Tag: AI

The Dawn of AI and Crypto Civilization

On By mehranmuslimiblogIn AI, Bitcoin, crypto, CyberSecurityLeave a comment

The day after superintelligence won’t look like science fiction. It will look like software updates shipping at the speed of thought and entire industries quietly reorganizing themselves before lunch. The popular image of a single “big bang” event misses the truth: superintelligence will arrive as an overwhelming accumulation of competence—systems that design better systems, diagnose with inhuman accuracy, and coordinate decisions at a scale no human institution can rival. When optimization becomes recursive, progress compresses. What once took decades will happen in weeks.

We already have hints of this future hiding in plain sight. In 2023, DeepMind’s AlphaFold revolutionized biology by predicting the structures of more than 200 million proteins—essentially mapping the building blocks of life in a few years, a task that traditional methods could not complete in centuries. Large language models now write code, draft contracts, and discover novel materials by searching possibility spaces no human team could fully explore. Training compute has historically doubled roughly every 6–10 months during the early 2020s, far faster than Moore’s Law, and algorithmic efficiency gains have compounded that advantage. When intelligence accelerates itself, linear expectations break.

The economy the morning after will belong to organizations that treat intelligence as infrastructure. Productivity will spike not because workers become obsolete, but because one person will wield the leverage of a thousand. Software-defined everything—factories, finance, healthcare—will default to machine-led orchestration. Diagnosis rates will climb, downtime will shrink, and supply chains will become predictive rather than reactive. The center of gravity will move from labor scarcity to insight abundance.

Crypto will not be a side story in this world; it will be a native layer. Superintelligent systems require neutral, programmable money to transact at machine speed, settle globally, and audit without trust. Blockchains offer something legacy rails cannot: cryptographic finality, censorship resistance, and automated enforcement via smart contracts. When AI agents negotiate compute, data, and energy on our behalf, they will do it over open networks with tokens as executable incentives. Expect on-chain markets for model weights, verifiable data provenance, and compute futures. Expect decentralized identity to matter when bots and humans share the same platforms. Expect treasuries to diversify into scarce digital assets when algorithmic trading dwarfs traditional flows and fiat systems face real-time stress tests from machines that never sleep.

The energy footprint will surge first—and then collapse per unit of intelligence. Today’s data centers already rival small nations in power draw, yet the same optimization engines driving AI are slashing watts-per-operation each year. History is clear: as engines get smarter, they get leaner. From vacuum tubes to smartphones, efficiency rises faster than demand—until entirely new use cases layer on top. Superintelligence will do both: it will squeeze inefficiency out of the system while unlocking categories we’ve never priced before, like automated science as a service and personalized medicine at population scale.

The political impact will be just as real. States that master compute, data governance, and talent will compound their advantage. Those that don’t will import intelligence as a service and awaken to strategic dependence. Regulation will matter—but velocity will matter more. The nations that win will be the ones that regulate with a scalpel, not a hammer, pairing safety with speed. Meanwhile, crypto networks will function as jurisdiction-agnostic commons where innovation keeps moving even when borders slow.

Critics will warn about control, and rightly so. Power concentrated in any form demands constraints. Yet the greater risk is paralysis. Every previous leap—from electricity to the internet—created winners who leaned in and losers who hesitated. Superintelligence will be no different, except the spread between the two will widen overnight. The answer is not fear; it’s instrumentation. Align objectives, audit outputs, and decentralize critical infrastructure. Do not shut down the engine of abundance—build guardrails and drive.

The day after superintelligence, markets will open, packages will ship, and most people will go to work. But the substrate of reality will have changed. Intelligence will no longer be the bottleneck; courage will be. The bold will build economies where machines and humans create together, settle on-chain, and optimize in real time. The timid will debate yesterday’s problems in tomorrow’s world.

This is not a warning. It’s an invitation.

Superintelligence doesn’t replace humanity—it multiplies it. Crypto doesn’t disrupt finance—it finally makes it global, programmable, and impartial. And the future doesn’t arrive with fireworks. It arrives with results.

