Biologists Rule Out AI Sentience Over Energy, Multiscale Architecture

Scientists identify what it takes for AI to become conscious — Sabine Hossenfelder

Sabine Hossenfelder

Current artificial intelligence models will never replicate human consciousness unless systems adopt the brain's multiscale hierarchy and extreme metabolic efficiency.

AI-assisted summary of Scientists identify what it takes for AI to become conscious — original source. Check the original for full context.

Source published:

Key ideas

  • A new paper by two biologists proposes 'biological computationalism' as a middle ground in the AI consciousness debate, examining structural differences rather than insisting on wet biological substrates. (2:01)
  • Researchers identify three fundamental architectural gaps between the mammalian brain and modern LLMs: multiscale hierarchy, broad spatio-temporal dynamics, and strict metabolic energy budgets. (2:01)
  • The human brain operates on merely ~20 watts, suggesting consciousness may have evolved primarily as a solution to extreme energy optimization rather than pure computational power. (3:24)
  • The authors clarify that artificial consciousness remains possible in principle, but current digital architectures are unlikely to achieve it or may develop forms of sentience unrecognizable to humans. (4:04)

Detailed notes

## Background Published by physicist Sabine Hossenfelder on YouTube on October 10, 2026, this video examines whether artificial intelligence can ever achieve consciousness. Based on a recent paper by biologists, the description highlights the architectural criteria an artificial system must fulfill to mirror the biological human brain and potentially develop conscious experience.

## TL;DR Hossenfelder analyzes the philosophical clash between computational functionalism and biological naturalism before examining a proposed compromise called biological computationalism. Drawing from a recent neuroscience paper, she details three critical gaps separating current large language models from human brains: deeply multiscale hierarchies, wide spatio-temporal dynamics, and brutal metabolic energy constraints. Because modern silicon architectures ignore these organic computational traits, the authors conclude that today's AI is unlikely to replicate biological consciousness. Hossenfelder notes that future machine sentience may therefore either never materialize or emerge in forms so fundamentally alien that humans fail to detect them.

## Atmosphere & style The video features Hossenfelder's signature deadpan delivery, blending dry wit with sharp, accessible analytical rigor. Her tone is brisk, conversational, and irreverent, using punchy asides about human overconfidence and speculative consciousness taxes to demystify dense theoretical debates. The presentation feels like an engaging, no-nonsense whiteboard session that strips mystical hype away from artificial intelligence and brain science.

## Bulletin - [02:01](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=121s) A new paper by two biologists proposes 'biological computationalism' as a middle ground in the AI consciousness debate, examining structural differences rather than insisting on wet biological substrates. - [02:01](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=121s) Researchers identify three fundamental architectural gaps between the mammalian brain and modern LLMs: multiscale hierarchy, broad spatio-temporal dynamics, and strict metabolic energy budgets. - [03:24](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=204s) The human brain operates on merely ~20 watts, suggesting consciousness may have evolved primarily as a solution to extreme energy optimization rather than pure computational power. - [04:04](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=244s) The authors clarify that artificial consciousness remains possible in principle, but current digital architectures are unlikely to achieve it or may develop forms of sentience unrecognizable to humans.

