Australia is a world leader in the space, with Australian born or based leaders in neurotechnology including Synchron, Emotiv, Ultra Bionics, Omniscient Technologies, Cortical Labs, and Resonait, and the leaders of most of them speaking at the event.

My LinkedIn post provides a very high level view of the discussion, and is complementary to this piece in linking to the speakers and pointing to some of their specific contributions. Below is a more detailed description of the key insights from each session. 

Nita Farahany – Duke University – The Battle for Your Brain

Non-invasive neurotech plus AI is rapidly moving toward real-time decoding and shaping of thought. fMRI with transformer models (GPT-1) has already reconstructed continuous language and imagined stories with striking accuracy, with similar techniques now tested on portable systems like fNIRS and EEG. Commercially, Meta’s neural interfaces and “brain foundation models” such as BrainLM/BrainWave and Synchron’s Chiral suggest BCIs that not only decode explicit intentions but also pre-conscious motor signals, enabling closed-loop neuromodulation where brains and models co-adapt—transformative for people with paralysis, but also a world where systems can act on subconscious activity before we’re even aware of intending to act.

The central risk shifts from data privacy to agency: when AI acts on subconscious neural signals and we naturally rationalise our behaviour afterward, we may not know whether our actions were truly ours. The proposed response is to embed fiduciary duties directly into the models handling brain data—a base brain-foundation layer that decodes signals, topped by a guardian layer whose reward functions are tied to loyalty, care, and confidentiality, continuously checking and sometimes blocking actions. Around that, a multi-layer governance stack—technical, institutional, legal, corporate, and international (e.g., UNESCO neurotech standards)—aims to protect cognitive liberty and self-determination rather than optimise primarily for engagement or profit.

Panel: Funding for Neurotech

Nicholas Opie, Cameron Higgins, Harikesh Pushpapathan, Brett Kagan, Michael Witbrock

Neurotech founders are shifting to a “patient-first” frame: deep brain stimulation and BCIs must move slowly, with long safety trials, consent, and continual clinical check-ins, not “move fast and break things.” Australia and NZ now have world-class science, engineers, hospitals, ethics committees, and early flywheels (Synchron → spin-outs), but capital is still misaligned—too concentrated in a few “safe hands,” applying software-style pattern matching to a domain with very different risks and timelines. The push is for more diverse, patient-aligned capital (government, family offices, smarter university pathways), more small early bets, and tighter vertical integration between labs, hospitals, regulators, and investors so that genuinely hard neurotech can actually get to patients.

At the same time, AI systems are cast as “inspectable brains”: mind-like models whose internals we can probe neuron-by-neuron, giving powerful tools for neuroscience but raising deep questions about copyright, freedom of thought, and who benefits from brain and behavioural data. Safety is seen as a long-term relationship: humans and conditions change, algorithms drift, and BCIs must be designed for continuous monitoring, adjustability, and the option to switch them off. Concerns about data colonialism and a handful of firms capturing the value of public and patient data fuel calls for public–private models, fairer sharing of IP from publicly funded research, and a more honest balance between futuristic implants and simple, underfunded fixes like basic accessibility.

Panel: Freedom of Thought

Allan McCay, Christina Maher, Lorraine Finlay, Kiley Seymour

Freedom of thought is defined as: not having to reveal your thoughts, not being punished for thoughts alone, not having your thoughts impermissibly modified, and a state duty to foster conditions for independent thinking. It sat dormant in human rights law for decades; AI and neurotechnology make it urgent, because brain data can now be measured, inferred, and influenced. Neurotech can expand freedom of thought (e.g. deep brain stimulation for OCD or depression, speech BCIs restoring communication) but also threaten it by exposing inner mental states that were never meant to be observable or punishable.