The Godfather of AI Just Called Out the Entire AI Industry — But He Missed Something Huge

On By mehranmuslimiblogIn AILeave a comment

When Geoffrey Hinton, widely known as the “Godfather of AI,” speaks, the tech world listens. For decades, he pushed neural networks forward when most of the field dismissed them as a dead end. Now, after leaving Google and raising alarms about the speed and direction of artificial intelligence, he’s doing something few insiders dare: calling out the entire industry that he helped create.

Hinton’s message is straightforward but unsettling. AI is accelerating faster than society can adapt. The competition among major tech companies has become a race without guardrails, each breakthrough pushing us deeper into territory we barely understand. The risks he talks about aren’t science fiction; they’re the predictable consequences of deploying powerful learning systems at global scale without the institutional infrastructure needed to govern them.

He points out that no corporation or government has a full grip on what advanced AI systems are capable of today, let alone what they may be capable of in five years. He worries that models are becoming too powerful, too general, and too unpredictable. The alignment problem — making sure advanced AI systems behave in ways humans intend — remains unsolved. And yet the world continues deploying these systems in high-stakes environments: healthcare, finance, defense, education, and national security.

But here’s the part Hinton didn’t emphasize enough: the problem isn’t just the technology. The deeper issue is the structure of the global ecosystem building it.

The AI race isn’t happening in a vacuum. It’s happening inside a geopolitical contest, a corporate arms race, and an economic system designed to reward speed, not caution. Even if researchers agree on best practices, companies are pushed to break those practices the moment a competitor gains an advantage. Innovation outpaces regulation, regulation outpaces public understanding, and public understanding outpaces political will. This isn’t simply a technological problem — it’s a societal architecture problem.

Hinton is right that AI poses real risks, but the missing piece is the recognition that these risks are amplified by the incentives of the institutions deploying it. Tech companies are rewarded for releasing models that dazzle investors, not for slowing down to ensure long-term stability. National governments are rewarded for developing strategic AI capabilities before rival nations, not for building global treaties that restrict their use. Startups are rewarded for pushing boundaries, not for restraint. No amount of technical alignment work can compensate for misaligned incentives on a global scale.

Another point Hinton underestimates is the inevitability of decentralization. The industry is rapidly shifting away from a world where a handful of corporations control model development. Open-source models, community-driven research, and low-cost compute are making advanced AI available far beyond Silicon Valley. This democratization is powerful, but it also complicates the safety conversation. You cannot regulate an industry by only regulating a few companies when the capabilities are diffusing worldwide.

Hinton calls for caution, but we also need a coherent strategy — one that acknowledges the complexity of governing a technology that evolves faster than policy, faster than norms, and faster than global cooperation. His concerns about runaway AI systems are real, but the more pressing threat may be runaway incentives driving reckless deployment.

The Godfather of AI is sounding the alarm, and the industry should listen. But we must look beyond the technology itself. AI will not destabilize society on its own. What destabilizes society is the gap between the power of our tools and the maturity of the systems that wield them. That gap is widening. And unless the world addresses the incentives driving the AI race — not just the science behind it — even the most accurate warnings may come too late.

Silicon Valley Is Obsessed With the Wrong AI

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The Problem: Pursuing the Wrong AI

In the heart of the tech world, the playbook by many of the leading players in Silicon Valley has increasingly focused on one major objective: building ever-larger, ever-“smarter” general-purpose AI systems. But a growing chorus of academics, researchers and insiders argue this is the wrong target.

1. The obsession with “general intelligence”

  • Large Language Models (LLMs) and other “broad” AI systems dominate innovation pipelines. As one commentary puts it: “Many hundreds of billions of dollars are currently being pumped into building generative AI models; it’s a race to achieve human-level intelligence. But not even the developers fully understand how their models work or agree exactly what AGI means.” ft.com+2hec.edu+2
  • One eminent figure, Michael Jordan (UC–Berkeley), warned: “This race to build the biggest LLM … is not feasible and is going to ruin us.” hec.edu
  • The outcome: huge sums of money deployed, but unclear definitions, unclear pathways, and unclear value propositions for many of these efforts.

