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Biocomputers: The Future of Intelligence Beyond Silicon
634 pages, Paperback
Published September 13, 2026
About the author
Maria Johnsen
198 books49 followersMaria Johnsen, originating from Trondheim, Norway, is a multifaceted expert with a wide range of skills that span AI computer engineering, film and TV investment, writing, directing, producing, poetry, and digital marketing. Her multilingual upbringing significantly contributed to her linguistic expertise. Her academic background is equally diverse, encompassing Information Technology, Informatics, Beauty Arts and Culture, Computer Engineering, and Film Production.
Maria’s remarkable work has earned her numerous accolades, including the International Star Award for quality leadership, innovation, and excellence in 2019. Her films, available on platforms like Amazon Prime, have reached audiences in over 200 countries. In addition to her success in filmmaking, Maria has authored 85 books across multiple languages, including English, German, French, Spanish, Japanese, and Dutch, many of which are used as educational materials in postgraduate studies. Her influence as an author spans the globe, with her works being utilized in universities across North America and Europe.
Some of her most notable titles include Search Engine Revolution, The Business of Filmmaking: Building a Business and Networking Strategies in the Movie Industry, AI in Digital Marketing, The Future of Artificial Intelligence in Digital Marketing: The Next Big Technological Break, Blockchain in Digital Marketing: A New Paradigm of Trust, Multilingual Digital Marketing: Managing for Excellence in Online Marketing, AI and Moral Dilemmas, Large Language Models (LLMs), Self-Aware AI Robot, Computer Engineering, Neural Networks, and Sales in The Age of the Intelligent Web. These works highlight her deep knowledge of digital marketing and emerging technologies.
Maria’s educational experiences have taken her around the world, with studies at Sorbonne University in Paris, Kharkov University in Ukraine, and other institutions in Changchun, China, where she became proficient in Korean and Chinese. She has taught English, French, and Russian to undergraduate and postgraduate students, achieving a notable 70% success rate in helping her students secure scholarships for further studies in the US and Canada.
As an entrepreneur, Maria founded Golden Way Media, a global business promotion company. Based in London, UK, she leads both Golden Way Media Films and Golden Way Media, a digital marketing firm in Norway. Recognized as one of the top digital marketing influencers worldwide and among the top 100 global influencers in artificial intelligence (AI) and Fintech by Onalytica in 2016, Maria continues to thrive in multiple fields. She also writes commercially viable screenplays, making a significant impact in the film industry.
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Displaying 1 - 1 of 1 review
September 29, 2026
This is one of those books that makes you stop and think about what we actually mean when we talk about “computing.” We’re so used to thinking about computers as silicon chips, GPUs, servers and massive data centres that it’s easy to forget that biology itself is basically running incredibly complex information-processing systems all the time.
What I found interesting about this book is the shift in perspective. Instead of treating biology as something computers are simply used to study, it explores the idea that biological systems could themselves become part of the computing architecture. That sounds pretty futuristic, but the basic concept is surprisingly logical once you start thinking about DNA, proteins, cells and neural systems as ways of storing, processing and communicating information.
The subject can get technical, but the bigger ideas are what kept me interested. Silicon computing has become unbelievably powerful, yet it also has some obvious limitations. We keep demanding more processing power for AI, simulations and increasingly complicated models, while the hardware requires huge amounts of electricity, cooling and physical infrastructure. Biology operates under completely different constraints and, in some situations, does things that conventional machines struggle to reproduce efficiently.
I especially liked the questions the book raises around where computing could go next. Could future computers combine conventional electronics with biological components? Could DNA become a form of data storage? Could engineered cells perform specialised calculations? And could biological systems eventually help us build forms of computing that work more like living networks than traditional machines?
At the same time, I didn't come away thinking that biological computers are about to replace our laptops or GPUs anytime soon. That's probably one of the more important things to keep in mind while reading this kind of material. There is a huge difference between demonstrating that something is possible in a laboratory and turning it into reliable, scalable technology that can actually be manufactured and used.