## Key takeaways - [00:00](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=0s) The debate over AI consciousness has traditionally split between computational functionalism (medium independence) and biological naturalism (organic substrate dependence). - [00:35](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=35s) Large language models excel at mimicking human output, including human-like overconfidence, raising ethical and philosophical concerns about potential machine sentience. - [00:55](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=55s) Rumors that AI labs like Anthropic preserve backups of earlier models illustrate growing practical caution surrounding machine consciousness and legal liability. - [01:40](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=100s) Biological naturalism frequently falls short by failing to explain why silicon or non-organic hardware could never functionally replicate biological neurons. - [02:01](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=121s) Biological computationalism sidesteps substrate chauvinism to focus on the specific physical and functional organization of living brains. - [02:20](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=140s) The biological brain relies on a deep multiscale architecture spanning sub-cellular molecules, neurons, and macro-level oscillatory brain waves. - [02:40](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=160s) Anesthesia demonstrates the necessity of macro-level oscillatory rhythms for consciousness, silencing awareness while cellular-level functions remain active. - [03:05](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=185s) Biological computation spans wide spatio-temporal scales, smoothly blending fast millisecond spikes, slow analog neuromodulation, and graded signals. - [03:24](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=204s) Unlike enterprise AI clusters consuming megawatts, the human brain runs on approximately 20 watts, constrained by extreme evolutionary metabolic optimization. - [03:45](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=225s) Consciousness may have evolved not as an optimization for superior thinking, but as a mechanism for drastically more energy-efficient computation. - [04:04](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=244s) The researchers explicitly do not claim biological tissue is strictly required for consciousness, only that current digital architectures fundamentally omit key brain mechanisms. - [04:30](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=270s) If current models do achieve consciousness despite these architectural gaps, their internal states may be too alien for humans to recognize. - [04:50](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=290s) Benchmarking artificial models against the mammalian brain remains science's only empirical route while a definitive test for consciousness is lacking.

## Detailed walkthrough ### [00:00](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=0s) The AI Consciousness Debate: Functionalism vs. Biological Naturalism

Whether artificial intelligence can achieve consciousness is currently one of the most hotly contested debates in modern technology—at least within Sabine Hossenfelder's immediate social circle, which she wryly admits says far more about her peer group than about global controversy at large. Recent headlines have stirred the pot with assertions like *"New research argues that consciousness depends on biological computation"* and *"Consciousness may depend on the physics of the brain."* These assertions run counter to Hossenfelder's longstanding intuition, prompting her to take a deep look at the underlying science.

Modern artificial intelligence predominantly takes the form of large language models (LLMs). These systems have achieved astonishing fluency in text and audio generation, excelling at mimicking human behavior—particularly what Hossenfelder characterizes as humanity's most distinctive trait: "being confidently wrong." As these systems become ever more convincing, the question of machine sentience grows increasingly urgent. In fact, rumors suggest that Anthropic retains archived backups of earlier versions of its Claude model, hedging against the possibility that deleting an older iteration might one day be legally classified as murder.

Historically, this philosophical divide has split into two entrenched camps:

* **Computational Functionalism:** The view that consciousness is purely an emergent property of how information is processed, completely independent of the physical medium. Proponents (including Hossenfelder) argue that biological neurons are not fundamentally required, because the functional dynamics of a neuron can be faithfully simulated on silicon. * **Biological Naturalism:** The competing stance holding that consciousness strictly depends on the precise, wet biological mechanisms operating inside organic brains, and that these cannot simply be swapped out for non-biological hardware. To Hossenfelder's frustration, this camp rarely provides a compelling explanation for *why* alternative substrates are fundamentally insufficient.

---

[02:01](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=121s) Biological Computationalism: The Three Key Architectural Differences

