Showing posts with label FutureOfAI. Show all posts
Showing posts with label FutureOfAI. Show all posts

Tuesday, June 30, 2026

LLMs aren't Uber; they're MoviePass

 

Every investor wants AI to be Uber. Burn cash now, win the market, raise prices later, print money. The losses are an investment in dominance.

It's the wrong story. The honest one is MoviePass and the difference is the whole ballgame if you're betting your costs on cheap AI staying cheap.

Uber's losses had an exit. MoviePass's didn't.

Uber bled billions on purpose. But notice why the bleeding stopped. Once it owned a city - enough drivers, enough riders, a habit - serving one more ride barely cost anything while its pricing power climbed. The subsidy bought a position that got cheaper to hold. Network effects did the work. There was a door marked "profit" at the end.

MoviePass sold unlimited movies for $9.95 a month. The flaw was brutal: every time you used it, they paid the theater near full price for your seat. The more you loved it, the more they lost on you. Scale never fixed the math - it multiplied the loss. Your best customers were your worst customers. It grew itself to death in about a year.

One subsidy had an exit. The other had a cliff.

The line that separates them: for Uber, usage trended toward revenue. For MoviePass, usage was cost.

So which one is an LLM?

Sit with that line, because LLMs fall on the wrong side of it.

Every prompt burns real compute, every single time. Your heaviest users - the ones running agents, generating all day, building their whole workflow on it - cost the provider the most. Using it more makes the math worse, not better. That's the MoviePass shape exactly: usage is cost, not revenue, and the people who love it most bleed you fastest.

Uber's cost per ride fell toward zero as it scaled. An LLM's cost per query is inference, and inference doesn't drop because you signed up more customers. It climbs with how hard each one leans on you. Models do get cheaper per token over time - but agents now spend tokens fifty at a time, and the frontier keeps moving to bigger, costlier models everyone then expects as standard. The cost floor keeps walking forward.

There's no geographic density to defend, either. Weights leak. Open models catch up. The "unlimited intelligence for $20 a month" plan is running the MoviePass playbook with better branding.

You can already watch the same death spiral

This is the part that should make the analogy land. MoviePass didn't die in one step. It died in a sequence - and AI is visibly walking the same one.

MoviePass tried to hold the unlimited promise, then started carving it up: blackout dates on the movies you actually wanted, peak-hour restrictions, "fair use" caps, verification hoops, and finally a hard quota that replaced "unlimited" entirely. Each step quietly admitted the model didn't work.

Now look at AI in 2026. The best models drifting out of the flat subscription and behind higher tiers or API metering. Usage windows and rolling caps. Slower models on the cheap plan, the good one upstream. Limits that tighten right as you grow dependent. None of that is failure of the technology; it's the same forced retreat from "unlimited," just run earlier and more gracefully than MoviePass managed. We're several steps into a seven-step story whose last step, for MoviePass, was the lights going out.

Where the analogy breaks (and it does)

I'd be doing the same lazy thing I'm criticizing if I pretended it's a clean match. Two real differences:

AI has a moat MoviePass never had: switching costs. Once your data, prompts, tools, and your team's habits live inside one provider, you don't wander off for a dollar. That's far stickier than a movie subscription.

And providers have an escape MoviePass fumbled. MoviePass metered late and clumsily, and customers revolted. AI companies are metering early and smoothly, while the product is still loved. Same move, just run better.

So LLMs won't "die" like MoviePass. But the flat price will, the same way unlimited movies did.

What a leader actually does about it

This isn't "AI is a bubble." The technology is real and permanent. The pricing is the temporary part. Three moves:

Budget for the real cost, not the coupon. If your business case only works at today's subscription rate, you don't have a business case. You have a free trial. Model what happens when your most valuable, heaviest use becomes your most expensive.

Don't get hooked on one provider's cheap tier. The more generous it feels now, the more certain it is to tighten. Build so you can swap models without rebuilding the house.