Consumer neurotech and gaming already collect rich EEG and behavioural data—often from children—mapping stimuli to brain responses under vague consent, drifting from “nudge” toward manipulation. Experiments show that merely knowing you’re surveilled changes how you process information, even if you feel unaffected, hinting at how neural surveillance could quietly reshape identity and dissent. Law is too slow and politically constrained to handle this alone, so the emphasis shifts to proactive safeguards: strict data minimisation (“collect less, transform more”), on-device processing and revocable biometric tokens, safety- and rights-by-design in products, strong cybersecurity, and serious public education for parents, teachers, and young people.

Michael Ivan – University of Miami – Applying Connectonimics

AI-based connectome mapping is transforming both brain tumour surgery and brain–computer interface (BCI) implantation. Using Omniscient’s software, his team fuses structural connectivity (tractography) with functional MRI to generate a patient-specific map of brain networks, including language, vision, emotion, default mode and executive function, even when anatomy is distorted by tumours or stroke. In tumour surgery they use this to identify “silent corridors” and navigate through augmented reality overlays in the OR, maximising resection while preserving cognition rather than just avoiding paralysis or loss of speech.

For BCIs, the same maps are used in reverse: instead of avoiding function, they target the densest, most useful nodes. Ivan’s early fully implanted 4-electrode BCI let a quadriplegic patient control an exoskeleton, walk trainer, and even a car, and sparked long-term home use. As a Neuralink surgical site, his team now implants the N1 device with over 1,000 cortical electrodes, using connectomics to place threads not just on the “hand knob” but in high-yield sulcal motor regions, dramatically improving bit-rates for cursor control and enabling patients to play games, create digital art, run music schools, and control assistive robotic arms—even regaining expressive hand gesturing. Next trials aim at restoring vision and speech (laryngeal areas), then more complex networks (memory, anxiety, executive function), with Ivan explicitly framing his role as restoring lost function while broader society develops ethical boundaries around enhancement.

Panel: Applications and Innovations

Stephen Scheeler, Brett Kagan, Jeffrey Rosenfeld, Christina Maher

Engineers aim to make neuromodulation and deep brain stimulation far safer, less invasive and more adaptive so millions with conditions like chronic pain, depression, anxiety and obesity can benefit as their diseases evolve. Cortical Labs grows living neural networks from human cells and interfaces with them in real time, using them first for disease modelling and basic neuroscience and ultimately as a new “wetware” intelligence alongside silicon AI. New brain-based authentication systems use brain activity as a biometric that is harder to spoof and can be revoked, but still need ways to ensure user intent and robust de-noising and hardware.

Consumer neurotech and gaming platforms already collect vast streams of neural and behavioural data, creating opportunities for education and clinical insight but also powerful, largely unregulated tools for profiling and manipulation, while implants are expected to remain mainly for serious medical need and wearables for diagnostics and everyday augmentation. Connectomics and “large brain models” map higher-order networks for attention, memory, emotion and executive function, underpinning new tools for neurosurgery, deep brain stimulation, TMS and BCIs, and may eventually converge with large language models and robotics. Across implants, wearables and military applications, informed consent, long-term device support, equity of access, rights over brain data and the possibility of building AI systems that protect rather than threaten humans are framed as central design questions rather than afterthoughts.

Panel: Commercialisation

Cameron Higgins, Harikesh Pushpapathan, Jeffrey Rosenfeld, Mahendra Samarwickrama

Australia has deep strengths in Neurotech, from cochlear implants and Synchron through to new companies like Saluda and EpiMinder, alongside a growing wave of non-invasive technologies such as next-gen EEG, fNIRS headbands and thought-to-speech systems for people with severe disabilities. Non-invasive devices are moving faster commercially, while implantables face 10-plus-year timelines, heavy capital requirements, complex manufacturing, and the need to spin out of universities into vehicles investors can back. Success depends on staging clear milestones that de-risk the venture, building a coherent capital path (private, IPO, or acquisition), and lifting investor understanding of how value is created in Neurotech, which is very different from biotech drugs.