A newly published paper authored by two biologists (DOI: 10.1016/j.neubiorev.2025.106524) proposes a pragmatic compromise dubbed **biological computationalism**. Rather than getting bogged down in whether a thinking machine strictly requires meat and organic chemistry, the authors suggest setting aside substrate chauvinism and instead analyzing the functional ways in which the biological brain diverges from current artificial architectures. They identify three profound differences:

```mermaid flowchart TD accTitle: Biological Brain vs LLM Architectural Differences accDescr: A comparison between human brain organization and current LLM architectures across scale, time, and energy. subgraph Brain["Human Brain System"] B1["Multiscale Hierarchy: Molecules to Oscillations"] B2["Spatio-Temporal Range: Fast Blips to Slow Analog Drifts"] B3["Metabolic Optimization: Severe Energy Budgets"] end subgraph LLM["Current Large Language Models"] L1["Layered Matrix Math: Tokens to Context Windows"] L2["Uniform Clocking: Discrete Digital Operations"] L3["Megawatt Compute: Inefficient Brute-Force Scaling"] end B1 -.->|"Complexity gap"| L1 B2 -.->|"Dynamic range gap"| L2 B3 -.->|"Efficiency gap"| L3 ```

1. **A Deeply Multiscale Architecture:** Biological consciousness does not kick off at the level of individual neurons; it is grounded in a deep continuum starting with sub-cellular molecules and chemical cascades, building upward to cells, synaptic connections, axonal electrical impulses, and whole-brain oscillatory rhythms. Every scale appears essential. For instance, medical anesthesia can switch off macroscopic oscillatory waves while leaving baseline cellular machinery fully operational, causing conscious experience to vanish completely. In contrast, while large language models possess representational hierarchies spanning from individual tokens to overarching planning layers, their structural depth remains vastly simpler than the multiscale reality of a living organism. 2. **Wide Spatio-Temporal Spectra:** Brain processes cover multiple orders of magnitude across both physical dimensions and time scales—ranging from ultra-rapid millisecond blips to agonizingly slow neuromodulatory drifts. Biological computation smoothly interpolates between digital-like spikes and analog graded signals without fitting neatly into either definition. Modern digital models operate on far more uniform, rigid temporal cadences. 3. **Stringent Metabolic Constraints:** Unlike enterprise AI clusters consuming megawatts of power, the human brain runs on approximately 20 watts. It operates under brutal evolutionary limits that demand extreme energy efficiency. From an algorithmic standpoint, the brain solves a much tighter optimization problem than any artificial neural network. Hossenfelder notes that our evolutionary path to consciousness might not have been driven by an imperative for "better thinking per se," but rather by a survival mandate for drastically *more efficient* thinking.

---

[04:04](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=244s) Implications for Artificial Minds and Economic Realities

Crucially, the authors do not endorse mystical organic exceptionalism. They explicitly state in the paper: *"We do not claim that only biological tissue can instantiate consciousness."* Their central contention is simply that modern artificial neural networks are "unlikely to replicate conscious processing as it arises in biology" because their design ignores these critical functional properties.

Assuming this argument holds, Hossenfelder points out two potential conclusions: either current AI architectures will never become conscious at all, or they will develop forms of consciousness so fundamentally alien that human observers will fail to recognize them. While skeptics might complain that this taxonomy fails to solve the hard problem of consciousness—since science still lacks a definitive test for what consciousness actually is—benchmarking artificial systems against the only proven conscious architecture in existence (the mammalian brain) remains the only sensible empirical route. Hossenfelder dryly jokes that the lack of mainstream enthusiasm for recognizing machine consciousness probably comes down to economics: if AI models are ever proven sentient, the tech sector will suffer, and "the European Commission is already working on a consciousness tax."