And separate your real moat from the discount you're enjoying. The discount ends. Your data, your workflow, the thing a competitor can't copy - that's what deserves the investment.

MoviePass taught a generation a hard lesson: when the thing you love is sold below what it costs to make, the love is the problem. AI is more durable than that. But the price on your invoice today is a story about someone else's funding round - not about what this actually costs.

Plan for the bill, not the trial.


Monday, June 29, 2026

We're Running Out of Internet to Train AI On. Here's What Comes Next

 

There's a problem the AI labs don't put on the keynote slides. The big language models are running low on fuel.

They learned to write by reading almost everything humans have ever published. Books, code, forums, the whole internet. That well is close to dry. You can't double the size of human writing on demand, and the easy answer - train models on text other models wrote - quietly poisons them. Quality degrades. The industry has a name for it now: model collapse. (I unpacked that failure mode here → Habsburg Jaw in making - Model Collapse in AI.)

So the question that actually decides the next decade isn't "how much bigger can the models get." It's "where does the next training data come from when the internet runs out?"

The most serious answer going right now is this: stop collecting data. Start generating experience. That's what world models do.

A different kind of model

A language model predicts the next word. A world model predicts the next state of an environment, what happens when you turn the wheel, drop the glass, brake on ice. It learns physics and cause-and-effect, then lets a machine rehearse actions inside its own simulation before doing them for real.

The idea is older than the hype. David Ha and Jürgen Schmidhuber published a paper called "World Models" back in 2018, where an agent learned to play a game inside its own dreamed-up version of it. What's new is that the models finally got good enough to matter outside a lab and that the data wall gave everyone an urgent reason to care.

2026 is when it left the lab

Watch what shipped in the last six months, because the timing isn't a coincidence:

🔹 Google DeepMind released Project Genie to the public in January. Type a sentence, walk around a playable 3D world in real time. By May, it connected to Street View.

🔹 Nvidia launched Cosmos 3 on June 1 - an open model built specifically to train robots and self-driving cars inside generated worlds, with a coalition of robotics companies around it.

🔹 Waymo built its own world model in February to create the dangerous driving situations it can't safely film on real roads. Wayve shipped GAIA-3 for the same reason.

🔹 Fei-Fei Li's World Labs opened Marble to the public. Odyssey raised $310M.

And the loudest signal: Yann LeCun bet his next chapter on the claim that scaling language models is a dead end, and that models which learn the structure of the world are the real path forward.

These aren't six companies chasing a demo. There are six answers to the same shortage.

Why this is a business story, not a robotics one

Here's the move that matters for anyone running a company. Real-world data is slow, expensive, and often impossible to gather - a billion driving miles, a million robot grasps, the rare disaster you can't stage. A world model lets you manufacture that experience. Simulate the edge cases by the million, overnight, for the cost of compute instead of years and lives.

If that sounds abstract, connect it to something you already use: the digital twin. A digital twin tells you what your factory or supply chain looks like right now. A world model is the layer that predicts what it does next - so you can test a change a thousand ways before spending a dollar in the real world.

That's why this spreads well past cars and robots: manufacturing lines, warehouse logistics, supply-chain shocks, financial scenarios, anywhere with humanoids or autonomy on the roadmap. The common thread is rehearsal - deciding after you've seen it play out, not before.

The divide it creates

For three years, the AI advantage was about generating content faster. The next advantage is quieter and harder to copy: generating experience. The companies that learn to simulate their own reality will train, test, and decide faster than the ones still waiting to collect data from the real one. That gap compounds the way cloud and data infrastructure did - invisibly, until a competitor is simply moving at a speed you can't match and you can't quite explain why.

LLMs hit a data wall. World models walk around it by building their own.

So the question worth sitting with: when the easy data runs out, will your company still be waiting to collect more - or generating what it needs?

 

Visual Recap 


 


Wednesday, February 11, 2026

The Race for AI Dominance: Which Platform Will Lead the Pack?

