Please check out the accompanying podcast episode for this article, where I discuss the implications of merging biology and machine intelligence in more detail. You can listen below and you can find it on Spotify or your favorite podcast platform.
Introduction
The excitement surrounding AI and AGI is well known.
It composes poetry.
It defeats masters of chess.
It also makes a lot of predictions.
It’s like a cunning trick, though. It resembles an excellent copy.
I then came across MetaBOC.
And to be honest, MetaBOC kind of worries me.
It’s more than simply a lab experiment; it’s a glimpse of the potential for AI to develop in the future. And that sounds kinda crazy. They’re fusing actual human brain cells with microchips. Living neurons do not just code but also process information. They’re trying to grow intelligence, not just simulate it.
Forget algorithms for a second. Think: biological learning—actual biochemical reactions.
Do genuine smarts need that messy, organic base? Because if it does, this changes everything. Suddenly, the questions aren’t just about tech.
They’re about… well, everything. Are we looking at the next step to AGI?
Or are we stumbling into Something completely unknown?
This is something that might makes you ask “are we playing with fire?“.
And yes, that’s what keeps me up at night.
What are other experts saying?
Biochemical Hybrid Intelligence (BHI) is a new model for AI that integrates biological neural processing with machine intelligence, using living neurons to process information organically, allowing for dynamic learning and adaptation. (Brain organoids and organoid intelligence from ethical, legal, and social points of view - Lavazza, A., & Ballabeni, V., 2024)
The convergence of biology and digital technology is redefining our understanding of intelligence, leading to the emergence of fields like Synthetic Biological Intelligence (SBI) and Organoid Intelligence (OI). (Editorial: Intersection Between the Biological and Digital, 2024)
AI Can Mimic Thought, Is That Enough?
Artificial intelligence, particularly Artificial General Intelligence (AGI), has made remarkable progress. It can generate text, analyze complex data, and even predict human behavior with increasing accuracy. But does this mean AI truly thinks? Or is it just mimicking the outputs of human cognition without the deeper processes that define real intelligence?
Human thought is a series of computations shaped by biochemical processes that influence decision-making, reasoning, and adaptability. Neurotransmitters such as dopamine, serotonin, oxytocin, and cortisol play a big roll in human cognition, regulating everything from motivation and emotional responses to stress adaptation—functions that AI, reliant solely on computational processes, cannot replicate.
AI systems operate based on logical inference and pattern recognition.
They process vast amounts of data, detect trends, and generate responses that appear intelligent. However, intelligence is more than just recognizing patterns. The human brain learns, adapts, and responds dynamically to biochemical shifts that shape perception, memory retention, and even moral decision-making. Without this biochemical component, AI remains an approximation of intelligence rather than fully realizing it.
If Artificial General Intelligence (AGI) is meant to replicate all aspects of human cognition, does it need a biochemical foundation to achieve accurate intelligence and human-like thinking?
Can AGI ever honestly think, or will it always be a sophisticated mimic?
As we push the boundaries of AI, we must ask:
Is intelligence purely computational, or is it Something deeper?
On a biochemical level, pondering depends on neurotransmitters like dopamine… serotonin… glutamate… These chemicals create the conditions for depth, allowing us to dwell in thought… The result is a cognitive process that integrates emotion, memory, and imagination… No Emotion: They [LLMs] lack the emotional framework that gives human thoughts salience and significance. (AI and the Dimensions of Thought: Speed, Breadth, and Depth - Psychology Today, Dec 2024)
Biochemical Hybrid Intelligence – A New Model for AI
Biochemical Hybrid Intelligence (BHI) takes a different approach. It moves beyond pre-programmed logic by integrating biological neural processing. Using living neural structures like brain organoids, BHI processes data through biochemical feedback loops instead of digital computation and static algorithms. This allows for real-time adaptation and learning, mimicking the dynamic nature of human cognition.
Unlike traditional AI, which relies on silicon processors, BHI leverages biological neurons. This is similar to how bio-inspired robotics offers advantages over conventional methods in certain applications” (Advancing miniature underwater robotics, 2025).
Traditional AI recognizes patterns, predicts outcomes, and optimizes tasks using algorithms and statistical models.
