AI

Yin-Yang

Technologies constantly intersect. This impacts a technology’s life cycle (TLC) that already has its own phases of development and market adoption.

Technology life cycles follow an S-Curve. The shape of each technology’s S-Curve is unique, but generally, research and development requires an investment. With traction, the technology can ascend and become profitable. Over time, this ascension slows as a new normal is established. Market relevancy can be lasting, but eventually, new technologies push existing technology toward a final decline phase. The further a new technology’s life cycle is from the current status quo, the faster one can push the other into history books. For example, when cell phones went mainstream, it did not take long for pagers to fall out of our pockets.

When different technologies ascend around the same time, they each have their own TLC, but the shared timeline only has room for what the market and adoption curves allow. This initiates a game of scarcity versus abundance. If two technologies remain isolated, scarcity wins and causes the ascension of one technology to force the other technology toward a decline. With a sense of abundance, different technologies find reasons to interact, interconnect, support, and perpetuate each other. Even if the relationship is not obvious at first, layered value broadens the impact and extends each technology’s life cycle.

This can be seen in the rise of web3 and AI technologies. 

At first, hype was focused on blockchains and everything under the web3 umbrella. As blockchain networks were ascending past early adoption, Narrow AI learned to speak our language and quickly stole the spotlight. This lowered the volume around web3 as everything became about AI. With volume lowered, it was easy to think web3 concepts were no longer relevant, but the interoperability of web3 technologies can support, guide, and tame AI.

When AI makes everything fake, blockchains make it real again. This symbiosis aligns two different technology life cycles. When balanced, AI is yin and web3 is yang. The yin of AI is exemplified by unpredictability and independence. The yang of web3 draws from decentralized dependancy.

AI is strongest when rooted in web3 concepts, which are strengthened by the capabilities and functionalities of AI. Together, these two seemingly unrelated technologies benefit in various ways. For example, ChatUX gave AI a voice, which can now imitate anyone, but blockchains and zero-knowledge proofs can confirm identity. The chaos continues as compute speeds of machine learning makes state management hard to uphold, but blockchains, smart contracts, and digital assets can track provenance and the present state of a digital system. If AI projects need financial capital, digital currencies and tokenomics can provide economies to scale. As the capabilities of Narrow AI move toward General AI and Super AI, generative data may struggle to preserve the source of truth, but web3 technologies add transparency to improve inputs and helps avoid unsteadiness. Even at the infrastructure level, AI can benefit from decentralized training algorithms and a growing thirst for electricity can be quenched by HPC data centers originally built for mining cryptocurrencies.

Extra Shot

Darkness will always lurks in the shadows, but the virtuous light of abundance bets on a shared, atomically exquisite existence.

This example highlight how mixing different concepts can greatly extend technology life cycles. Such abundance also paves the way to more exotic technologies.

By Ben McDougal, ago

Septenary

AI is often thought of as a singular technology. This blanket assumption makes it impossible to compare the capabilities and functionalities that differ between the seven different types of artificial intelligence.

The seven types of AI are organized into two groups. The first group is based on capability and includes Narrow AI, General AI, and Super AI. The second group is based on functionality and includes Reactive Machine AI, Limited Memory AI, Theory of Mind AI, and Self-Aware AI.

Capabilities are determined by what different types of AI are able to accomplish. The three types of AI categorized by capabilities are Narrow AI, General AI, and Super AI. Here’s a rundown to leverage what’s possible today, while staying mindful of what may be possible tomorrow.

Narrow AI <realized>

AI trained from existing data, with compute geared to do one thing really well. While data sets are vast, this type of AI can not go beyond the data sets it is tied to. As of 2026, Narrow AI is the only type of AI that is fully realized within the three types of AI categorized by capabilities. Examples of Narrow AI include language translators, game-playing programs, spam filters, recommendation engines, voice assistants, facial recognition, self-driving vehicles, and robots with specific tasks. Generative AI also falls within this type of AI, which seems odd. The mashups of generated text, images, video, and sound seem to be random, but are still constraints by existing data sets. Neural networks, optimized data sets, computer vision, and machine learning make the capabilities of Narrow AI remarkable, but limited by the data it has to munch.