 

In the fast-evolving world of artificial intelligence, the question on everyone's mind is: Which technology platform will emerge as the dominant player? As we stand in early 2026, the race remains wide open, with no single winner in sight. Instead, we're likely heading toward an oligopoly - a handful of powerhouses controlling different segments of the market. Drawing from current trends, key players, and emerging battlegrounds, let's dive into the landscape shaping AI's future. Whether you're a tech leader, investor, or enthusiast, understanding these dynamics could inform your next strategic move.

The image illustrates a market share chart for leading vendors in the Generative AI sector, highlighting major players like Microsoft, Google, and Intel, with other significant contributors such as IBM and Cognizant.

AI-generated content may be incorrect.

Ref: https://iot-analytics.com/leading-generative-ai-companies/

Current Leaders and Their Advantages

The AI arena is dominated by a mix of established tech giants and innovative upstarts, each leveraging unique strengths in infrastructure, research, and user ecosystems. Here's a breakdown of the front runners.

Cloud & Infrastructure Giants

These players form the backbone of AI development, providing the scalable computing power essential for training and deploying models.

  • Google (DeepMind, Vertex AI, TensorFlow): Google leads with groundbreaking research, from inventing Transformers to conquering games like AlphaGo. Their massive data troves from Search, YouTube, and other services, combined with robust cloud infrastructure and custom chips, give them an unparalleled edge. They dominate in open-source tools like TensorFlow and JAX, reaching consumers through everyday apps like Gmail, Meet, and Shopping. However, they must navigate ethical dilemmas and increasing regulatory pressures to maintain momentum.
  • Microsoft (Azure AI, OpenAI Partnership, Copilot): Microsoft's strategic bets, including their deep ties with OpenAI and tools like GitHub Copilot, position them as enterprise favorites. Seamless AI integration into Office 365 and Azure boosts productivity, while their developer ecosystem ensures broad adoption in businesses. The edge? Copilot's embedding in Windows and Office tools. Challenges include heavy reliance on OpenAI's models and potential antitrust scrutiny.
  • Amazon (AWS AI, Bedrock, SageMaker): As the cloud market leader, AWS offers comprehensive AI/ML services tailored for enterprises, backed by custom chips like Trainium and Inferentia. Their strength lies in secure, scalable deployments trusted by organizations worldwide. Yet they lag in consumer-facing AI, often seen as the "plumbing" behind the scenes rather than a flashy innovator.

Consumer & Ecosystem Players

Focusing on end-user experiences, these companies are building AI into daily life through apps, social platforms, and devices.

  • OpenAI (ChatGPT, GPT-4, DALL·E): With a first-mover advantage in generative AI, OpenAI has captured viral consumer attention through rapid innovations. Their brand strength and developer APIs foster a thriving ecosystem. However, high operational costs, dependence on Microsoft, and open-source rivals pose significant hurdles.
  • Meta (Llama, FAIR Research): Meta's open-source push with models like Llama 2/3, fueled by vast social data, sets them up for dominance in accessible AI. Integration into metaverse tech and products like Ray-Ban smart glasses highlights their consumer edge. Privacy scandals and delayed monetization remain key challenges.
  • Apple: Apple's forte is hardware-software synergy, exemplified by M-series chips enabling on-device AI. Their privacy-centric approach and devoted user base could revolutionize personal AI via upgrades to Siri or AR/VR experiences. The catch? They're playing catch-up in cloud-based generative AI.

Wildcards

Don't count out these outliers, which could pivot the race with specialized expertise.

  • NVIDIA: Ruling AI hardware with GPUs and CUDA, NVIDIA's Omniverse platform and partnerships make them indispensable. If hardware bottlenecks persist, they could become the "Intel of AI."
  • China’s Players (Baidu, Alibaba, Tencent, Huawei): Backed by government support and a huge domestic market, these firms excel in verticals like healthcare and finance, potentially leading in regions with strict data laws.
  • Open-Source Communities (Hugging Face, Mistral, Stability AI): By democratizing AI, these groups drive rapid, customizable innovation. Improved quality and safety could upend proprietary dominance.