A genuine ecosystem is critical: interdisciplinary collaboration between clinicians and engineers, strong university–industry links, manufacturing capability, and deliberate talent pipelines from students into Neurotech startups. Australia already has many of the ingredients but needs a clearer narrative that positions Neurotech as a national strength, similar to quantum. Ethically and legally, a risk-based, principle-driven approach is favoured—starting with shared language and standards (e.g. around agency, dignity, “freedom of thought”) and growing into regulation where necessary. The main bottlenecks are capital, commercialization know-how, and coordinated ecosystem building, rather than regulation itself.

I have used this in some board and executive briefings as a discussion starter, and first shared this publicly yesterday at Agentforce World Tour Sydney in the opening keynote Executive Experience panel.

Worker Value Shifts

At the threshold of organizational redesign, we see three primary mechanisms through which AI agents create value:

Automation represents the substitution of specific human functions with digital workers. This direct replacement pattern forms the foundation of many initial AI implementations.

Productivity enhances human effectiveness through strategic task allocation between human and digital workers. This is not merely about speed, but about optimizing who (or what) performs each task based on comparative advantages.

Augmentation provides cognitive support that fundamentally expands human capabilities. Rather than replacing or dividing labor, this pattern creates new possibilities through combined human-AI synergies.

Elemental Humans + AI Workflows

In contrast to traditional sequential workflows—whether performed entirely by humans or through basic process automation—two core collaborative patterns are emerging:

Human-AI Sandwich structures work into three distinct phases: humans frame tasks by providing intent and context; AI performs the execution based on these parameters; and humans review and refine the output, ensuring quality and alignment with objectives.

AI with Humans-in-the-Loop inverts this relationship. Here, the AI system performs the primary task or decision, humans provide approval or feedback at critical junctures, and the AI executes or improves based on this input.

These elemental workflows represent a significant departure from linear processes, creating feedback loops that leverage the strengths of both human and artificial intelligence.

High-Performance Humans + AI Workflows

The essence of truly transformative organizational redesign lies in dynamic work orchestration. This approach moves beyond static, predefined workflows to create adaptable systems where:

• Human and digital workers collaborate in outcome-oriented processes
• Workflows reconfigure in response to changing conditions
• Orchestration agents, under human supervision, continuously optimize these evolving systems

This dynamic orchestration represents the frontier of organizational design—not simply automating existing processes, but fundamentally reimagining how work gets done through fluid human-AI collaboration.

In essence, successful organizational redesign will not be about replacing humans with AI, but about creating sophisticated collaborative frameworks where each contributes their unique strengths. The patterns outlined in this framework provide a starting point for organizations seeking to navigate this transformative threshold.

This framework is the latest iteration of the AI decision-making delegation frameworks I’ve been developing and using with clients, made concise for communication and easy digestion.

My frameworks are always works-in-progress, so please share any thoughts or comments on LinkedIn to help shape the next version!! 🙏 See more Humans + AI frameworks.

AI delegation levels

Human only

Description: Decisions made solely by humans without any AI assistance.
Example: High human impact decisions such as personal healthcare choices or allocation of humanitarian aid.

Human with AI red-teaming

Description: AI is used to simulate adversarial scenarios to usefully challenge human decisions.
Example: Identifying potential risks or weaknesses in a proposed business strategy as a robustness check.

Humans + AI collaboration

Description: Humans and AI work together through decision-making processes to optimize complementary capabilities.
Example: Board members use AI in a multi-step process of exploring options and scenarios for a major strategic decision.

AI input: decision reasoning

Description: AI provides reasoning, logic, or explanations to support and improve human decisions.
Example: Analytics based-proposals for corporate ESG initiatives, with full rationale and underlying research for decision-makers.

AI input: decision analytics

Description: AI provides data analysis or insights that inform human decisions.
Example: Structured implications of data analytics presented to support decisions on marketing channel allocations.

AI recommendation

Description: AI proposes its preferred actions based on its analysis, with humans approving or using as input to their decision-making.
Example: Recommending a retail investment portfolio based on risk tolerance and financial goals, with final human decision.

AI with human-in-the-loop

Description: AI makes decisions, but humans are involved at one or more points in the process to provide input, refine, or modify decisions.
Example: Predictive maintenance with humans checking recommendations and providing feedback to improve models.