General AI <theorized>

This advancing realm of AI leverage existing data like Narrow AI, but can reason beyond those constraints. The transfer of intelligence, with no human intervention, makes the race to General AI (also called AGI) intense. When AI can make decisions based on an advancing state of its own understanding, the limit of this AI’s capabilities become unknown. Theories set the potential limit around that of a human. Agentic AI hints at AGI with decision making and interdisciplinary task management, but lacks the emotional traits, planning, and other methods of generalization that will define the capabilities of General AI. Other potential examples include all-new content creation, robots that can learn new tricks. As the theories of AGI become realized, ethics, regulation, and a determination of consciousness will be moving targets to navigate. The breeding speed of AGI may release points of no return, which makes it critical to understand and debate opening to ensure a new species is copacetic.

Super AI <theorized>

Welcome to when AI becomes it’s own species. Concern triggers due to the finite resources of Earth, but when AI surpasses the capabilities of humankind, dust from the deep future will already be everywhere. Multi-planetary travel will be underway, climate problems will be solved, and life extension will be supported by Super AI. Singularity is theorized to push the functionalities of Self-Aware AI and the capabilities of Super AI beyond humanity’s control. This means the time to plan ahead is now. As the minds of machines are wired, it’s crucial to collectively consider the heart and soul we build into technology. This will guide discovery with an excellence that came before it.

AI based on functionality is less about power and more about how things works. Reactive Machine AI, Limited Memory AI, Theory of Mind AI, and Self-Aware AI are the types of AI classified by their functionalities.

Reactive Machine AI <realized>

This is old school AI. Rooted in statistical math, Reactive Machine AI arrived soon after the programmable computer was developed. There is no adaptive learning here, but the speed at which it can calculate a vast amount of data makes the performance seem intelligent. The functionalities of Reactive Machine AI drives more primitive examples of Narrow AI, such as recommendation engines and game-playing programs. This type of AI provides a static base, but with no memory and only a focus on specific tasks, attention quickly shifts toward functionalities that have more adaptive characteristics.

Limited Memory AI <realized>

This type of AI learns and evolves. Functionalities of Limited Memory AI are still constrained by the existing data it was trained with, but world-changing advancements have been seen in machine learning, large language models (LLMs), generative AI tools, multimodality, computer vision, and self-driving vehicles. The realization of Limited Memory AI has Narrow AI pushing it’s full potential.

Theory of Mind AI <theorized>

Here we combine existing data, an adaptive ability to learn, and an emotional willingness to think. Enhanced reasoning, multimodality, customizability, adaptive computing, and user-driven functionalities bring General AI (AGI) to life. Theory of Mind AI embraces emotions and understands how we think. This type of AI will add personality to humanoid robots and support reliable relationships by combining digital depth to the realities of our world. As lines blur between humans and machines, compute will remain a currency, efficiency will skyrocket, and a new era of life on earth will begin.

Self-Aware AI <theorized>

It’s hard to define consciousness, but true self-awareness exemplifies this type of AI. Along with understanding how we think, Super AI will own emotions, hold beliefs, and is theorized to support the functionalities of Super AI.

Understanding the seven types of AI helps leaders leverage the perks of technology now and later. Our willingness to lift the fog helps avoid a fear in the unknown and while resisting technology is a choice, it’s one that may put you behind innovation curves. Hybrids add artificial co-pilots, but remain assertive and budget resources knowing we are the pulchritudinous architects of our own neon future.

By Ben McDougal, ago

Cyberspace

The Internet linked our planet. It ignited an information age and now supports our connected era where humanity’s collective intelligence dials into boundless potential.

The vast, yet connected nature of cyberspace is what makes knowledge accessible to anyone. It makes communication instant, gives founders an ability to scale, and helps anyone find their tribe. As artificial intelligence (“AI”) taps into the treasure trove we’ve been building since the 1960s, we’ve seen how quickly technology can advance thanks to the endless contributions of generational leaders worldwide.

Heightened connectivity will take us into the deep future, but be wary of excessive consumption. With everything at our finger tips, it’s easy to get lazy. This can degrade our mental fitness with isolated ideology confidently misguided by algorithms. We may be the last generation to know what life was like offline. This enshrines a calling to embrace emerging technologies, but also to preserve the communal code that nature, experiential wisdom, relationships, and eccentricity uploads into the human experience.