Key Battlegrounds

The path to AI supremacy will be fought on three fronts:

  1. Infrastructure: Control over clouds, chips, and data centers is crucial. Leaders here include AWS, Microsoft Azure, Google Cloud, and NVIDIA.
  2. Models & Algorithms: Building the most capable, efficient, and safe AI systems. Top contenders: OpenAI, Google DeepMind, Meta, and Mistral.
  3. Applications & Ecosystems: Integrating AI into everyday tools for consumers, enterprises, and edge devices. Standouts: Google, Microsoft (via Copilot), Apple (on-device), and Meta (social/AR).

Predictions: Who Could Win?

Looking ahead, outcomes vary by timeline and market shifts.

  • Short-Term (2026–2030): In enterprise AI, expect Microsoft (Copilot + Azure) and Google (Vertex AI) to lead. For consumers, OpenAI (ChatGPT) and Meta (Llama) will shine, with Apple possibly disrupting via on-device advancements.
  • Long-Term (2030+): If AI commoditizes, open-source leaders like Hugging Face or Mistral, or hardware giants like NVIDIA, could prevail. Regulation might elevate compliance experts (e.g., IBM, Palantir) or regional players (e.g., Huawei in China). Should AGI breakthrough, Google DeepMind or OpenAI are frontrunners for mastering generalization, safety, and scale.

Dark Horses & Disruptors

Beyond the big names, watch these:

  • Startups: Anthropic (safety-first AI), Inflection AI (personal AI), and Cohere (enterprise LLMs) could claim niches.
  • Decentralized AI: Blockchain-integrated platforms like Fetch.ai or SingularityNET may rise if trust becomes paramount.
  • Government-Led AI: Policies like the EU AI Act or U.S. initiatives could boost public-interest platforms. Similarly, Sovereign AI platforms may dominate in their area of influence.

What Could Change the Game?

Several factors could upend the status quo:

  • Breakthroughs in AI Architecture: Innovations like hybrid symbolic-neural models or neuromorphic computing might crown new leaders.
  • Regulation: Tough rules on privacy, bias, or safety could benefit incumbents like Microsoft or IBM over agile startups.
  • Hardware Innovations: Quantum or photonic chips might challenge NVIDIA's GPU stronghold.
  • User Trust: Platforms excelling in combating misinformation, bias, and ensuring safety will earn lasting loyalty.

Ultimately, the "dominant" platform hinges on your needs: Microsoft or Google for enterprises; OpenAI or Apple for consumer apps; Meta or Hugging Face for open-source solutions; NVIDIA or Qualcomm for hardware/edge AI. 


 What do you think - will we see a single winner, or a collaborative ecosystem? Share your thoughts in the comments!

Wednesday, July 9, 2025

Is Contemporary AI a Mature Engineering Discipline?

 

At a recent industry meetup, a thought-provoking question emerged: Has Artificial Intelligence (AI) evolved into a mature engineering discipline? The discussion was lively but largely focused on AI in isolation. That conversation stuck with me, prompting a deeper dive—not just into AI, but into how engineering disciplines as a whole mature over time.

As an electronics engineer by education and a software professional by trade, I’ve had the privilege of witnessing the evolution of two of the most impactful fields of the last century. Exploring their journeys offers valuable context for assessing where AI stands today.

Electronics Engineering: From Breadboards to Modular Mastery

The early days of Electronics Engineering involved manually designing circuits on paper, followed by prototyping on breadboards using discrete components—resistors, capacitors, diodes, and transistors. Once a design was validated, it was implemented on a fabricated Printed Circuit Board (PCB) for practical use.