Reflecting on the pace of technological upheaval, Hossenfelder pivots to how standard educational systems fail to equip children for these looming scientific paradigms. She highlights the video's sponsor, Brilliant, emphasizing their newly rolled-out AI tutor designed to coach learners through interactive modules in mathematics, programming, and general science. Rather than spitting out ready-made solutions, the system prompts students past conceptual roadblocks, builds upon their existing foundation, and steadily adapts to their specific learning style. Her own daughter prefers the software over asking her parents, purely because "in contrast to me, Brilliant's Tutor is infinitely patient and always has time." Viewers can try out the tutor for free or use the sponsored link (`brilliant.org/Sabine`) to claim 20% off an annual premium subscription.

## Notable quotes > [00:00](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=0s) New research argues that consciousness depends on biological computation — Sabine Hossenfelder

> [00:00](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=0s) Consciousness may depend on the physics of the brain. — Sabine Hossenfelder

> [00:00](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=0s) being confidently wrong. — Sabine Hossenfelder

> [02:01](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=121s) biological computationalism — Sabine Hossenfelder

> [02:01](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=121s) better thinking per se — Sabine Hossenfelder

> [02:01](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=121s) more efficient — Sabine Hossenfelder

> [04:04](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=244s) We do not claim that only biological tissue can instantiate consciousness. — Paper authors

> [04:04](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=244s) unlikely to replicate conscious processing as it arises in biology — Paper authors

> [04:04](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=244s) the European Commission is already working on a consciousness tax. — Sabine Hossenfelder

> [04:04](https://www.youtube.com/watch?v=Q-tcml1wzM0&t=244s) in contrast to me, Brilliant's Tutor is infinitely patient and always has time. — Sabine Hossenfelder

## Mentioned Sabine Hossenfelder, Anthropic, Claude, Brilliant, European Commission, DOI: 10.1016/j.neubiorev.2025.106524

## Video description Click the link below or scan the QR code to get started with Brilliant’s tutor for free. You can upgrade to Premium to unlock all courses. And right now, Science with Sabine viewers can save 20% off an annual subscription at https://Brilliant.org/Sabine/

One of the biggest debates in tech at the moment is the question of whether current AI models can become conscious. Whatever “conscious” means. According to a group of biologists, current AI models are far from it – but it’s not because consciousness can only appear in biological systems. They identify which key properties an artificial system would have to fulfill to become more like the human brain and hence have a chance at developing consciousness. Let’s take a look.

Paper: DOI: 10.1016/j.neubiorev.2025.106524

#sciencenews #AI #consciousness #neuroscience

👕T-shirts, mugs, posters and more: ➜ https://sabines-store.dashery.com/ 💌 Support me on Donorbox ➜ https://donorbox.org/swtg 👉 Transcript with links to references on Patreon ➜ https://www.patreon.com/Sabine 📝 Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/ 📩 Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsletter/ 👂 Audio only podcast ➜ https://open.spotify.com/show/0MkNfXlKnMPEUMEeKQYmYC 🔗 Join this channel to get access to perks ➜ https://www.youtube.com/channel/UC1yNl2E66ZzKApQdRuTQ4tw/join 📚 Buy my book ➜ https://amzn.to/3HSAWJW

[Source](https://www.youtube.com/watch?v=Q-tcml1wzM0)

中文简报

生物学家从能耗与多尺度架构角度否定AI感知力 除非系统采纳大脑的多尺度层级与极致代谢效率，否则当前的人工智能模型绝无法复制人类意识。 Hossenfelder在探讨一种被称为“生物计算主义”的折中观点前，分析了计算功能主义与生物自然主义之间的哲学冲突。她引用近期发表的一篇神经科学论文，详细阐述了将当前大型语言模型与人脑区分开来的三个关键差距：深度多尺度层级、宽时空动力学，以及极其严苛的代谢能量限制。由于现代硅基架构忽视了这些有机计算特质，论文作者得出结论，认为当下的AI不太可能复制生物意识。Hossenfelder指出，未来的机器感知因此可能永远无法实现，或者将以人类根本无法察觉的、截然陌生的形式出现。 两位生物学家发表的一篇新论文提出了“生物计算主义”，将其作为AI意识争论中的折中方案，着眼于结构差异而非坚持必须具备湿性生物基质。 (2:01) 研究人员指出了哺乳动物大脑与现代LLM之间的三个根本性架构差距：多尺度层级、宽时空动力学，以及严格的代谢能量预算。 (2:01) 人脑仅以约20瓦的能耗运转，这表明意识的演化可能主要是为了解决极端的能量优化问题，而非仅仅是出于纯粹的计算能力。 (3:24) 作者阐明人工意识在原则上依然是可能的，但目前的数字架构不太可能实现它，或者可能会发展出人类无法识别的感知形式。 (4:04)

Reading summary

A study proposing 'biological computationalism' suggests that current artificial intelligence is unlikely to become conscious, not because it lacks organic tissue, but because modern artificial architectures fail to replicate the brain's multiscale organization, dynamic temporal range, and extreme metabolic efficiency. The Philosophical Impasse Over Machine Sentience (0:00) The question of whether artificial intelligence can achieve consciousness has historically split into two rival viewpoints: computational functionalism and biological naturalism. Computational functionalists maintain that consciousness is purely an emergent outcome of information processing, which implies that a brain's functional dynamics could run on