Intelligent Founder AI Podcast

@poonamparihar

One focused investigation per week: a critical AI or tech story breaking now, what actually changed beneath the headlines, and how to respond for real business value. www.intelligentfounder.ai

  1. 4d ago

    Ep.014 - Open Source vs Proprietary AI: The Model Decision That Shapes Your Infrastructure

    The performance gap between open-source and proprietary frontier models has collapsed. DeepSeek V3 offers performance comparable to GPT-4o at 27 cents per million input tokens, compared to roughly 2.50 dollars for GPT-5. DeepSeek’s reasoning model costs 55 cents per million input tokens - 96 percent cheaper than equivalent OpenAI reasoning models. Open-source models now cover approximately 80 percent of real-world enterprise use cases at 86 percent lower cost than proprietary alternatives. The practical spending threshold is 15K pounds per month in API costs. Below that, the engineering overhead of self-hosting open-source models is not worth it. Above it, the economics justify a proper evaluation. Above 100 million tokens per month with adequate engineering capacity, open-source self-hosting is almost always significantly cheaper. Proprietary models give you access to frontier capability with zero deployment overhead. Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. The trade-offs are vendor lock-in, no control over model behaviour or training data, and complete dependency on a vendor who can change pricing or deprecate model versions on their timeline. Open-source models give you full control, fine-tuning capability on proprietary data, and deployment flexibility including air-gapped environments. The trade-off is that you own the operational complexity. Fine-tuning pays back when you exceed roughly ten thousand requests per day - below that, prompt engineering with a frontier model is more economical. Modern prompt optimisation techniques have been shown to outperform reinforcement learning fine-tuning by 6 to 19% points on benchmark tasks while using up to 35 times fewer compute resources. Fine-tuning wins for very high volume token cost reduction and for hard-to-prompt output formats. The model choice and the infrastructure choice are the same decision viewed from two angles. Get clear on your volume, your compliance requirements, and your engineering capacity, and the right choice usually becomes obvious. This is 5th episode in the series of Build vs Buy vs Rent: The AI Infrastructure Decision Tree for Startups Listen to the full episode here, in Substack app, or Apple, Spotify / youtube. Thanks for reading Intelligent Founder AI! This post is public so feel free to share it. Latest On AIUnfiltered - GenAI Was the Warning Shot, Agentic AI Is the Next Test Different angles, same battlefield: trust, safety, and control over information. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.intelligentfounder.ai/subscribe