The advent of Integrated Circuits (ICs) shifted the focus away from discrete components to chip-based solutions, enabling the development of more complex and compact devices. With the introduction of electronic design automation (EDA) software, breadboards became obsolete. Engineers could now design and simulate circuits virtually, while innovations in PCB manufacturing allowed for multilayer, high-density layouts, driving massive growth in the electronics industry.

As circuit complexity increased, the repairability of boards diminished. Faulty boards were more cost-effective to replace than repair, giving rise to a module-based approach where entire subassemblies—power supplies, heatsinks, sensors—were treated as replaceable units. This replaceability, coupled with the establishment of standardized design processes and professional certifications, reflects the discipline's maturity.

This evolution from component-level design to modular systems is one axis of engineering maturity. Another is the educational background required for practitioners. Initially, expertise in electronics demanded a PhD-level understanding. Today, a broad spectrum of professionals—from electronics engineers and science graduates to high school hobbyists—can contribute meaningfully to the field.

Software Engineering: From Binary to Low-Code

Software Engineering has experienced a similarly dynamic journey. Early programming involved writing binary code, followed by assembly language. The development of the C programming language marked a turning point, unlocking higher-level programming languages such as COBOL, which ushered in increasing levels of abstraction and domain-specific specialization.

Today, we have dedicated languages for specific tasks: HTML, CSS, and JavaScript for front-end development; SQL for data manipulation; and many others. Libraries and frameworks—like Java’s Struts for web applications—further simplified the development of complex systems. The latest trend is toward low-code and no-code platforms, allowing users to configure applications through visual interfaces rather than writing traditional code.

Metrics in Software Engineering have also matured. Early measures focused on hardware utilization and code efficiency (e.g., Chidamber & Kemerer metrics), but contemporary metrics, such as those in the DORA framework, emphasize business impacts and user satisfaction.

 

Innovations in storage media from punched cards and magnetic tapes to floppy disks, hard drives (HSSs), and solid-state drives (SSDs) have further supported the field’s ability to handle larger instruction sets and data, reflecting its growing maturity and capacity to address societal needs.

On the education front, software engineering has democratized significantly. What once required a PhD is now accessible to computer science graduates, electrical engineers, general science graduates, and even self-taught individuals and high school students.

Contemporary AI: Powerful but Early in Its Journey

In contrast, contemporary AI remains in its early stages of development. The field demands a deep understanding of complex mathematical concepts—such as linear algebra, statistics, and optimization—alongside specialized programming skills in languages like Python and frameworks like TensorFlow or PyTorch. This high entry barrier mirrors the educational requirements of early Electronics and Software Engineering.

 

AI is characterized by rapid innovation, evidenced by the proliferation of research papers and the emergence of new standards, such as the Agent Name Service (ANS) and Model Context Protocol (MCP). Experimentation with novel hardware architectures and internal communication protocols (e.g. DeepEP by Deepseek) underscores the field’s dynamic nature. However, current metrics in AI remain narrowly focused on technical aspects, such as hardware utilization, code efficiency, and the accuracy or "truthfulness" of outputs, rather than broader business or societal impacts.

 

Significant challenges persist, including the need for explainable AI to ensure transparency in decision-making and the ethical implications of deploying AI systems. These factors, combined with the field’s reliance on cutting-edge expertise and its evolving foundations, suggest that contemporary AI has not yet reached the maturity of its engineering counterparts.

Conclusion

While contemporary AI has made remarkable strides, it has not yet achieved the maturity seen in Electronics Engineering or Software Engineering. Its dependence on advanced technical knowledge, the rapid pace of research, and the predominance of technical metrics all indicate a discipline still in its formative stages. By contrast, Electronics and Software Engineering have developed standardized practices, broadened educational accessibility, and shifted their focus to encompass broader impacts—hallmarks of mature engineering fields.

 

To advance AI toward maturity, the community must prioritize several key areas: investing in education and training programs to widen access, establishing industry-wide standards to stabilize practices, and reorienting metrics to measure societal and business value alongside technical performance. Only through such efforts can AI fully evolve into a robust and mature engineering discipline.