2. The neglect of tangible, high-impact problems

  • Some analysts observe that while the flashy AI models capture headlines, less glamorous—but far more urgent—needs are being sidelined. For example, tackling climate modelling, healthcare optimisation, supply-chain resilience. One article states: “So, why the disconnect? … Venture capitalists often look for ‘moonshots’ … While valuable, this can lead to overlooking less flashy but equally impactful innovations.” Medium
  • Thus: the mismatch—between what is being funded and hyped vs. what social, economic and environmental problems urgently demand.

3. The hype machine & distorted incentives

  • Tech insiders are increasingly critical of the hype. A piece stated: “In the bustling corridors of Silicon Valley … AI’s promise is being drowned out by excessive hype … many entrepreneurs and executives are voicing a more tempered reality.” WebProNews
  • The incentives for investors and founders often favour scale, big numbers, large models—not necessarily societal benefit or practical utility.
  • Also: the culture of “move fast and break things” is alive in AI development, which may amplify risks rather than mitigate them. techcrunch.com+1

Why It Matters: The Stakes Are High

A. Mis-allocation of resources

When capital, talent and infrastructure pour into grandiose, long-term visions (e.g., AGI, human-level reasoning machines) rather than solving present-day needs, the opportunity cost is large. For example: the world may not get the AI tools it needs for public health, climate resilience, infrastructure optimisation.

B. The erosion of trust and legitimacy

The competitive hype around “super-intelligent” machines raises expectations that often go unmet. When the public or regulators see a gap between promise and delivery, trust in the entire field drains. One academic work warns of “solutionism” and “scale thinking” that can undermine genuine social change. arxiv.org+1
Also: ethical frameworks are being invoked but often violated. As one author wrote:

“Silicon Valley is knowingly violating ethical AI principles. Society can’t respond if we let disagreements poison the debate.” carnegiecouncil.org

C. Real-world consequences

  • The preoccupation with futuristic AI distracts from present risk management. For instance, issues such as data privacy, bias, algorithmic transparency are urgent but get less attention than “will AI become human-level?” questions.
  • Moreover, some communities within tech—especially those tied to rationalist / effective-altruist threads—report psychological harms, ideological cult-like dynamics and personal suffering linked to over-fetishising AI risk. moneycontrol.com
  • A deeper danger: by building systems for scalability, dominance or control (rather than for distributed benefit) we risk exacerbating inequalities, concentrating power, and embedding flawed assumptions about what AI should do. One piece titled “The Future of AI: A Horizon of Inequality and Control” highlights this risk. Worth

Key Facts (Bold for emphasis)

  • Hundreds of billions of dollars are being invested into generative AI models aiming for “AGI” (artificial general intelligence) even though the definition of AGI remains vague or disputed. ft.com
  • Not even the building teams fully understand how many of the large models operate or what “intelligence” really means in their context. ft.com+1
  • According to research, efforts grounded in “scale thinking”—the assumption that bigger models + more data = qualitative leap—are unlikely to achieve deep systemic change. arxiv.org
  • The term “AI” is increasingly used to sprinkle hype in investment pitches despite founders/investors often lacking clear grasp of what’s being built. Vanity Fair
  • Ethical AI frameworks are often bypassed or undermined in practice; serious debate and alignment across tech, policy, academia is fragmented, giving vested interests opportunity to dodge accountability. carnegiecouncil.org

The Underlying Mis‐Assumptions

1. Intelligence = general reasoning

The Valley ethos tends to treat “intelligence” as a monolithic target—machines that can reason, think, learn like humans—rather than many specialised tools that solve specific tasks. But specialised tools often yield more immediate, measurable value.

2. Bigger is automatically better

The faith in ever-larger models, more compute, more data is rooted in optimism that scale will produce qualitatively new capabilities. But critics say this is flawed: some architectures hit diminishing returns, and “depth” of reasoning is still lacking. thealgorithmicbridge.com+1

3. Tech will save everything

A grand narrative exists: deploy AI, transform humanity, fix all problems. But this “solutionism” often undervalues social, economic, institutional dimensions. The tech-centric view gives insufficient weight to human, policy and systemic factors. Worth+1

What a Better Approach Might Look Like

• Reprioritise meaningful problems

Shift some of the focus and resources toward real-world, high-impact outcomes: healthcare diagnostics, climate mitigation, efficient energy grids, education access.

• Emphasise clarity and specification over hype

Rather than saying “we will build AGI”, ask “what specific outcome do we want? How will we measure success? Who benefits and how?”