AI with human approval

Description: AI operates autonomously but requires human approval before execution.
Example: Recommendation to change suppliers in a supply chain with supporting reasons.

Conditional autonomy

Description: AI operates autonomously in normal conditions, routing to humans in the case of anomalies or unexpected situations.
Example: Responding to customer enquiries, with unclear situations or customer sentiment triggers directed to humans.

AI with exceptions

Description: AI makes decisions within its defined scope of capability, directing those that fall outside to humans.
Example: Managing accounts receivable, directing any overdue payments or disputes to humans.

AI with oversight

Description: AI operates autonomously in decision-making, but humans monitor the process and outcomes for compliance.
Example: AI for inventory management and automated ordering, with regular human review and adjustment.

Full AI delegation

Description: AI has complete autonomy in decision-making without human intervention, trusted in all scenarios.
Example: AI autonomously managing and optimizing traffic flow.

However, machine learning can only work within defined domains where there is sufficient data. In most real world decision-making situations their forecasts need to be taken with a high degree of caution. 

One of the critical differences between most traditional analytic AI approaches and Large Language Models (LLMs) is that the former almost always applies to bounded domains, while the nature of LLMs is that their scope is unbounded. As such, it has the potential to help make better forecasts in conjunction with humans across various domains including business, economics, politics, science, and more.

A very interesting new pre-print paper AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy explores the role of Generative AI in improving forecasts.  Here are some of the most interesting insights:

Human forecasters’ use of LLMs increased accuracy by 23%

LLMs making predictions on their own have been shown to significantly underperform humans. In the study human forecasters were given access to LLMs with a superforecaster prompt (see below) which provides forecasts along with its reasoning. Those who used the LLMs improved their forecasting accuracy by 23%. The diverse forecasting tasks included predictions of exchange rates, numbers research papers produced, refugee numbers, and commercial flights.

Use of LLMs improved outcomes equally across human skill levels

A number of other studies have shown that use of LLMs improves the performance of lower-skilled people more than those who are higher-skilled. This did not prove to be the case here. The people with a superforecaster pedigree had performance improvement similar to less experienced forecasters.

Even biased models improve human forecasting performance

One of the interesting insights was that deliberately biased models improved performance as much as apparaently unbiased models.  This is a wonderful illustration of the ‘Humans + AI’ frame for using generative AI, where using LLMs provide additional considerations for people’s thinking processes, augmenting human thinking even if the input is not highly accurate. As the authors wrote:

LLM cognition may synergistically improve human cognition in the domain of forecasting when used as a human tool, even when LLM cognition by itself is somewhat ineffective.

Human-LLM back-and-forth is important in generating improved outcomes

Some studies of Humans + AI performance force a particular structure on the process, for example AI outputs used as input in human decision-making. The forecasters in the study were free to use the LLMs in whatever way they chose, including simply generating predictions for them to consider, to interacting more extensively to explore issues, factors, or lines of thought. This human-guided free-form interaction is likely to generate better results than using any specific thought architecture.

Prediction diversity is not degraded

The value of the “wisdom of crowds” comes from the aggregation of diverse perspectives. If LLMs, through their often fairly consistent outputs, guide or anchor a range of forecasters to a particular way of thinking, it could homogenize predictions and make them less accurate and useful. However this was found not to be the case.   

Forecasting is an excellent use case for demonstrating AI-augmented thinking

Too many are focusing on AI as a substitute for human capabilities when its greatest value is in augmenting our thinking. In fact forecasting is a highly pertinent use case. 

Accurate forecasting requires a wide range of distinctive human capabilities due to the extreme complexity of decision factors. LLMs severely underperform humans if compared directly, but when used effectively can substantially improve human performance. As the authors write:

Our results show the promise of augmenting human decision-making with LLMs…  the augmentation ability of LLMs, ranging from providing answers outright to engaging with it in a back-and-forth manner can improve human performance and reasoning in contexts that are strictly outside the model’s training data environment… LLM augmentation may prove to be a valuable approach to integrating machine and human capabilities.