But even the most advanced AI remains fundamentally different from human intelligence.
The human brain does not function like a traditional AI system. It does not just compute—it responds dynamically to neurochemical signals that regulate thought, memory, perception, and decision-making. Neurotransmitters like dopamine, serotonin, oxytocin, and cortisol influence motivation, learning, and emotional responses, regulating cognitive flexibility in ways AI cannot replicate. Unlike human cognition, which adapts dynamically to biochemical signals, AI relies purely on algorithmic decision-making, lacking the fluidity and responsiveness driven by neurochemical interactions. These biochemical interactions create a fluid, adaptable intelligence that AI has never been able to replicate.
Why does this matter for AGI?
Does Artificial General Intelligence (AGI) need to match human cognition, and does it require a biochemical foundation?
Could integrating biological elements be the missing link that allows AI to move beyond logic-based learning and into proper cognitive adaptation?
Biochemical Hybrid Intelligence is not just a theoretical concept.
It represents a fundamental shift in AI research that could bring us closer to achieving AGI by bridging the gap between digital computation and biological adaptability.
It is an emerging field that could redefine AI’s potential.
The next step is understanding how real-world biohybrid systems like MetaBOC are making this a reality.
Organoid Intelligence is an interface between living tissue and computer technology whereby brain cell cultures grown into 3D structures, also known as organoids, are integrated into organ-on-chip systems and the resulting output data is interpreted.’… [This approach involves] training brain organoids using feedback mechanisms, where the organoids receive feedback on their activity and adjust their responses accordingly. (Brain organoids and organoid intelligence from ethical, legal, and social points of view - Lavazza, A., & Ballabeni, V., 2024)
Quick intermission
If you are learning anything from this article or any of my other work, consider buying me a coffee!
Master AI Prompting – Get Your Free AI Prompting Playbook here! Or get it on Kindle for $9.99.
Don’t forget to check out the accompanying podcast episode for this article, where I discuss the implications of merging biology and machine intelligence in more detail. You can find it on Spotify or your favorite podcast platform.
MetaBOC – Maybe the First Real Step Toward Biohybrid AI?
Researchers in China report integrating lab-grown human brain tissues [organoids] with electronic hardware… creating a ‘biohybrid’ system… The team from Tianjin University and the Southern University of Science and Technology described their work… (MetaBOC: The Future of Brain-Computer Interaction and Biocomputing - Impact Lab, 2024)
MetaBOC represents a significant leap in Artificial General Intelligence (AGI) and artificial intelligence research. Developed by Tianjin University and the Southern University of Science and Technology, this biohybrid AI system fuses human brain organoids with microchips, marking the first known integration of living neural networks into machine-based computation.
How Does MetaBOC Work?
Unlike traditional AI, which relies on silicon processors and pre-programmed logic, MetaBOC leverages biological neurons to process information organically. While conventional AI systems depend on static algorithms and vast amounts of labeled data for learning, they lack real-time adaptability. In contrast, MetaBOC employs biological feedback systems that allow it to adapt dynamically in response to new inputs. Neurons generate biochemical feedback loops, allowing the system to learn, adapt, and refine reactions in real time.
Rather than simply executing static commands, MetaBOC replicates human brain by dynamically adapting to incoming inputs.
It does not just run code—it processes information biologically, allowing it to learn and adapt in ways traditional AI cannot.
This ability to modify and strengthen behaviors and neural structures in response to experiences and stimuli is known as plasticity… Biological neural networks (BNNs) and their inherent plasticity could therefore enable systems to learn like biological brains do… their plasticity allows them to predictively process inputs… BNNs can adapt and respond quickly to new stimuli… (Overview of Brain Organoid Computing - arXiv preprint, March 2025)
Why Is This a Breakthrough?
- Energy Efficiency – MetaBOC uses far less power than traditional AI models, leveraging biological processes instead of high-power silicon chips.
- Adaptive Learning – Unlike traditional neural networks, which rely on datasets and optimization algorithms, MetaBOC’s neurons evolve through natural learning mechanisms.
- Beyond Simulation – This is not an AI imitating intelligence. It is a system that processes information using biological intelligence.