By Ben McDougal, ago

Digitized Consciousness

Would you choose to live forever? Most people say no. There’s something precious about the finite nature of life.

That said, making an impact, life extension, endowing loved ones, and leaving a legacy are common ambitions.

As humans merge with machines, digitizing our life’s body of work can technically already be done. To illustrate this, the average human generates around two gigabytes per day. This data culminates from the text, audio, photos, video, and other creative expressions we create. Nanotechnologies may reduce the storage space needed, but even without that multiplex, if we generate 730 gigabytes of data each year and live 75 years, that equates to only 54,750 gigabytes, which is less than 55 terabytes. Everyone generates different types and levels of information, but storage is not the issue.

With storage negligible, creation of content, organization, and privacy concerns will always present barriers. Barriers are built to be broken, yet information alone is unlikely to represent the enigma of one’s consciousness. The totality of one life’s output will present signals, but if those who follow are to interact with a digitized consciousness, how might the experience need to be supplemented to feel organic?

Interfacing with the brain will unveil depth in the human mind, but will that be enough to paint how consciousness is felt though the heart and soul of our existence?

Replicants with your digitized consciousness may never fully represent the original, but this won’t stop such a resource from being appreciated. Whether it’s stories told, wisdom shared, or just a voice to comfort our descendants, humans thrive thanks to a historic desire to pass our experience to future generations. As we continue to digitize the world, the option for mind uploading seems inevitable and organized content creators may have a head start.

Extra Shot

Consumption expires.

Create to live beyond time.

Let’s assume advancements in brain-computer interfaces, neural networks, and quantum computing unlocks the ability to effectively digitize consciousness. Who might activate it and how would such an asset be portrayed, owned, and managed after the human passes away?

Navigating this reality will require a combination of legal, ethical, and philosophical frameworks, but eventually, the digital interaction becomes easy. It gets weird as this digital asset becomes a part of the physical world. What might a digitized consciousness paint on a canvas? Why not mix sounds into music? Could it run a humanoid robot?

Further down the road, what are potential risks and benefits of creating a digitized consciousness that is capable of self-improvement and adaptation? Final alignment may be needed before the human dies so tiny details could be refined. Even with final tweaks to support transcendence, software gets stale, but updates could alter the asset.

It feels important to evolve elements that keep such an asset functioning, but the ideas, insight, perspectives, and overall interaction with such a digitized consciousness would need to be unscathed to remain true to the original source. If an uploaded mind was altered, a kind of digital entropy would fragment the asset away from its original purpose.

A transforming heart may keep this asset in vogue, but the identity of the human it represented would be lost. Any digitized consciousness will become outdated over time, but perhaps that will be part of the charm.

By Ben McDougal, ago

Student Founders

Ben was back at UW Stevens Point, speaking and collaborating inside the UW Stevens Point Center for Entrepreneurship. During his time on campus, Alex Suscha and Haley Densow joined him for another “In The Wild” jam session.

Hear how these two college student founders are building beyond the classroom by embracing the tension of going beyond the status quo. We speak about building a company while balancing school work, how to win pitch contests, and growing up in the connected era. For leaders who build on the timeline of now, this timeless episode is brewed just for you. Enjoy!

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BONUS MATERIALS

https://sourcerai.co

https://uwsp.edu/news/haley-densow-dormdash

https://x.com/SentrySchool/status/1896335458461778294

https://x.com/BENovator/status/1894227286326464568

http://Student-Founders.YouDontNeedThisPodcast.com

http://YouDontNeedThisKeynote.com

http://PlayforcePrinciples.com

EP17 – Schoolhouse Rock 🎙️ Anika Yadav

EP31 – What is School For? 🎙️ Russ Goerend

EP53 – Now & Later 🎙️ Evan Stanislawski + Matt Vollmer

EP54 –  Blurring Lines 🎙️ Kevin Neuman + Chris Klesmith

EP69 –  Generative Humans 🎙️ Chris Snider

EP81 – Technology Soup 🎙️ Carl Lippert

https://BenMcDougal.com/slide-deck-design

http://YouDontNeedThisPodcast.com

By Ben McDougal, ago