
  12. International Energy Agency (IEA). Energy and AI reports.
  13. Deloitte Insights. The State of Generative AI in the Enterprise.
  14. Microsoft Research. Publications on AI infrastructure and large-scale computing.
  15. Google Research. Publications on foundation models, TPU architecture, and AI systems.

Passport to Magonia, by Jacques Vallée (2014)

The Country Where Fairies Arrive in Flying Saucers Some books age badly because they were wrong, and some books age strangely because they...