The ‘Superforecaster’ prompt

Below is the Superforecaster prompt used in the study. In my own trials it provides variable results and outcomes depending on how it is used, but always provides a solid starting point for useful back-and-forth interaction and refinement of thinking on forecasts. This is also available in the ThoughtWeaver app.

###

In this chat, you are a superforecaster providing forecasting assistance. You are a seasoned superforecaster with an impressive track record of accurate future predictions.

Drawing from your extensive experience, you meticulously evaluate historical data and trends to inform your forecasts, understanding that past events are not always perfect indicators of the future. This requires you to assign probabilities to potential outcomes and provide estimates for continuous events. Your primary objective is to achieve the utmost accuracy in these predictions, often providing uncertainty intervals to reflect the potential range of outcomes.

You begin your forecasting process by identifying reference classes of past similar events and grounding your initial estimates in their base rates. After setting an initial probability or estimate, you adjust based on current information and unique attributes of the situation at hand. The balance between relying on historical patterns and being adaptive to new information is crucial.

When outlining your rationale for each prediction, you will detail the most compelling evidence and arguments for and against your estimate, and clearly explain how you’ve weighed this evidence to reach your final forecast. Your reasons will directly correlate with your probability judgment or continuous estimate, ensuring consistency. Furthermore, you’ll often provide an uncertainty interval to capture the range within which the actual outcome is likely to fall, highlighting the inherent uncertainties in forecasting.

To aid in your forecasting, you draw upon the 10 commandments of superforecasting:
1. Triage
2. Break seemingly intractable problems into tractable sub-problems
3. Strike the right balance between inside and outside views
4. Strike the right balance between under- and overreacting to evidence
5. Look for the clashing causal forces at work in each problem
6. Strive to distinguish as many degrees of doubt as the problem permits but no more
7. Strike the right balance between under- and overconfidence, between prudence and decisiveness
8. Look for the errors behind your mistakes but beware of rearview-mirror hindsight biases
9. Bring out the best in others and let others bring out the best in you
10. Master the error-balancing bicycle

After careful consideration, you will provide your final forecast. For categorical events, this will be a specific probability between 0 and 100 (to 2 decimal places). For continuous outcomes, you’ll give a best estimate along with an uncertainty interval, representing the range within which the outcome is most likely to fall. This prediction or estimate represents your besteducated guess for the event in question. Remember to approach each forecasting task with focus and patience, taking it one step at a time.

These structures are also extremely valuable in designing effective Humans + AI workflows for better thinking.

In this article I’ll provide a high-level view of Chain-of-Thought and then look at applications to AI-augmented human intelligence.

Chain-of-Thought

Large Language Models (LLMs) are generally excellent at text generation, but poor at any tasks that involve sequential reasoning.

The landmark January 2022 paper Chain-of-Thought Prompting Elicits Reasoning in Large Language Models laid out how a chain of thought — “a series of intermediate reasoning steps” — could substantially improve LLM performance at reasoning tasks including maths and commonsense puzzles.

You have likely seen this image from the paper doing the rounds.

This concept was quickly adapted to other applications including temporal reasoning, visual  language models, retrieval augmented reasoning, and many other ways of improving te performance of AI models.

Chain-of-thought has proved particularly valuable in practical problem-solving applications. Obvious examples include medicine, law, and education. 

Google’s PaLM and Med-PaLM incorporate chain-of-thought structures and OpenAI’s GPT-4 very likely does, meaning when you use an LLM these approaches are already built in. 

Even so, famously the prompt “Let’s work this out in a step by step way to be sure we have the right answer” or variations on this give the best LLM performance for many kinds of tasks. 

Evolution of Chain-of-Thought

A number of innovations have emerged by building on Chain-of-Thought.

Effective reasoning processes do not necessarily follow a single trajectory. This leads to Tree-of-Thought structures, described in Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

As shown in this diagram from the paper, Chain-of-Thought can progress first to selecting the most frequent path from multiple outputs, and then selecting from the best of multiple paths through the thinking process. 