• Balance scale with embedment

Recognise that not all problems need massive global models; sometimes smaller, domain-specific, context-aware systems are more effective and ethical.

• Integrate ethics, governance and societal perspectives early

Ensure that technical design includes transparency, accountability, human-in-the-loop, deliberation over what the system should (and should not) do.

• Accept limitations and focus on augmentation

Rather than aiming for replacement of human reasoning, focus on AI as amplifier of human capabilities, especially in under-served domains.

Conclusion

The current trajectory of Silicon Valley’s AI obsession—large models, general reasoning, big scale—carries significant opportunity, but also significant risk. By continuing to chase the “wrong AI,” we risk misallocating massive resources, under-serving critical societal needs, and perpetuating a tech-centric hubris. The corrective is not to reject AI, but to refocus it: towards clear problems, measurable outcomes, human-centred design, and ethical embedment.
Only then can AI become the tool we need for impact, rather than the spectacle we fear.

The Dawn of Self-Improving AI: Meta’s Secret Weapon

On By mehranmuslimiblogIn AI, Innovation, MetaverseLeave a comment

In the relentless race toward artificial general intelligence (AGI), tech titans have long vied for supremacy. Yet, in recent months, whispers from the corridors of Meta — formerly Facebook — suggest a revolutionary breakthrough that could redefine the landscape of AI: self-improving intelligence. This development, still shrouded in corporate secrecy, promises to fundamentally alter how machines learn, adapt, and evolve.

The Age of Autonomous Learning

Traditional AI systems, no matter how advanced, are bound by the limitations of their initial programming. Machine learning models improve through human-curated data and incremental updates, requiring engineers to intervene at nearly every stage. Meta’s rumored innovation, however, could unlock a new paradigm: machines capable of autonomously identifying inefficiencies, generating novel strategies, and iteratively improving their own architectures without human intervention.

Imagine an AI that not only solves problems but actively redesigns its own cognitive structure to perform better. This is no longer science fiction. Meta’s approach reportedly leverages recursive self-improvement, a concept long theorized in academic circles but never fully realized in a practical, scalable system.

The Mechanics Behind the Breakthrough

While Meta has remained tight-lipped, analysts speculate the core of this technology combines three critical components:

  1. Advanced Neural Architecture Search (NAS): Allowing AI to automatically discover optimal network structures for any task.
  2. Meta-Learning Algorithms: Teaching AI systems how to learn more efficiently from limited data, effectively learning how to learn.
  3. Self-Optimization Protocols: Enabling continuous performance evaluation and autonomous refinement of both model parameters and operational strategies.

Together, these elements could allow an AI to self-evolve at unprecedented speed, shrinking the gap between human-level intelligence and machine cognition.

Implications for the Future

The implications of such a system are staggering. Economically, industries from finance to pharmaceuticals could experience dramatic acceleration in innovation cycles. Imagine AI that designs drugs, develops new materials, or optimizes global supply chains faster than any human team could conceive.

Societally, however, the stakes are equally high. Self-improving AI could outpace regulatory frameworks, creating ethical and safety dilemmas that humanity is ill-prepared to manage. Ensuring that such systems remain aligned with human values will be paramount — a task as complex as the technology itself.

Meta’s Strategic Edge

Meta’s secretive culture, combined with its vast computational resources, gives it a strategic advantage. Unlike startups, which often focus on narrow applications, Meta possesses the infrastructure to scale a self-improving AI globally, integrating it across social platforms, virtual reality ecosystems, and potentially even financial services.

This capability suggests that Meta isn’t just chasing incremental improvements in AI — it is aiming to redefine intelligence itself, positioning the company at the forefront of the next technological revolution.

The Dawn of a New Era

We may be standing at the threshold of a new era, where intelligence is no longer solely human-driven but co-created with autonomous, self-improving systems. Meta’s breakthrough, if realized, will force industries, governments, and societies to rethink not just technology, but the very definition of knowledge, creativity, and agency.

The future of AI is no longer about performing tasks — it is about evolving, iterating, and surpassing the boundaries of its own design. And in that future, Meta could very well be the architect of a new epoch in intelligence.