LLMs in 2026: Why Fundamentals Matter More Than Features
Sebastian Raschka’s advice to tech leaders: understand the core of large language models before chasing features.
When you know the fundamentals, the hype fades.
Out now on Digital Disruption.
#TechPodcast #LargeLanguageModels #AIStrategy
Tool Use Is the Real LLM Breakthrough in 2026
The next leap in LLM progress is not memory. It’s tool use.
Instead of guessing how many R’s are in “strawberry,” models can call a Python tool and calculate it accurately.
Sebastian Raschka explains why tool-augmented AI is driving real progress in 2025 and 2026.
Out now on Digital Disruption.
#TechPodcast #LargeLanguageModels #AICoding
If AI takes everyone’s jobs… who’s going to buy anything?
AI and jobs is a conversation that is raising a lot of concern right now.
Many are saying AI is taking all the jobs.
But the roles seeing this change were already being automated for years.
The better question isn’t “Will AI take everything?”
It’s “How will AI change the work we do?”
Watch the full episode with Ed Zitron @BetterOfflinePod as he breaks down one of tech’s most misunderstood narratives, the myth of AI-driven job loss, revealing who’s really being replaced.
#AI #FutureOfJobs #CareerTalk #AIBubble #TechDebate #EntryLevelJobs #EdZitron #CareerTalk #TechDebate
Are LLM Benchmarks Misleading?
How do you fairly compare large language models?
Sebastian Raschka says model evaluation is one of the biggest problems in AI right now. Even “LLM judges” are just other models scoring answers on a rubric.
Benchmarks matter, but context matters more. Out now on Digital Disruption.
#TechPodcast #LargeLanguageModels #AIEvaluation
Build an LLM in a Weekend? Why It Changes Everything
Want to really understand large language models?
Sebastian Raschka says build a small one yourself. Once you see the core, the hype turns into mechanics.
Out now on Digital Disruption. Follow for more real talk on AI.
#TechPodcast #LargeLanguageModels #AIStrategy
AI Coding Tools in 2026: Why Developers Still Matter @SebastianRaschka
AI can build the first version fast, like dark mode in a day. But shipping still means testing, tweaking, and iteration.
@SebastianRaschka on Digital Disruption. Out now on all your favourite platforms.
#TechPodcast #AICoding #LargeLanguageModels
LLMs in 2026: What’s Real, What’s Hype, and What’s Coming Next
Is AI actually going to replace developers? Or is the hype getting ahead of reality?
On this episode of Digital Disruption, we’re joined by Sebastian Raschka, AI Research Engineer and author.
@SebastianRaschka sits down with Geoff Nielson to unpack the real state of Large Language Models (LLMs) in 2026. As an LLM research engineer, Sebastian bridges deep technical expertise with practical, real-world AI implementation. In this conversation, he cuts through AI hype to focus on what’s actually achievable with modern LLMs, reasoning models, reinforcement learning, and inference scaling and where the limitations still exist. Sebastian explains why most companies should not build a large language model from scratch, but also why understanding the fundamentals may be one of the most important investments technology leaders can make.
This conversation breaks down:
◼️Why coding is currently the strongest LLM use case
◼️Why “reasoning” models still fail simple tasks like counting letters in “strawberry”
◼️The reality behind Math Olympiad gold-level AI claims
◼️The true cost of training large models (millions in GPU compute)
◼️The privacy risks of uploading proprietary data into APIs
◼️How enterprises should think about fine-tuning vs API-based prompting
◼️Why benchmarks and leaderboards can be misleading
Sebastian Raschka has over a decade of experience in artificial intelligence and machine learning. His work bridges academia and industry, serving as a Senior Engineer at Lightning AI and as a faculty member at the University of Wisconsin–Madison. He is the author of Build a Large Language Model from Scratch and is widely recognized for his practical, code-driven approach to AI education and research. His expertise lies in LLM research, transformer architectures, reinforcement learning, and the development of high-performance AI systems, with a strong focus on real-world implementation.
In this video:
00:00 Intro
01:23 The Rise of “Reasoning” and Thinking Models
03:06 Inference scaling vs training scaling
06:17 What LLMs are actually good (and bad) at
07:09 The “Strawberry” Problem and Reasoning Limits
09:00 Tool use and why LLMs don’t need to count letters
10:20 Math Olympiads & self-refinement techniques
12:01 Why coding is the killer use case
13:28 Does AI make developers obsolete?
18:02 The Reality of 10x developer productivity claims
21:43 Generalist vs specialized models
23:53 Build from scratch vs fine-tune vs API prompting
25:01The true cost of training an LLM
27:33 API customization vs owning your model
29:12 Who should build an LLM from scratch?
33:16 Data requirements & why you need terabytes
34:28 Enterprise data challenges
35:40 Retrieval-Augmented Generation (RAG) explained
46:05 Multi-agent systems & tool calling
49:48 The problem with LLM benchmarks
55:43 Using LLMs as judges
58:00 Biggest misconceptions about LLMs
1:04:19 Reinforcement learning with verifiable rewards
1:06:32 Advice for technology leaders
1:11:48 Escaping AI hype through fundamentals
#ai #coding #futureofwork #llm #softwaredeveloper #aicoding #largelanguagemodels
Connect with Sebastian:
LinkedIn: https://www.linkedin.com/in/sebastianraschka/
X: https://x.com/rasbt
Our links:
Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcast
Follow us on YouTube: https://www.youtube.com/@InfoTechRG
Check out other episodes of Digital Disruption: https://youtube.com/playlist?list=PLIImliNP0zfxRA1X67AhPDJmlYWcFfhDT&feature=shared
Will AI Replace Lawyers? Or Just Help Consumers?
AI is not replacing lawyers first. It is filling the gap where no lawyer would take the case, like a $20 to $50 refund.
Josh Browder explains why regulation slows “AI law firms,” but AI can still act as a consumer advocate and a tool that saves lawyers time and money.
Out now on all your favourite platforms.
#TechPodcast #LegalTech #AI
ChatGPT Mirrors Us: Why AI Training Needs Human Cleanup
ChatGPT predicts the next word by training on huge slices of the internet, books, and media.
Josh Browder’s point: AI is a reflection of us, and not all of human history or online content is good.
That is why humans still have to label, filter, and clean the data, conspiracy theories and all.
Out now on all your favourite platforms.
#TechPodcast #AITraining #AIEthics
AI vs AI Negotiations: Fighting Dark Patterns With Automation
Some companies profit from dark patterns because cancelling is a pain.
Josh Browder explains how AI agents can negotiate bills and cancel subscriptions, sometimes AI vs AI against corporate chatbots.
Fight fire with fire. Out now on all your favourite platforms.
#TechPodcast #ConsumerRights #AI
$60 an Hour to Teach Robots to Fold Laundry
They paid people $60 an hour to fold laundry on camera so robots can learn it.
Josh Browder says 2026 is the year robotics goes mainstream.
Out now on all your favourite platforms.
#TechPodcast #Robotics #A
The “Robot Lawyer” on AI in Law: What Should Never Be Automated?
AI is great with rules, but law is also people and emotions.
This week on Digital Disruption, we’re joined by Josh Browder, the “robot lawyer” behind DoNotPay, to break down where AI can help and where justice must stay human.
Out now on all your favourite platforms.
#TechPodcast #LegalTech #AIinLaw
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LLMs in 2026: Why Fundamentals Matter More Than Features