  2. Jul 13

    Ep.013 - The On-Premises Case: When Buying Hardware Actually Wins

    If you’re pushing serious AI workloads, there’s a point where buying your own GPUs quietly beats “just use the cloud” on pure maths. Once you cross that point, the savings stop being theoretical and start showing up in your P&L. This is 4th episode in the series of Build vs Buy vs Rent: The AI Infrastructure Decision Tree for Startups. TL;DR * Above ~70% GPU utilisation, owning hardware usually beats the cloud on cost. * The real break‑even sits roughly between 55–75% utilisation, depending on power, amortisation, and cloud pricing. * Lenovo’s TCO work: ~8x cheaper than cloud infra and up to ~18x cheaper than frontier APIs per million tokens. * At 10B tokens/month, three‑year savings vs pure APIs can exceed £2M. * 8x H100 server: $250k–$400k upfront plus $3k–$5k/month to run; ~$11k/month effective cost over three years. * Equivalent cloud H100 capacity: roughly $14k–$20k/month. * You must factor in power (6–10 kW per rack unit), UK colocation (£500–£2,000/rack/month), and infra engineers (£80k–£140k/year). * A hybrid model (own the baseline, rent the spikes) can cut AI infra spend by ~40–60% vs pure cloud. Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. At around 70 percent GPU utilisation, owning on‑prem infrastructure is usually the better deal for AI inference. Below that level, the cloud’s flexibility earns its premium because you’re not paying for expensive hardware that sits idle when traffic drops. In practice, the break‑even lives somewhere between 55 and 75 percent sustained utilisation, depending on things like your electricity rate, how many years you plan to amortise the kit, and which cloud pricing tier you’re comparing against. Lenovo’s 2026 total cost of ownership work puts real numbers to this. They found that self‑hosted GPU infrastructure can be about 8 times cheaper per million tokens than raw cloud infrastructure, and up to 18 times cheaper than using frontier model APIs. Once you’re at around ten billion tokens a month, the three‑year cost gap between owning hardware and living entirely on cloud APIs can easily exceed two million pounds. The sticker price on an 8‑GPU H100 box is not small. You’re looking at roughly 250,000 to 400,000 dollars upfront for the server itself. Then you add 3,000 to 5,000 dollars a month in operating costs for colocation, power, cooling, and maintenance. Spread the hardware over three years and your effective monthly cost lands at around 11,000 dollars. Buying similar H100 capacity on‑demand in the cloud typically ends up between 14,000 and 20,000 dollars a month, so the saving is real and it compounds over time. Where founders often get caught out is in the hidden line items. Modern H100 servers can pull 6 to 10 kilowatts per rack unit, which means you can’t just drop them into a normal office and hope for the best. You need proper co-location, and in the UK that runs about 500 to 2,000 pounds per rack per month. You also need engineers who can run GPU infrastructure safely and reliably, and UK market rates put that at roughly 80,000 to 140,000 pounds per person per year. On top of that, you’re carrying hardware risk: once you buy, your performance ceiling is locked in for the amortisation period while cloud alternatives quietly keep improving in the background. That’s why most serious AI teams don’t go “all cloud” or “all on‑prem” for long. The model they converge on is hybrid: own enough hardware to cover your predictable baseline workloads, and use the cloud when you need to absorb burst traffic. if planned well, that blended architecture cuts your overall AI infrastructure bill by about 40 to 60 percent compared to living entirely in the cloud. It’s where most AI companies end up once their volume of inference forces them to care about infrastructure as more than a line item. Listen to the full episode here, in Substack app, or Apple, Spotify / youtube. Thanks for reading Intelligent Founder AI! This post is public so feel free to share it. This week Intelligent Founder AI and AI Unfiltered broke into Substack’s “Rising in Technology” leaderboard at #88—together, after already hitting #98 in Business earlier this year. Thank you for all the support so far. AI Unfiltered is where we track the latest AI news, investigations, and “what just happened?” moments and, more importantly, what they actually mean for operators, founders, and buyers. Think model‑on‑model training fights, national‑security angles, regulatory shifts, and how all of that moves the ground under your product roadmap. Intelligent Founder AI goes deeper: long‑form dives into technical and business architecture, operator‑grade GTM, and governance playbooks for getting AI into real enterprises and critical infrastructure without getting lost in the hype. It’s where we turn those headlines into concrete frameworks you can actually run inside your own stack. Together, AIU and IF.ai are designed as a pair: one keeps you on top of what’s changing week to week, the other helps you re‑wire your architecture, strategy, and sales motion around it. If that sounds useful, check out both, hit subscribe, and tell us what you want us to dissect next. Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.intelligentfounder.ai/subscribe

  3. Jul 6

    Ep.012 - GPU Rental Markets: The New Compute Arbitrage

    This is 3rd in the series of Build vs Buy vs Rent: The AI Infrastructure Decision Tree for Startups. The global GPU rental market grew from 3.2 billion dollars in 2023 to a projected 9.8 billion by 2025. That growth created a new category of infrastructure provider called neoclouds or GPU-as-a-Service. Neo-clouds ( GPU-as-a-Service) compete with AWS, GCP, and Azure on price and specialization. Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. CoreWeave is the largest, with trailing revenues around 3.5 billion dollars. Lambda Labs, Crusoe, RunPod, and Spheron cover different segments of the market, from enterprise multi-year contracts to developer-friendly hourly access to sustainability-focused compute. and what they all have in common is that they are cheaper than hyperscalers for GPU-intensive AI workloads. Current pricing for H100-class compute runs between 1.80 and 3.50 dollars per hour on specialist providers, with spot instances as low as 1.20 dollars per hour. AMD MI300X alternatives often come in 30 to 40 percent cheaper than Nvidia H100 for equivalent inference throughput. There are Four pricing models available: * on-demand by the hour, * reserved instances with 30 to 60 percent discounts for committed periods, * spot instances that can be interrupted, and * bare metal for teams at significant scale. The strategy maps directly to workload type. Variable traffic: on-demand. Stable production inference: reserved. Training and batch jobs: spot. High-throughput inference: bare metal. The most commonly missed cost is egress. Moving data out of a cloud environment typically costs 80 to 90 dollars per terabyte. For applications with larger inputs or outputs, egress can add 20 to 40 percent to the apparent cost of GPU rental. and It is almost never included in headline pricing comparisons. SO, the right way to use GPU rental markets is as a bridge. Validate on APIs, build observability into your utilisation patterns, then move to reserved GPU rental once your traffic is predictable enough to commit. That is the staircase: API to reserved rental to owned hardware, moving up each step only when the data justifies it. Listen to the full episode here, in Substack app, or Apple, Spotify / youtube. Thanks for reading / listening Intelligent Founder AI! This post is public so feel free to share it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.intelligentfounder.ai/subscribe