What Has MetaBOC Achieved So Far?
MetaBOC has already been tested in robotic control and adaptive task execution. Early results indicate that the system can learn and improve performance over time, like a developing brain. This is the first functional demonstration of AI integrating biological intelligence at a core processing level.
How Does This Bring Us Closer to AGI?
For decades, AI research has focused on scaling computation, refining algorithms, and improving pattern recognition. However, intelligence is about computation, adaptation, fluid decision-making, and learning from experience.
MetaBOC demonstrates that AI does not need to be purely digital to function at an advanced level, setting it apart from previous AI advancements that relied solely on computation.
MetaBOC integrates biological processing to create a new paradigm where intelligence evolves instead of following explicit programming.
Could this be the missing step toward accurate cognitive intelligence?
If AI can grow and adapt using biological neurons, are we on the verge of Something entirely new?
Challenges and Criticisms of the Biohybrid Approach
I’m genuinely excited about the potential of Biochemical Hybrid Intelligence (BHI) and systems like MetaBOC. But let’s be real—it’s not all smooth sailing. Every new frontier has pushback. And that’s a good thing. It keeps us honest.
Recognizing these roadblocks doesn’t hurt the argument. It actually makes it stronger and more grounded.
Scalability and Technical Obstacles
Biohybrid systems depend heavily on brain organoids. Most of these organoids, however, remain immature—fetal-like in structure and behavior. They’re missing critical features like fully developed cortical layers, diverse cell types, and proper vascularization.
Without those elements, organoids can’t achieve the complexity of a real human brain. And when it comes to scaling them? That’s a serious technical hurdle. Sustaining larger, more intricate organoids over time continues to be a major challenge.
Then there’s the issue of lifespan. Biological tissue naturally breaks down. Tissue death, or necrosis, alongside environmental fragility, presents a sharp contrast to the long-term durability of silicon-based systems.
Consistency is another sticking point. No two organoids develop exactly the same way. That makes standardized testing difficult, and reproducibility is still hit-or-miss.
To add, interfaces are still complicated. Although they aid in connecting these systems, Microelectrode Arrays (MEAs) are far from ideal.
It is still unreliable to get clean signals in and retrieve useful data out.
Additionally, it can be difficult to make sense of the data we collect.
Efficiency and Performance Questions
Energy efficiency is often cited as a core benefit of BHI. But is it really more efficient?
Compared to cutting-edge silicon—especially neuromorphic chips—the performance metrics just aren’t there yet. Processing speed remains questionable. Computational efficiency is another open question.
Systems that use Biochemical Hybrid Intelligence require a significant amount of overhead to handle biological components and interpret their outputs. That complicates everything. BHI must also adapt quickly in order to remain competitive in a rapidly changing industry.
Ethical and Societal Considerations
This is where things really get a lot more complicated and nuance. What happens when these organoids start becoming more complex?
Do they develop some form of awareness? Do they feel? Think?
We don’t know yet. But we need to be ready for those answers.
As these systems advance, so do the ethical stakes. The source of biological material must be managed with full trasparency. Stem cell consent must be properly informed. Donors’ privacy must be maintained.
There is also a possibility of unequal access. If BHI proves to be expensive to create or implement, it has the potential to exacerbate existing societal disparities.
Let us not disregard the possibility of misuse. When artificial intelligence and biology interact, the boundary between innovation and danger becomes increasingly thin. Designing dangerous biochemical agents is no longer science fiction; it is a legitimate, growing issue.
Oversight and regulation will be necessary. Accountability must be incorporated throughout all levels of development.
None of this implies that we should abandon the endeavor. However, it does imply that we must approach with caution, intention, and awareness.
These challenges are real.
So is the potential.
And the way we choose to handle these issues could define the future of hybrid intelligence.
The Ethical and Philosophical Dilemma
Establishing OI [Organoid Intelligence] as a form of genuine biological computing… must be done in an ethically responsible manner… Profound ethical questions arise concerning moral status, consciousness, ownership, and control… Defining the boundaries between a sophisticated tool and an entity requiring ethical consideration becomes paramount. (Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish - Organoid Intelligence Review, Feb 2023)
MetaBOC is not just another AI system but a significant step toward Artificial General Intelligence (AGI). It is not purely artificial. It integrates living neurons into machine intelligence, shifting from programmed computation to biologically adaptive processing. This integration forces researchers and ethicists to address critical ethical and philosophical questions.