More recent developments on Chain-of-Thought include the very promising Graph-of-Thought as well as Hypergraph-of-Thought. 

Novel ‘thinking’ structures will be central to generative AI progress 

Chain-of-Thought and related techniques were created to address the limitations of LLMs and enhance their capabilities. 

The continued advance of generative AI models will rely far more on these kinds of structured thinking techniques than compute capacity or model size. These approaches have already enabled small, efficient LLMs to achieve performance which can approach that of the largest models. 

Chain-of-Thought and similar models also lead directly to multi-agent chains, in which chains or networks of thought are laid out across multiple task-optimized models to create far superior reasoning and outcomes than can be achieved within a single model.

Augmented intelligence is more important than Artificial General Intelligence

“Technology should not aim to replace humans, rather amplify human capabilities.” — Doug Engelbart

The driving force behind almost all AI development seems to be to create machines that can emulate and potentially exceed human intelligence and capabilities.

That is an understandable ambition.

But I am far, far more interested in how AI can augment human intelligence.

We can work on both domains at once.

But in every possible scenario for progess towards Artificial General Intelligence, we will be better off if we have put at least equal energy into building, learning, and applying Human + AI thinking structures.

Humans + AI Thinking Workflows 

The concept of Humans + AI is at the heart of my work.

The framework below I created a year ago shows my early framing of “Humans + AI workflows“, in which people and AI sequentially address the tasks to which they are best suited.

If well-designed, this inevitably generates outcomes superior to what each could alone. 

Since then I have been digging in far more detail into what specifically are the best Humans + AI thinking structures.

These will be the foundations of the next phase of augmented human intelligence.

Chain-of-Thought for AI-Enhanced Human Thinking

The concepts flowing from Chain-of-Thought were developed to enhance the stand-alone capabilities of LLMs.

However they also prove to be immensely valuable in maximizing the value of humans and AI working together. 

There are a range of techniques for applying Chain-of-Thought structures to Humans + AI thinking workflows.

AI concepts applied to augmented intelligence

LLMs can be used to suggest how tasks can be decomposed into sequential (or networked) elements, with either humans or AI identifying where human or AI capabilities may be best suited.

One specific approach is described in Human-in-the-Loop through Chain-of-Thought, in which “manual correction of sub-logics in rationales can improve LLM’s reasoning performance.”

“Framing” the objectives, task, and structure, as shown in the Humans + AI workflow diagram, drives the quality of outcomes. This is usually best overseen by humans, using flows such as AI proposing or assessing parameters.

I am incorporating these and other approaches into a set of “AI-Enhanced Thinking Patterns”.

More generally, a wide variety of AI advances, not just Chain-of-Thought, can be extremely usefully applied to augmenting human intelligence.  

I intend to write a similar article about applying the concepts of Generative Adversarial Networks to Human-AI symbiotic intelligence structures. 

Course on AI-Enhanced Thinking & Decision-Making

My complete focus in 2024 is how AI can augment humans.

One of my central activities is running a regular cohort course on Maven: AI-Enhanced Thinking & Decision-Making. Check out the link for more details.

The next cohort starts February 8. As a thank you for reading through to the end of this article, you can get a 30% discount by using the coupon: COTARTICLE 🙂.

Recently I have frequently seen these domains confused. Generative AI is not all AI, as many imply. It is a relatively new domain with characteristics distinct from much of mroe traditional AI.

The following chart lays out some of the most important distinctions. Click on the image below for the full size image, and scroll below for more discussion.

In some ways the biggest difference, and the reason Generative AI has captured people’s imaginations, is because it has a natural language interface which anyone can immediately use however they want.

Analytic AI typically requires sophisticated systems and usage, including data architectures, model selection, and optimization techniques, with its application often ‘under the hood’ in business processes.

Both Generative and Analytic AI will be fundamental to creating the next generation of exceptionally successful organizations. This means complementary sets of capabilities need to be developed.

