What podcasts say about Sebastian Raschka
Every statement, with the speaker, the exact quote and the moment it was said.
What Sebastian Raschka has said on podcasts
20 statements · 13 positive · 2 negative · 1 mixed · 4 neutral
No company will retain exclusive access to AI technology.
“I don't think there will be a clear winner in terms of technology access.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 18:05
Budgets and hardware, rather than ideas, will differentiate frontier AI companies.
“the differentiating factor will be budget and hardware constraints”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 18:12
The AI industry will not become winner-take-all.
“I don't see currently take it all scenario where a winner takes it all.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 18:25
In AI competition, the newest released model is usually the strongest.
“the most recent model is probably always the best model.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 23:00
Tool use can reduce LLM hallucinations by replacing memorization with external verification.
“one of the best ways to solve hallucinations is to not try to always remember information or make things up”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 47:17
Modern LLM architectures remain fundamentally similar to GPT-2.
“It's not really fundamentally that different. It's still the same architecture.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 58:32
Recent LLM capability gains primarily come from training algorithms rather than new architectures.
“it's more on the algorithmic side rather than the architecture”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 1:00:15
Autoregressive transformers remain state of the art despite emerging alternatives.
“there's nothing that has replaced the autoregressive transformer as state of the art model”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 1:02:09
All major training and inference scaling methods remain useful.
“I do think all of these pre training, mid training, post training, inference scaling, they are all still things you want to do.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 1:18:32
Pretraining supplies knowledge while post-training unlocks task-solving skills.
“post training is more like the skill unlock where pre training is like soaking up the knowledge essentially”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 1:22:54
Domain-specific proprietary data will sustain scaling gains beyond general-purpose LLMs.
“I do think scaling in that sense might be still pretty much alive if you also look in domain specific applications.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 1:30:57
Learners should reserve daily offline study time while using LLMs for other tasks.
“maybe the trick here is you make dedicated offline time where you study two hours a day and the rest of the day use LLM.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 1:51:30
RLVR rapidly improved a Qwen 3 model’s Math500 accuracy from 15% to 50%.
“The base model had an accuracy of about 15%, just 50 steps. Like in a few minutes with RL VR the model went from 15% to 50% accuracy.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 1:58:56
Fair LLM evaluation requires benchmarks created after deployment cutoff dates.
“the only fair way to evaluate an LL is to have a new benchmark that is after the cutoff date when the LLM was deployed.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 2:01:48
RLVR scales by repeatedly testing models on increasingly difficult verifiable problems.
“with RLVR you literally give the model well, you let the model solve more and more complex, difficult problems.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 2:08:45
Aspiring AI practitioners should begin by implementing a simple model from scratch.
“I would personally start, like you said, implementing a simple model from scratch.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 2:13:05
Text diffusion models will complement rather than replace autoregressive LLMs.
“I don't think the text diffusion model is going to replace auto regressive LLMs but it will be something maybe for quick, cheap at scale tasks.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 2:47:25
Tool use reduces, but does not eliminate, LLM hallucinations.
“Not solve it, but reduce it.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 2:49:35
AI capability improvements will continue through amplification rather than a paradigm shift.
“I can see that continuing for a long time.”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 3:41:21
Nvidia’s main competitive moat is the CUDA ecosystem rather than GPU hardware alone.
“the mode of Nvidia is, is probably not just the gpu, it's more like the Cuda ecosystem”
Open the episode · #490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGIListen at 4:15:13
Statements are attributed to the speaker as said on the episode and reflect their view at the time, not PodLume's. They are not advice.