  4. Jun 30

    Ep.011 - The Real Cost of Inference APIs: What You Are Actually Paying For

    There are now over 69 providers offering LLM inference. Prices range from 14 cents per million input tokens at the cheap end to 180 dollars per million output tokens at the frontier. That is a 1,280-times gap. Most founders are somewhere in the middle without a clear reason to be there. The calculation is somewhat straightforward once you do it. lets take your daily active users, multiply by interactions per user per day, multiply by tokens per interaction - split into input and output, because output tokens are typically two to four times more expensive, and run that number through the per-token prices of three or four providers. The result is often a shock. A product with ten thousand daily active users and five AI interactions each can easily reach twenty thousand pounds per month on a frontier model, and under five hundred pounds per month on a comparable open-weight alternative. Three things determine whether the API route makes sense. Volume: below fifteen thousand pounds per month in API spend, the engineering overhead of self-hosting is not worth it. Predictability: APIs absorb traffic spikes without operational overhead, and that flexibility has real value for early-stage products. Model quality requirements: not every task needs a frontier model. Routing simple queries to smaller, cheaper models can cut inference costs by 40 to 70 percent with no user-visible quality loss. Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is how the vendor lock-in risk gets real and underestimated. When you build on a single API provider, you are locked in not just contractually but through accumulated prompt engineering work, evaluation datasets tuned to that model’s behaviour, and observability tooling built around that provider’s response format. The exit cost from a fully-loaded enterprise AI stack has been estimated at between two hundred thousand and one million dollars in re-engineering. The mitigation is simple: use an abstraction layer between your application code and the vendor API. LiteLLM and Portkey both do this. It adds minimal overhead and lets you switch providers with a configuration change. The cheapest route from day one is not the cheapest route at scale. Start tracking your cost per inference from launch. Listen to the full podcast episode for the full breakdown. This episode is second in the series Build vs Buy vs Rent. see you in the next episode. Note : APIs are sometime pronouced APees, apologies for that, I let Elevenlabs run wild. Thanks for listening/reading Intelligent Founder AI! This post is public so feel free to share it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.intelligentfounder.ai/subscribe

  5. Jun 22

    Ep.010 — Build vs Buy vs Rent: The AI Infrastructure Decision Tree for Startups

    Every AI startup hits the same wall eventually. The product is working, users are growing, and then the infrastructure bill arrives and nothing makes sense anymore. The question is not which model to use or which framework to build on. The question is where your AI actually lives, who owns it, and what happens to your margins as you scale. There are three positions available to you. You can build on hosted inference APIs, paying OpenAI or Anthropic or one of the cheaper alternatives per token. You can rent GPU compute by the hour from neocloud providers like Lambda, CoreWeave, or Crusoe. Or you can buy hardware and operate it yourself. Build, rent, buy. Three positions, very different economics at different scales. Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Most founders treat this as a one-time decision. It is not. It is a continuous optimisation problem. and, the right answer changes as your traffic grows, as GPU prices shift, and as your model strategy matures. H100 cloud rental rates have fallen from eight dollars an hour in early 2023 to around one eighty to three fifty per hour in mid-2026. API prices have fallen too, but unevenly - there is now a 640-times gap between the cheapest viable LLM API and the most expensive frontier option. That spread is enormous, and most founders are not actively managing it. The decision is driven by three variables: your utilization rate, your workload predictability, and your engineering capacity. High sustained utilization, predictable traffic, and a team that can operate infrastructure - when all three are true, owning your compute makes economic sense. When any one is missing, you want flexibility. Here is the number that changes how you think about this. Eighty percent of AI GPU spend is now inference, not training. That means your infrastructure choice is being made primarily for production workloads, not for training runs. And for regulated sectors like aerospace, transport, healthcare, financial services - where your data goes is not a preference. It is a legal requirement. This series runs eight episodes. We cover inference API economics, GPU rental markets, the on-premises case, open source versus proprietary models, AI FinOps, sovereign AI and compliance, edge inference, and the Nvidia compute wars story. Listen to the full episode here, in Substack app, or Apple, Spotify / youtube. I’ll add a companion cost calculator and spreadsheet at intelligentfounder.ai soon. Thanks for listening to Intelligent Founder AI! This post is public so feel free to share it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.intelligentfounder.ai/subscribe