There are also some pretty obvious challenges. Biological systems are messy compared with digital electronics. Computers are designed around predictable operations, precise control and repeatability. Cells don't always behave like neat little logic gates. Then there are questions around speed, reliability, contamination, maintenance and how you actually interface biological processes with conventional hardware.
For me, that tension is what makes the subject interesting. The future probably isn't going to be as simple as “silicon is finished and biology takes over.” It feels more likely that we'll see different types of computing working alongside each other, with conventional chips handling some tasks and biological or bio-inspired systems handling others.
The book also made me think differently about AI. Everyone talks about making AI models bigger and throwing more computing power at them, but perhaps the longer-term question is whether intelligence can be approached through completely different physical systems. Maybe the next interesting breakthrough isn't just a faster processor, but a different way of processing information altogether.
Overall, this is a fascinating read if you're interested in AI, emerging technology, neuroscience or just the weird possibilities of future computing. Some ideas are definitely easier to digest than others, and I think the subject benefits from taking your time rather than trying to absorb everything in one sitting.
What stayed with me most wasn't the idea that biological computers will definitely become the next big thing. It was the broader idea that we may have been thinking about computers too narrowly. We built machines in the image of our current technology. Biology gives us another possibility: perhaps intelligence doesn't have to be processed the way we've been doing it for the last few decades.
What I found interesting about this book is the shift in perspective. Instead of treating biology as something computers are simply used to study, it explores the idea that biological systems could themselves become part of the computing architecture. That sounds pretty futuristic, but the basic concept is surprisingly logical once you start thinking about DNA, proteins, cells and neural systems as ways of storing, processing and communicating information.
The subject can get technical, but the bigger ideas are what kept me interested. Silicon computing has become unbelievably powerful, yet it also has some obvious limitations. We keep demanding more processing power for AI, simulations and increasingly complicated models, while the hardware requires huge amounts of electricity, cooling and physical infrastructure. Biology operates under completely different constraints and, in some situations, does things that conventional machines struggle to reproduce efficiently.
I especially liked the questions the book raises around where computing could go next. Could future computers combine conventional electronics with biological components? Could DNA become a form of data storage? Could engineered cells perform specialised calculations? And could biological systems eventually help us build forms of computing that work more like living networks than traditional machines?
At the same time, I didn't come away thinking that biological computers are about to replace our laptops or GPUs anytime soon. That's probably one of the more important things to keep in mind while reading this kind of material. There is a huge difference between demonstrating that something is possible in a laboratory and turning it into reliable, scalable technology that can actually be manufactured and used.
There are also some pretty obvious challenges. Biological systems are messy compared with digital electronics. Computers are designed around predictable operations, precise control and repeatability. Cells don't always behave like neat little logic gates. Then there are questions around speed, reliability, contamination, maintenance and how you actually interface biological processes with conventional hardware.
For me, that tension is what makes the subject interesting. The future probably isn't going to be as simple as “silicon is finished and biology takes over.” It feels more likely that we'll see different types of computing working alongside each other, with conventional chips handling some tasks and biological or bio-inspired systems handling others.
The book also made me think differently about AI. Everyone talks about making AI models bigger and throwing more computing power at them, but perhaps the longer-term question is whether intelligence can be approached through completely different physical systems. Maybe the next interesting breakthrough isn't just a faster processor, but a different way of processing information altogether.
Overall, this is a fascinating read if you're interested in AI, emerging technology, neuroscience or just the weird possibilities of future computing. Some ideas are definitely easier to digest than others, and I think the subject benefits from taking your time rather than trying to absorb everything in one sitting.
What stayed with me most wasn't the idea that biological computers will definitely become the next big thing. It was the broader idea that we may have been thinking about computers too narrowly. We built machines in the image of our current technology. Biology gives us another possibility: perhaps intelligence doesn't have to be processed the way we've been doing it for the last few decades.
Displaying 1 - 1 of 1 review