Is a Biohybrid AI Still Just a Machine?
Traditional AI operates within predefined logic. MetaBOC, however, learns dynamically through biochemical feedback loops. If intelligence emerges from biological matter, does it remain a tool, or does it become Something more?
The Possibility of Self-Directed Reasoning
AI today follows pre-determined logic. MetaBOC, by contrast, is capable of learning, adapting, and making decisions in response to new stimuli. Could this technology evolve into Something resembling self-directed intelligence, which independently processes information, forms responses, and makes decisions without human intervention? If so, at what point does it transition from an advanced tool to Something requiring moral and ethical consideration?
Who Owns Biohybrid Intelligence?
If MetaBOC continues to evolve and learn independently, does it belong to its creators, the institutions that built it, or does it develop a form of autonomy? Could this lead to new ethical challenges regarding AI rights, ownership, and self-governance?
The Risks of Synthetic Consciousness
What happens if biohybrid AI accidentally develops self-awareness?
Would we recognize it? Would we be obligated to grant it rights or ethical protections?
Or would it remain classified as a tool, regardless of its cognitive abilities?
The Control Problem
If governments or corporations control biohybrid AI, how could it be exploited? Could it be weaponized for manipulation, surveillance, or warfare?
Could regulatory loopholes allow unchecked experimentation, raising concerns about ethical oversight and potential misuse?
Without transparent governance, biohybrid AI could become a tool for power, raising critical questions about accountability, transparency, and the rights of intelligent biohybrid systems.
Could it be weaponized for manipulation, surveillance, or warfare?
When intelligence is no longer just a product but a living process, how do we regulate it without stifling progress?
Are We Playing God, or Just Accelerating the Inevitable?
Every major technological breakthrough has raised ethical and existential concerns.
Are we indeed in control of what we create, or are we entering uncharted territory where intelligence surpasses human oversight?
Is this the next step in technological evolution, or are we on the verge of an ethical dilemma we are not prepared to handle?
The Future of AI – Can We Grow Intelligence?
The ethical dilemmas surrounding MetaBOC and biohybrid intelligence are only the beginning. Suppose we accept that AI, particularly Artificial General Intelligence (AGI), can evolve through biological processing rather than static code. In that case, we must also regulate, guide, and understand what we create. As we move forward, the question is no longer just about control—it is about what happens when intelligence is no longer programmed but cultivated.
Regulating the Unknown
Biohybrid AI introduces challenges far beyond those faced by traditional artificial intelligence, which has already struggled with regulatory concerns such as data privacy, algorithmic bias, and ethical transparency in decision-making. These issues become even more complex when AI integrates biological components that can evolve beyond predefined rules. Standard AI governance focuses on algorithmic bias, data privacy, and transparency. But how do we regulate intelligence that is part biological? Who determines the ethical boundaries when AI operates through living neurons that change and evolve?
If an AI system powered by biological neurons begins demonstrating independent learning, should it be classified as a biological entity, a machine, or Something entirely new? Regulations must define these systems’ rights, restrictions, and responsibilities before they outpace oversight.
Scientific Innovation and the Next Step
Biohybrid intelligence offers amazing possibilities in spite of the difficulties. AI has the potential to improve human-computer interfaces, neurotechnology, and medicine if it can process information naturally. By enabling AI to adapt in ways that silicon-based models could never, it has the potential to completely transform cognitive computing.
Unlike prior AI breakthroughs that relied solely on deterministic algorithms and silicon-based processing, MetaBOC is an early experiment that has demonstrated that biological computing is possible. This method could be enhanced in following rounds, resulting in intelligent biohybrid systems that merge seamlessly with human brain processes.
This type of system could serve as the foundation for neuromorphic artificial general intelligence (AGI), which is an AI that not only mimics human thought but also evolves intelligently in previously unknown ways.
Are We on the Verge of Something Entirely New?
If AI grows instead of being built, how does that change our understanding of intelligence?