  6. Mar 22

    Is Your Salary About to Come in AI Tokens? Ep.009 - The Token Economy

    Jensen Huang had a week. well, I had a week too, I was traveling so missed entire last week here. Sorry. but I am back now. so lets go back to Jenson who comparatively had more? fun. definitely more interesting. The NVIDIA CEO spent seven days dropping statements at his own GPU Technology Conference, the Morgan Stanley TMT Conference, and finally on the All-In Podcast and by Thursday the internet had a new topic it couldn’t stop arguing about. The clip that went viral? Huang said and I quote. Jensen Huang: “We’re trying to.” “Let me give you the thought experiment: Let’s say you have a software engineer or AI researcher and you pay them $500,000 a year. We do that all the time.” “That $500,000 engineer, at the end of the year, I’m going to ask them, how much did you spend in tokens?” “If that person said, ‘$5,000,’ I will go ape… something else.” “If that $500,000 engineer did not consume at least $250,000 worth of tokens, I’m going to be deeply alarmed. He confirmed NVIDIA is trying to spend $2 billion annually on tokens for its engineering team. He compared engineers who don’t use AI to chip designers still insisting on paper and pencil instead of CAD software. The take was everywhere by Friday. But most of what was written about it, including the Reddit thread with 937 upvotes and 362 comments, only captured half the story. Here’s the full picture. 🎙️ In this podcast episode, we go deeper on everything above, including. * 🔁 The Jevons Paradox explained from scratch — why cheaper tokens always means more spending, not less * 🤖 What an AI agent actually is — and why it consumes 1,000x more tokens than a simple question * ⚠️ Goodhart’s Law in practice — how token burn rate becomes a metric engineers will game * 💼 The 4th pillar of compensation unpacked — what tokens as pay actually means for your financial security * 🌍 The offshoring disruption nobody’s talking about — why flat token costs globally are reshaping hiring maths * 🏢 What SK Telecom did right — and why their model is the one worth copying If you prefer to read? Here’s the breakdown from a 360 degree perspective. What’s a Token, and Why Does It Cost Money? A token is the unit of measurement for AI processing. Every word you type into an AI, every word it writes back, broken down into fragments called tokens. A sentence is roughly 20 tokens. A full document might be several thousand. Every time you run an AI model, tokens are consumed, and tokens cost money. For a simple ChatGPT query: roughly 1,000 tokens. For a research pipeline: 5,000–50,000 tokens. For an AI agent that runs autonomously » searching, coding, testing, iterating, without you pressing a single button, we’re talking hundreds of thousands of tokens per run. A fleet of agents running continuously? Billions of tokens per day. This distinction between “I asked the AI a question” and “the AI is working for me around the clock” is the entire foundation of Huang’s argument. He’s not imagining engineers typing prompts. He’s imagining engineers deploying autonomous AI workforces. Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. The Actual Thesis: Tokens as the 4th Pillar of Compensation Before Huang went viral, VC Tomasz Tunguz of Theory Ventures had already been quietly building this framework. His argument? AI inference is becoming the fourth component of engineering compensation, alongside salary, bonus, and equity. His numbers? a $375K engineer with a $100K token budget has a $475K total package. That token budget doesn’t vest or appreciate, but it enables leverage that no previous tool budget could match. Huang scaled this up to a mandate: a $500K engineer should be consuming at least $250K in tokens. Across NVIDIA’s engineering workforce, that’s a $2B annual token spend, which the company confirmed it’s actively pursuing. The framing is deliberately recruiting-adjacent. “Engineers are now asking ‘what’s my token budget?’ when evaluating offers,” Huang said at GTC. Whether or not this is universally true yet, it’s becoming true fast. [ In this newsletter you get sharp, unfiltered short essays; for full‑length, deep‑dive analysis on AI, subscribe to our companion publication, Intelligent Founder AI. ] The Conflict of Interest Is Real (But Incomplete) The Reddit critique was blunt and structurally correct: NVIDIA sells the GPUs that generate the tokens. Every dollar your engineers spend on tokens flows back, eventually, to GPU demand. Mandating token consumption at scale is demand creation by the person selling the supply. The HP printer analogy made the rounds: “HP would be deeply annoyed if its $200 printer didn’t use $600 of ink.” The Oreo CEO comparison: “Oreo cookies are as important as oxygen.” These are crude but fair. But they’re only half the story. The Jevons Paradox » An economic principle from the 19th century, explains what’s actually happening. When coal-burning technology improved and coal became cheaper, total coal consumption exploded, because efficiency unlocked entirely new applications. The same dynamic is at work with AI tokens: costs have dropped 150x since 2021, yet enterprise inference spending grew 320% in the same period. Cheaper tokens unlock agentic use cases that weren’t viable at higher prices. Agentic use cases consume tokens at orders of magnitude greater scale than simple queries. Total demand surges even as unit cost falls. This is the engine behind NVIDIA’s $1 trillion infrastructure forecast through 2027, and its $215.9B in FY2026 revenu, up 65% year on year. Huang is selling his product and accurately describing a structural shift. Both things are true. The Goodhart’s Law Problem When a measure becomes a target, it stops being a good measure. If you tell your engineers to hit $250K in token spend, some will ask “how do I produce the most value?” and some will ask “how do I hit the number?” The second group will run unnecessarily complex pipelines, use expensive frontier models where a cheaper fine-tuned model would do, leave agents running on idle tasks, and avoid caching that would make them more efficient. The technically correct objective is the inverse of what Huang is incentivizing » token minimization per outcome. Good AI-native engineering means squeezing maximum value out of minimum compute through smart model routing, prompt compression, caching, batching. Measuring raw token volume actively penalizes these skills. The metric that actually matters: Token ROI Ratio value created per dollar of inference consumed. A 10:1 ratio ($10 of revenue per $1 of tokens) is the kind of benchmark forward-looking engineering teams are building toward. That’s the measure worth adopting. The Headcount Question Nobody Is Saying Out Loud Here’s what the viral debate mostly avoided. If a token budget approaching a salary starts to become standard, CFO might l eventually ask? at what token-to-headcount ratio does the compute do enough work that we need fewer humans? And They’re already answering that question. Microsoft cut 15,000 jobs last year while committing $80B to AI infrastructure. Crypto.com laid off 12% of staff in March while revenue was growing, citing AI handling high-volume work. Block cut nearly half its workforce. Around 55,000 US tech layoffs in 2025 were directly attributed to AI-driven restructuring. Huang’s own roadmap puts NVIDIA at 75,000 employees working alongside 7.5 million AI agents? a 100:1 ratio. The “token budget as perk” framing is the friendly version of this story. The CFO version is considerably less friendly. What Smart Founders Should Do With This * Track tokens against outcomes, not as a standalone KPI. Build the denominator: what did $100 of tokens produce? A feature, a resolved ticket, a market analysis? The ratio is the signal. The volume is noise. * Treat token budgets in comp negotiations the same way you’d treat unusual equity terms. Does it vest? What happens if you leave? What’s the cash equivalent? A large non-compounding asset can obscure what you’re actually being paid. * The Jevons Paradox is your tailwind if you’re building on inference infrastructure. Costs will keep falling. Agentic deployment will keep expanding. Products that reduce token waste per outcome, or amplify team output per token consumed, are in a structurally strong position for the next three to five years. * The token economy is real. Huang is both selling chips and describing a genuine transition. The job is to understand which is which — and build accordingly. Thanks for reading Intelligent Founder AI! This post is public so feel free to share it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.intelligentfounder.ai/subscribe