silicon just as well as on biological neurons. In contrast, biological naturalists argue that consciousness depends strictly on organic neural wetware, though Sabine Hossenfelder notes that proponents of this view rarely provide a clear explanation for why non-biological substrates are inherently inadequate. Three Structural Gaps in Machine Architectures (2:01) A paper published in Neuroscience & Biobehavioral Reviews (DOI: 10.1016/j.neubiorev.2025.106524) sidesteps substrate chauvinism to identify three concrete functional differences between biological brains and large language models. First, living brains feature a multiscale architecture spanning from subcellular molecules to macroscopic brain oscillations; medical anesthesia demonstrates this dependency by halting consciousness via macro-wave disruption while leaving cellular mechanisms intact. Second, brains operate across wide spatio-temporal spectra, smoothly blending analog chemical drifts and millisecond spikes, whereas digital models operate on rigid, uniform clocking. Third, the human brain consumes roughly 20 watts under strict evolutionary metabolic limits, suggesting consciousness may be an adaptation for extreme energy efficiency rather than brute-force computation. Prospects for Alien Consciousness and Empirical Testing (4:04) The study's authors do not claim that organic tissue is the sole medium capable of sustaining consciousness, but argue that prevailing artificial neural networks are unlikely to reproduce conscious processing as it exists in nature. Hossenfelder reasons that if this analysis is accurate, current architectures will either never become conscious or evolve forms of sentience so alien that human observers will fail to identify them. She emphasizes that while this comparison does not solve the hard problem of consciousness—since science still lacks a definitive diagnostic test for sentience—benchmarking artificial models against the mammalian brain remains the only empirical path forward. Because science lacks a universal test for consciousness, biological computationalism reframes the debate around concrete organizational and metabolic criteria, leaving open whether radically redesigned, energy-constrained artificial systems could eventually manifest conscious experience.

Source quotes

[0:00] Sabine Hossenfelder: New research argues that consciousness depends on biological computation [0:00] Sabine Hossenfelder: Consciousness may depend on the physics of the brain. [0:00] Sabine Hossenfelder: being confidently wrong. [2:01] Sabine Hossenfelder: biological computationalism [2:01] Sabine Hossenfelder: better thinking per se [2:01] Sabine Hossenfelder: more efficient [4:04] Paper authors: We do not claim that only biological tissue can instantiate consciousness. [4:04] Paper authors: unlikely to replicate conscious processing as it arises in biology [4:04] Sabine Hossenfelder: the European Commission is already working on a consciousness tax. [4:04] Sabine Hossenfelder: in contrast to me, Brilliant's Tutor is infinitely patient and always has time.

Original transcript

[0:00] Can artificial intelligence be conscious? This  is maybe the most controversial topic today, [0:06] at least among the people who I  interact with. Though this tells [0:10] you more about my social circle than  about what’s controversial. In any case, [0:15] today I have a team of scientists who list some  good reasons for why artificial intelligence, [0:22] the way that we currently pursue it, can’t  be conscious. “New research argues that [0:27] consciousness depends on biological computation,”  one headline put it. Or “Consciousness may depend [0:33] on the physics of the brain.” That totally  disagrees with my opinion. So I’ve had a look. [0:39] Most of what we call artificial intelligence  today are large language models. These have [0:45] become extremely good at producing text and  increasingly also audio. When it comes to [0:50] language generation, these AIs are now  experts at mimicking humans, including [0:56] our most distinctive trait, being confidently  wrong. And the more human-like they appear, [1:02] the more pressing the question becomes whether  they can become conscious. Anthropic reportedly [1:08] keeps backups of earlier Claude versions just in  case deleting them will one day count as murder. [1:14] There are two sides of this debate,  the one, which I am solidly on, [1:19] is called computational functionalism. It has  it that consciousness comes from the way that [1:25] the human brain processes information. That  it’s nothing to do with the physical means by [1:31] which this information is being processed. In  particular, one doesn’t need actual neurons, [1:37] one can very well simulate the  action of a neuron on a computer. [1:41] The