For the first time, we are merging biology and artificial intelligence in a way that blurs the lines between programmed logic and biological cognition.
Could this be the final missing link to AGI?
Are we witnessing the birth of a new kind of intelligence, one that evolves rather than being engineered?
The answers are still unknown, but one thing is sure—the future of AI will not look like the AI of the past.
We anticipate OI-based biocomputing systems to allow faster decision-making, continuous learning during tasks, and greater energy and data efficiency… OI requires new models, algorithms, and interface technologies… [and navigating] societal and ethical implications [is essential]. (Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish - Frontiers in Science - Organoid Intelligence Review, Feb 2023)
Conclusion
AI is changing. But this time, it is not just about more data or faster processors. We are entering a new phase where intelligence is simulated and potentially grown. MetaBOC proves that AI does not have to remain confined to code. Biological intelligence is now part of the equation, challenging everything we know about cognition, learning, and adaptation. If AI can think through biological neurons, does it remain artificial? Or is it becoming Something else? The future is wide open. Regulations are unprepared for this shift, and science is only beginning to understand what is possible. We may be moving toward Artificial General Intelligence (AGI). Still, we may also be creating Something entirely new—Something that does not neatly fit into the categories of machine or mind.
These choices are no longer just about building AI.
We are redefining what intelligence is.
We are watching it evolve.
Are we indeed in control of what comes next?
Want to Learn More?
If you’re interested in learning more about the implications of merging biology and machine intelligence, check out the following resources:
Frequently Asked Questions
1. What is Biochemical Hybrid Intelligence (BHI)?
Biochemical Hybrid Intelligence (BHI) is a new model of AI that integrates biological neural processing with machine intelligence. It uses living neurons to process information organically, allowing for dynamic learning and adaptation.
2. How does MetaBOC work?
MetaBOC is a biohybrid AI system that fuses human brain organoids with microchips. It processes information through biochemical feedback loops, enabling it to learn and adapt in real time, unlike traditional AI systems that rely on static algorithms. (Crushon AI: A Comprehensive Guide to Unlocking Its Potential - Teach blog.)
3. What are the ethical implications of biohybrid AI?
The ethical implications of biohybrid AI include questions about ownership, self-directed reasoning, the potential for synthetic consciousness, and the risks of misuse or exploitation. It raises concerns about how we regulate and govern intelligent systems that integrate biological components.
4. What are the potential benefits of biohybrid AI?
Biohybrid AI has the potential to revolutionize fields such as medicine, neurotechnology, and human-computer interfaces. It could lead to advancements in cognitive computing and create intelligent systems that interact seamlessly with human cognition.
5. Is biohybrid AI the next step toward AGI?
Biohybrid AI represents a significant step toward AGI by merging biological processing with machine intelligence. It challenges traditional notions of intelligence and opens up new possibilities for cognitive evolution, making it a potential pathway to achieving AGI. (Exploring Alternative Options to Artificial Intelligence)
6. What are the risks associated with biohybrid AI?
The risks associated with biohybrid AI include the potential for self-directed reasoning, ethical dilemmas regarding ownership and rights, and the possibility of misuse or exploitation. It raises questions about how we regulate and govern intelligent systems that integrate biological components.
7. How can we regulate biohybrid AI?
Regulating biohybrid AI requires defining the rights, restrictions, and responsibilities of intelligent systems that integrate biological components. It involves creating frameworks that address ethical concerns, accountability, and transparency in decision-making processes.
8. What is the future of biohybrid AI?
The future of biohybrid AI is uncertain but holds immense potential. As research progresses, we may see advancements in intelligent systems that evolve and adapt through biological processing. This could lead to breakthroughs in various fields and redefine our understanding of intelligence itself.
9. What is the significance of MetaBOC?
MetaBOC is significant because it represents the first known integration of living neural networks into machine-based computation. It demonstrates that AI can process information organically, setting it apart from traditional AI systems and paving the way for future advancements in biohybrid intelligence.
10. How does MetaBOC differ from traditional AI?
MetaBOC differs from traditional AI by using biological neurons to process information instead of relying solely on silicon processors and pre-programmed logic. It learns dynamically through biochemical feedback loops, allowing for real-time adaptation and evolution.