  7. Mar 8

    MWC 2026 Review: What Founders and Builders Need to Know - Ep.008

    Mobile World Congress 2026 (Barcelona, 2–5 March) was the telecom industry's biggest annual gathering, attracting ~109,000 attendees for its 20th anniversary edition. The theme was "The IQ Era" intelligence everywhere, from radio towers to robots. Mobile technologies and services generated $7.6 trillion of economic value in 2025 (6.4% of global GDP), projected to reach $11.3 trillion by 2030. But beneath the demos and keynotes, and behind the branding, the real story is an industry sitting at a crossroads: extraordinary technology, no clear path to new revenue. If you’ haven’t read the pre-conference MWC article yet, here is the link - Now post conference, here’s what actually mattered and what it means if you’re building products, not selling phone contracts. ( total 14 points, top 3 elaborated here, and all rest in the podcast. ) 1. AI Is Eating the Network, But Nobody Knows Who Pays! Every major vendor pitched “AI-native” networks. the idea that future mobile infrastructure should be designed around machine learning from the ground up, not bolted on afterwards. Samsung showed multi-agent AI systems that manage networks end-to-end. Qualcomm launched a modem chip built specifically for “agentic AI”, software agents that act autonomously on your behalf. Deutsche Telekom demoed a system that detects, diagnoses, and fixes network problems across the entire stack using multiple AI agents working together. Almost all of this AI is about cutting costs (fixing faults faster, optimizing radio signals, automating tickets) rather than generating new revenue. The ROI gap isn’t about technology/ it’s about business model imagination. Bain & Company’s post-show report warned that the gap between telco leaders and laggards is widening fast, and any operator that can’t quantify AI business value soon needs to rethink its roadmap. If you’re building AI-powered products, maintenance platforms, agentic systems, automation tools, telcos are becoming a buyer. But expect long sales cycles and a focus on opex reduction, not top-line growth bets. The real opening may be in building the tools that help telcos prove ROI, not the AI models themselves. Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 2. NVIDIA Is Becoming the Platform Telcos Build On A coalition of major operators and vendors committed at MWC to build future 6G networks on NVIDIA’s AI-native, open platforms. The AI-RAN Alliance, the industry group pushing GPU-accelerated radio networks hit 132 members and showed 33 demos. Ericsson demonstrated its cloud RAN software running on NVIDIA hardware with T-Mobile, proving that radio network software can be portable across different chip platforms. A separate initiative called AI-WIN (AI-Native Wireless Networks) brings together American companies to create a sovereign, NVIDIA-based AI network stack, a geopolitical as much as a technical play.​ What does this means? What NVIDIA did to AI is what Android did to phones, as it’s becoming the default development platform. If the mobile industry builds on CUDA (NVIDIA’s software framework), it locks in a dependency that shapes everything downstream: * who supplies hardware, * who writes the software, * who captures the value. For hardware builders and edge compute startups, this is a gravitational shift worth tracking. 3. 6G: Impressive Tech, Zero Paying Customers Multiple vendors showed off 6G technology: terahertz radio transmission, AI-native network architectures, even Li-Fi at 5 Gbps. A coalition of major operators targets commercial 6G from 2029. Ericsson claimed the first pre-standard 6G over-the-air session. The sceptic’s view is that, until someone answers “who pays for this and why?” the whole 6G story stays academic. and that exactly the view of the independent analysts too. Experienced industry are pushing hard against repeating the mistakes of 5G, which over-promised and under-delivered on new revenue. They have argued for evolving from existing 5G infrastructure rather than starting from scratch with a new, expensive core network. One analyst likened AI-RAN with GPUs in base stations to “MEC 2.0”, a reference to multi-access edge computing, which failed not on technology but on economics. and the same risk applies here as well.​ For founder's and builders? If your product depends on 6G capabilities (ultra-low latency, massive device density), you have at least 3–5 years before real deployment. Build for what 5G and Wi-Fi can do today, not what 6G promises for 2030. Here is everything we ‘ve covered in the podcast:- 🔹 Satellites going mainstream and disrupting telcos » Starlink Mobile, Europe’s satellite split, and the ESA × GSMA €100M convergence fund 🔹 Smart glasses as the most tangible consumer product » Google Android XR, Samsung Galaxy XR, and the emerging app platform 🔹 The API economy » GSMA Open Gateway’s progress, where it works (fraud), and where it doesn’t yet 🔹 The $1 trillion scam epidemic » GSMA’s anti-scam push and the Scam Signal API 🔹 Private 5G moving from pilots to production » $8B market, airport and port deployments, flying 5G 🔹 Tokens as the new invisible traffic » why telcos can’t see or monetise AI workloads 🔹 Physical AI doesn’t need 5G or 6G » the case for connectivity-agnostic autonomous systems 🔹 Digital sovereignty reshaping European procurement » sovereign clouds, EU funding, and new compliance expectations 🔹 Quantum-safe telecoms being built today » quantum key distribution, post-quantum cryptography, and telcos as buyers 🔹 Energy efficiency and battery-free IoT » 30%+ savings in production and energy-harvesting sensors 🔹 The revenue problem nobody solved » flat ARPU, value capture moving up the stack, and the culture gap holding telcos back The bottom line from MWC 2026 is simple: the technology is moving faster than the business models, the culture, and the courage to change. For founders and builders, that means building for what works today. 5G, Wi-Fi, and increasingly satellite, while keeping a close eye on where real funding and procurement shifts are happening. Don't bet your roadmap on telco promises that are still years from delivery. Listen to the full episode for the complete breakdown, and follow Intelligent Founder here and on your podcast platform of choice for upcoming deep dives into the signals that matter most , from sovereign AI infrastructure to the satellite power struggle shaping European connectivity. Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.intelligentfounder.ai/subscribe