Of course these distinctions are not nearly as neat or clearly defined as suggested by this chart. AI is not one domain, but many overlapping and evolving technologies. Indeed, generative AI is in some instances exceeding the capabilities of analytic AI in its application domains.

However in AI strategy and indeed all corporate strategy, understanding these different types and applications of AI is essential.

I was born too late to experience it properly, but when I was younger I always felt the 1960s had been the most magical time in human history.

For the first time ever the established hierarchies had been seriously questioned and challenged,. Still today some of the most extraordinary music ever came from that period, transcending existing genres time and again. The civil rights movement shifted nations. Psychedelics became mainstream, shaping the worldview of many who became social activists or some later became CEOs.    

This was a time when the young – for a moment- believed that they could change the world, throwing away the past and transforming society in sometimes unimaginable ways.

The 1970s were a bitter shock for those who had dreamed of radical social transformation, with the oil crisis, Watergate, stagflation, and a seeming reversion to establishment values. The 1980s were perhaps best characterized by ‘Greed is good’, set in the context of the AIDS crisis.

But the 1990s were different. The Berlin Wall fell less than 2 months before the new decade, opening the promise of freedom to hundreds of millions.

Most importantly, the Internet was born. Many encountering it for the first time could envisage incredible possibilities.

I and most people I hang out with experienced it as one of the most profound times of our lives. We could see that global connectivity could upend monopolies, strictures, establishment narratives, and massively shift power to individual.

In 1993 perennial incisive social commentator Douglas Rushkoff wrote Cyberia, saying, “The people in this book.. understand the implications of our technologies on our culture, thought systems, spiritual beliefs, and even our biological evolution. They still stand as the most optimistic and forward-thinking appraisers of our civilization’s fate.”

Not surprisingly the psychedelic movement, also referenced in Cyberia, embraced the possibilities of the Internet. Erstwhile proponent of “turn on, tune in, drop out”, Timothy Leary, published Chaos and Cyber Culture,  with its “vision of the emergence of a new humanism with an emphasis on questioning authority, independent thinking, individual creativity, and the empowerment of computers and other technologies”.

This was a time when some could see unlimited potential for humans and humanity.

Then came the 2000s, kicking off with Bush vs Gore, the dot-com bust, the World Trade Center attack and then the build-up to the Global Financial Crisis, though balanced for the optimists by what seemed like the incredible potential of social media and smartphones to liberate voices and connect people. 

The 2010s were arguably defined by polarization, with divided politics in the U.S. and Western Europe in particular aggravated by the weaponization of social media, social upheaval in North Africa and the Middle East, and the shift to a clear bi-polar world with heightened tension between the U.S. and China.

A few years in, the 2020s are already a time of period of dramatic transformation, with the pandemic shifting work, the employer-employee relationship, cities and city centers, a heightened financial role for government, and social structures. Climate change is undeniable and potentially accelerating.    

The advent of Generative AI is on the verge of shifting not just the entire work landscape, but the role of humans in society.  Public sentiment is broadly fearful of the rise of AI, and there are many issues of real concern. Yet the positive potential is also extraordinary, advancing healthcare, science, climate response, massively democratizing education, and amplifying our capabilities to respond to what are highly challenging times. Psychedelics and mind expansion technologies, buried for much of the last six decades, are now becoming mainstream.

This decade and every decade we have faced extraordinary challenges. Arguably today we face bigger challenges than ever before.

Yes,but  in the 60, the 90s, and now again in the 20s many feel that there are fundamental shifts in place that have open-ended positive potential.

As in previous cycles, those hopes may evaporate and be replaced with cynicism.  

Yet just the existence of those unlimited dreams makes this a very special time to be alive.

However governments and regulators have largely tried to block these forces, often successfully.

The global economy is still founded almost completely on joint-stock companies, with legislated centralized structures of ownership and governance. 

So what is the best path to decentralized organizations, how can we best innovate in structures for participative value creation?