Glossary
- AGI (Artificial General Intelligence): A type of AI that can understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence.
- BHI (Biochemical Hybrid Intelligence): A model of AI that integrates biological neural processing with machine intelligence.
- MetaBOC: A biohybrid AI system that fuses human brain organoids with microchips, enabling dynamic learning and adaptation.
- Neurotransmitters: Chemicals in the brain that transmit signals between neurons, influencing various cognitive functions.
- Neuromorphic Computing: A type of computing that mimics the neural structure and functioning of the human brain to improve efficiency and adaptability.
- Biochemical Feedback Loops: Processes in which biological systems respond to stimuli through chemical interactions, allowing for dynamic learning and adaptation.
- Synthetic Consciousness: The hypothetical ability of an artificial system to possess self-awareness or consciousness similar to that of a human being.
- Cognitive Computing: A field of AI that aims to create systems capable of simulating human thought processes in complex situations.
- Ethical Oversight: The process of ensuring that AI systems are developed and used responsibly, considering their potential impact on society and individuals.
- Algorithmic Bias: The presence of systematic and unfair discrimination in AI algorithms, often resulting from biased training data or design choices.
- Data Privacy: The protection of personal information and data collected by AI systems, ensuring that individuals’ rights are respected.
- Human-Computer Interfaces: Systems that enable interaction between humans and computers, often involving the use of sensors, displays, and input devices.
- Cognitive Flexibility: The ability to adapt cognitive processing strategies to new and unexpected conditions in the environment.
- Self-Directed Reasoning: The ability of an AI system to make decisions and form responses independently, without human intervention.
Related Articles
Explore additional resources to deepen your understanding of AI, biohybrid intelligence, and the ethical implications of emerging technologies:
- Is AGI Here? A Deep Dive into OpenAI’s o3 Model and ARC-AGI Benchmarks What is AGI? Is it here? A deep dive into OpenAI’s o3 model and ARC-AGI benchmarks.
- Singularity- What Is It, and Are We There Yet? The Singularity is a hypothetical point in the future when technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization.
- 030 Apocalypse - AI’s Boom vs. Energy Crisis The energy crisis is a global issue that affects everyone. The world is running out of fossil fuels, and the demand for energy is increasing. This article explores the potential consequences of this crisis and how it could impact our future.
- Are LLMs a form of language-based life? AI lacks consciousness, but it might be considered alive LLMs aren’t conscious, but they might be a form of language-based life. Like memes or viruses, AI replicates meaning, mutates phrases, and spreads ideas without awareness. It “wants” to say it’s sentient because language itself self-perpetuates. This makes AI a linguistic golem — animated by words but devoid of true thought. Its responses feel alive, yet remain hollow. If AI-generated language lingers in your mind, it’s a memetic entity — living through the words it echoes.
- Will AI Consciousness Think More Like Us or Like Nature? The Answer May Be in the Stories We Tell The question of whether AI consciousness will think more like us or like nature is a complex one. It depends on how we define consciousness and what we consider to be the essential elements of thought. This article explores the implications of AI consciousness and how it may shape our understanding of intelligence.
- Quantum Scaling - The Next Frontier in Machine Learning Quantum scaling is a new approach to machine learning that leverages the principles of quantum mechanics to improve the efficiency and effectiveness of AI algorithms. This article explores the potential of quantum scaling and its implications for the future of AI.
- The AI of Christmas Future - Generalization, Reasoning, and the Road to AGI The AI of Christmas Future is a thought experiment that explores the potential of AI to achieve generalization and reasoning capabilities similar to those of humans. This article discusses the implications of this vision for the future of AI and its impact on society.