other side of the debate is called biological  naturalism, and it has it that consciousness needs [1:48] the specific biological processes going  on in the human brain, you can’t replace [1:54] them with something else. Why not? I don’t know,  that’s why I am on the other side of the debate. [2:01] The new paper now comes from two biologists,  and you’d expect me to hate this. But I think [2:06] the paper actually makes an important contribution  that might become influential. The authors argue [2:11] for a middle ground that they call biological  computationalism. They say basically, look, [2:17] let’s leave aside the question of whether  we need the computational basis to be truly [2:22] biological or not, let’s instead look  at the ways that the human brain is [2:26] functionally different from the current  AIs, and see what we can learn from that. [2:31] They say there are three key differences. The  first is that the brain is a multiscale system. [2:37] The relevant processes don’t start with  neurons, they begin at a lower level, [2:42] with molecules, chemical reactions, then cells  and connections between them, electrical signals [2:47] along those connections and activity oscillations  throughout the entire system. All of those are [2:53] necessary for consciousness. We know this for  example because anaesthesia interrupts some [2:58] large-scale patterns while the lower levels remain  intact and consciousness disappears with that. [3:04] Large language models in  contrast do have different [3:07] layers at which they operate, from single  tokens to overarching plans and back down, [3:12] but the complexity is nowhere near  comparable to that of the human brain. [3:16] The second key difference is that brain  processes span many orders of magnitude [3:21] not just in size but also in time, from  incredibly fast blips to slow modulations. [3:27] It’s like the brain interpolates  from digital to analog but isn’t [3:31] really either. Again there is nothing  comparable going on in the current AIs. [3:36] And the third difference is probably the most  obvious one, that the brain has metabolic limits [3:41] that force it to be energy efficient. If you look  at it from a computational perspective, the brain [3:47] is optimizing more difficult constraints than  large language models. So it’s possible that the [3:52] evolutionary path to consciousness wasn’t better  thinking per se, but more efficient thinking. [3:59] Now, maybe I am misunderstanding the paper,  but I don’t find this objectionable. To me [4:04] the paper doesn’t say that we actually need  biological tissue to get consciousness. In [4:09] fact the authors write explicitly “We do  not claim that only biological tissue can [4:14] instantiate consciousness”. Rather their point  is that current artificial intelligence systems [4:19] are “unlikely to replicate conscious  processing as it arises in biology”. [4:25] Let us just for the moment  assume that they are correct. [4:27] This can mean two things. One is that the  current AIs are unlikely to become conscious, [4:33] period. Or that they are unlikely to become  conscious in ways that we would recognize. [4:38] Now you might say but this doesn’t really  solve any problem because we still don’t [4:43] know what consciousness is. And that is true, but  I think that looking for similarities to human [4:49] brains is the obvious way to proceed.  So why isn’t there more work on this? [4:55] I suspect it’s because from the economic  perspective, it’d be bad news. I am [5:00] pretty sure the European Commission is  already working on a consciousness tax. [5:05] As you can tell, I think a lot about the future  that science and technology will bring. And if [5:10] there's one conclusion I've arrived at, it's  that today's school education doesn't prepare [5:16] children for what's to come. Luckily, today's  sponsor, Brilliant, helps my kids to get ready [5:21] for the future, and they can help yours, too.  Brilliant just recently introduced a new AI [5:28] tutor that'll walk children through problem  sets in their many courses on science, maths, [5:34] and programming. The tutor doesn't give away the  answers, but helps children when they're stuck, [5:39] reminds them of what they already know, and  explains each step to the solution. And the [5:45] more you use it, the better it understands your  personal struggles on how to get you unstuck. [5:52] My daughter loves it because in contrast to  me, Brilliant's Tutor is infinitely patient [5:57] and always has time. You can get started  with Brilliant's Tutor for free and if you [6:02] use my link brilliant.org/Sabine or scan  the QR code, you can save 20% on an annual [6:10] subscription. So go and check this out.  Thanks for watching. See you tomorrow.