  8. Feb 22

    Q1 2026 AI Reality Check. What’s Actually Working and What not - Ep.007

    Thursday’s deep dive took longer than expected. the agent war moved fast this week and I’m still debating whether to approach it from the OpenClaw architecture angle or the OpenAI acquisition angle, which arguably makes Anthropic the biggest loser in recent AI history. We’ll see how that plays out soon. The community is as usual divided. There’s real backlash over open-source ownership now that Steinberger is inside OpenAI, which is valid. so nothing new in open source, honestly but what’s interesting is that the backlash has increased activity in the space. Forks like ZeroClaw and PicoClaw are already gaining traction, and if nothing else, the acquisition seems to have lit a fire under independent developers to build harder and faster. Mac Minis are selling like hotcakes because Andrej Karpathy said he bought one. Well, you can absolutely set up an OpenClaw agent for half that cost, but Macs are cool, so I say go for it. On the enterprise and rivals front, not much to report this past week beyond the usual scrambling and fumbling coverage. So instead of chasing that noise, let’s do something more useful. and I’ll come back to this either in next post or a bit later. Now, I was going through my usual linkedin feed this morning and saw the McKinsey’s latest post, with basically 3 rehashed themes from its state of AI’25 report - * scaling is harder than experimenting, * governance matters, * agentic AI is next and few sharper points like sub-millisecond multi-agent orchestration, process context layers for agents, but again these are known issues. and so I thought let me pull down the Q1 reports and see if anyone is actually addressing the gaps or starting new conversations. and that's exactly what we’re covering in this podcast - 📊 Reports analyzed: McKinsey State of AI ‘25 | Deloitte Enterprise AI ‘26 | Stanford AI Index ‘25 | Gartner Predicts ‘26 | NVIDIA Telecom AI ‘26 - Plus: Orgvue Workforce Survey ‘25 and TechCrunch Enterprise VC Survey ‘26 The Headline Numbers first - 88% of organizations now use AI. 62% are experimenting with agents. But only 23% are scaling. And only 6% see real EBIT impact. That’s the funnel. 88 goes in, 6 comes out. Deloitte surveyed 3,235 leaders and found the same wall bur from a different angle. * Only 25% have moved 40% or more of pilots into production. * 74% plan agentic AI within two years. But only 21% have governance ready. and Talent readiness? Just 20%. Gartner’s counter-narrative is actually brutal 40%+ of agentic AI projects will be scrapped by end of 2027. Only about 130 of thousands of “agentic” vendors are genuine. The rest is agent washing. Stanford confirmed the tech barrier is collapsing inference costs dropped 280x in two years. But the organizational barrier? That’s the one that remains. What People Are Actually Saying? On Reddit, practitioners called Gartner’s 40% “generous”, one commenter put it quite bluntly saying: “This would mean lower failure rate than implementing a new CRM.” On LinkedIn, someone reframed McKinsey’s data as: “We spent $47M on AI. Nothing’s different.” The Deloitte governance gap » 74% planning agentic vs 21% ready, got called “a collision course, not a strategy.” Honestly, I agree. The reports are measuring adoption when they should be measuring operational readiness. Those are two very different things. Fair counterpoint though: one Reddit commenter noted Gartner has its own reason for pessimism. their business is being disrupted by AI too. Even the analysts have skin in the game. Quite right actually. What’s Actually Failing vs. What’s Actually Working? So the failures have names now: * Klarna replaced 700 jobs with AI, then rehired humans after quality dropped 22% * McDonald’s killed their AI drive-through after 3 years, the system rang up 260 McNuggets and added bacon to ice cream * Air Canada was held legally liable for a chatbot that invented a fake refund policy * 55% of companies that replaced workers with AI now say it