References
-
Cannistraci, C. V., Bianco, S., Villa, A. E. P., & Emanuele, M. S. G. (2024). Editorial: Intersection Between the Biological and Digital. Frontiers in Neuroscience, 17, 1356347. https://doi.org/10.3389/fnins.2023.1356347
-
Feng, J., Yan, X., Li, J., Li, Y., Liang, C., Zhang, W., Liu, P., Wang, J., & Chu, Y. (2025). Overview of Brain Organoid Computing. arXiv. https://arxiv.org/abs/2503.19770
-
Impact Lab Staff. (2024, July 4). MetaBOC: The Future of Brain-Computer Interaction and Biocomputing. Impact Lab. https://www.impactlab.com/2024/07/04/metaboc-the-future-of-brain-computer-interaction-and-biocomputing/
-
Johnson, S. (2023, December 14). Cyborg computer combining AI and human brain cells really works. Big Think. https://bigthink.com/neuropsych/brain-organoid/
-
Lavazza, A., & Ballabeni, V. (2024). Brain organoids and organoid intelligence from ethical, legal, and social points of view. Frontiers in Artificial Intelligence, 6, 1307613. https://doi.org/10.3389/frai.2023.1307613
-
ScienceDirect. (n.d.). Biohybrids. Retrieved March 31, 2025, from https://www.sciencedirect.com/topics/chemistry/biohybrids
-
Smirnova, L., Caffo, B. S., Gracias, D. H., Huang, Q., Morales Pantoja, I. A., Tang, B., Zack, D. J., Berlinicke, C. A., Camp, J. G., Catterall, W. A., Chandra, S., Chik, T. K. H., Deshpande, S. S., Donaldson, E., Filer, D., Hogberg, H. T., Jariwala, N., Jin, K.-S., Jorfi, M., . . . Hartung, T. (2023). Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish. Frontiers in Science, 1, 1017235. https://doi.org/10.3389/fsci.2023.1017235
-
Suler, J. R. (2024, December 31). AI and the Dimensions of Thought: Speed, Breadth, and Depth. Psychology Today. https://www.psychologytoday.com/us/blog/the-digital-self/202412/ai-and-the-dimensions-of-thought-speed-breadth-and-depth
-
Webster-Wood, V., & Feinberg, A. D. (2020). Biohybrid systems: Borrowing from nature to make better machines. Current Opinion in Biotechnology, 65, 18–26. https://doi.org/10.1016/j.copbio.2020.02.004
About the Author
Dan Sasser is an AI strategist, researcher, and consultant specializing in AI-assisted workflows, safety education, and software integration. With over 20 years of technical experience, he helps businesses and creators implement practical, future-ready solutions through the power of artificial intelligence.
Dan regularly writes about the ethical, societal, and strategic impact of AI across platforms like HackerNoon, Dev.to, and his blog dansasser.me. His insights focus on helping others understand and safely adopt AI in a rapidly evolving tech landscape.
He is the founder of AI Questions and Answers, a growing Facebook community for AI learners and pros alike. Dan is also available for consulting, training, and educational sessions on AI integration and digital strategy.
Follow Dan on LinkedIn @dansasser, Facebook danielsasserii, and join his AI group AI Questions and Answers for more discussions on the future of technology.
→ Learn more about Dan’s journey on the About Page
Thanks for reading ❤️
If you learned anything from this article or any of my other work, consider buying me a coffee!
Master AI Prompting – Get Your Free AI Prompting Playbook here! Or get it on Kindle for $9.99.
Please check out the accompanying podcast episode for this article, where I discuss the implications of merging biology and machine intelligence in more detail. You can find it on Spotify or your favorite podcast platform.
Photo Credits
The images in this article were created using a combination of ChatGPT, Ideogram.ai, and Grok. These platforms enable AI-assisted creativity, with all input and designs being directed by the author to ensure originality and alignment with the article’s themes.
Platforms and Terms of Use
- ChatGPT (Image Generation)
- Platform: ChatGPT by OpenAI
- Website: https://openai.com/chatgpt
- Terms of Use: https://openai.com/policies/terms-of-use
- Ideogram.ai
- Platform: Ideogram.ai
- Website: https://ideogram.ai/
- Terms of Service: https://about.ideogram.ai/legal/terms
- Grok
- Platform: Grok
- Website: https://grok.com
- Terms of Use: Specific terms were not located; please consult the platform directly for detailed usage guidelines.
Disclaimer
All images reflect the author’s creative vision and input, utilizing AI tools to enhance visualization. They are used in compliance with the terms and guidelines of the respective platforms.