was a mistake Every failure shares one trait: AI bolted on without workflow redesign. But the wins are real and where scope is narrow: * Insurance claims: 245% ROI on structured, well-defined tasks * Revenue leakage detection: $5.7M retained, cost less than one senior hire * Sales forecasting: accuracy jumped 63% to 85%, deal slippage down 28% * Customer support (done right): 55% tickets resolved autonomously, costs down 32% The pattern? Tightly scoped. Domain-specific. Clean data. Human escalation built in. Boring? Yes. Profitable? Absolutely. The Spend Paradox Global AI spending is projected to hit $2 trillion in 2026. But VCs predict vendor consolidation so more money but through fewer vendors. “Budgets will increase for a narrow set of AI products that clearly deliver results and will decline sharply for everything else.” But here’s the Gap Nobody’s Naming! Here’s what none of these reports actually addressing and thats the infrastructure visibility problem. Agents are being deployed on top of systems where operators can’t see the majority of what’s actually happening. The reports talk about adoption. Practitioners talk about failure. But almost nobody talks about the plumbing between “it works in a notebook” and “it works in a live production environment.” The 6% who are winning aren’t winning because they picked the right model. They’re winning because they built the operational backbone » orchestration, governance, and infrastructure that lets agents actually run in production, not just in a demo. Practical Implementation that reports aren’t covering. The Adoption isn’t the problem. Almost everyone has adopted, a little or more. (88% according to McKinsey of course). But I wanted to look at some AI deployment at scale examples because the reports data looked more theoretical otherwise. so here is what I found, and these are not part of any of the reports we are talking about. Won:Morgan Stanley (AI advisor assistant), Accelirate/UiPath (insurance claims), Anysphere/Cursor (AI coding), SK Telecom + Samsung (AI-RAN), Telecom sector broadly (autonomous networks). Lost:Klarna (fired 700, rehired humans), S&P Global’s 42% graveyard (enterprises scrapping initiatives), MIT’s 95% (zero P&L impact across $44B in investment). THE PATTERN Every winning example shares the same DNA: * Narrow, well-defined task (not “enhance productivity”) * Workflow redesigned around AI (not AI added to step 7) * Clean, structured data or proprietary data advantage * Measurable financial outcome tied to the deployment * Human-in-the-loop where judgment matters Every failure shares the opposite: * Vague goal (”improve efficiency”) * AI bolted onto broken processes * No governance before scaling * No KPI tracking * Fired people before understanding impact The pattern is the same every time, the winners redesigned the workflow before deploying, the losers bolted AI onto what was already broken. In simple language, the winners deployed AI into production, embedded it into core workflows, and got measurable business outcomes (revenue, cost savings, ROI). The losers adopted AI, ran pilots, and never made it past the proof-of-concept stage, or deployed it recklessly and had to reverse course. The Bottom Line AI adoption is universal. AI value capture is not. The technology has arrived. The organizations haven’t. 2026 won’t be the year AI transforms everything. It’ll be the year the shakeout begins, vendor consolidation, governance debt coming due, and pilot graveyards getting cleaned out. The next frontier isn’t a better model. It’s physical AI, sovereign infrastructure, and agentic orchestration at the edge. The winners won’t be those with the best algorithms. They’ll be those with the best plumbing. The full podcast digs deeper into all five reports and the gaps between them. Listen to the full breakdown on the Intelligent Founder podcast. Subscribe so you don't miss what comes next. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.intelligentfounder.ai/subscribe

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