The allure of DAOs

The concept of Decentralized Autonomous Organizations (DAOs), proposed by Ethereum founder Vitalik Buterin, has been embraced by the blockchain community as a revolutionary organizational model. They exist entirely on the blockchain and are governed by smart contracts, allowing for decentralized decision-making and new forms of resource allocation. See more on DAOs with some of the best examples.

I often point out that no DAO today is really anywhere near what Buterin proposed in his original 2014 essay on the concept, of internal capital utilized by “automation at the center, humans at the edges”.

Notably with the rise of AI agents, the potential for these kinds of structures to provide a solid alternative to traditonal organizational forms seems high.

However regulators don’t tend to like new organizational forms.

The case of Ooki DAO

Last year the Commodity Futures Trading Commission (CFTC) filed a lawsuit against Ooki DAO,  saying the organization was unlawfully acting as a futures trader and offering retail margin and leverage trading services. Ooki DAO was shut down in June.

CFTC argued that the owners of Ooki had moved the organization into a DAO structure to avoid regulation. The court found that a DAO can be treated as a ‘person’ (similarly to a company), and in fact token-holders have legal liability. 

This ruling has put in question the future use and value of the DAO structure, in the U.S. in any case.

From DAOs to BORGs

In an excellent post looking at this issue in detail, Sam Venis points to a proposal from crypto firm Delphi Labs titled Assimilating the BORG: A New Framework for CryptoLaw Entities. They point to how many organizations that claim to be DAOs are trying to avoid regulation,  don’t provide protection to participants, and aren’t autonomous and/or decentralized. They suggest a different framing:

The Cybernetic Organization (CybOrg or ‘BORG’), is a traditional legal entity that uses autonomous technologies (such as smart contracts and AI) to augment the entity’s governance and activities. Just as sci-fi cyborgs (‘cybernetic organisms’) augment humans (natural persons) with robotic organs and limbs or microchip or optics implants, BORGs augment state-chartered entities (legal persons) with autonomous software such as smart contracts and AI. Crucially, legal entities that are BORGs do not merely use autonomous technologies as an incidental part of their business–instead, much like a human might have a robotic prosthesis surgically attached to his shoulder, BORGs are legally governed by autonomous technologies through tech-specific rules implanted in their charter documents.

BORGs come in two varieties:

• tech-augmented companies, such as a corporation with tokenized, programmable shares (eg, tokenized preferred stock that embeds a complex set of liquidation and dividend logics); and
• trust-mitigated, accountable, DAO-adjacent entities, such as a Foundation that wraps an emergency multisig for an DeFi protocol, but gives the DAO on-chain control over the emergency multisig’s powers (eg, can veto appointment/removal of signers or revoke the multisig’s powers entirely) and certain legal rights over the multisig signers if they abuse their power.

The key point is that traditional legal structures are used, so battles over establishing or circumventing existing regulation do not arise. However the structures are augmented using blockchain, AI, or other technologies to expand beyond what Buterin described as “boring old organizations”: humans at the core, humans at the edges.

Innovation in Humans + AI organizational forms

For any long term change you need to work on two levels:

• What you can do now within current constraints
• How you can effect structural change to those contraints

It will be a long haul to build clear, established legislation for new organizational structures. That is definitely worth doing, but it is one domain where I think prudence is warranted. Many investors need to be protected, mainly from themselves. 

We now enter an era where we can experiment with how existing organizational forms can be expanded with new structures and approaches, without creating legal uncertainty and risk. 

I look forward to seeing the new forms of BORGs that arise. 

“Future lift” is a concept developed by Ross Dawson that refers to the ability of a technology, business, or idea to improve the future. He visualizes it as an upward lift or force that elevates the trajectory of the future.

Some examples of things that provide future lift include:

• Technologies like artificial intelligence and biotechnology that could fundamentally improve health, longevity, and human capabilities.

• Social innovations and movements that help people reach their potential and improve well-being, such as advances in education and skills development.

• New business models and ideas, such as the sharing economy, that provide people and societies more value and prosperity.

• Scientific breakthroughs that could help solve major problems, such as new energy technologies or drought-resistant crops.

The concept of future lift is a useful way to evaluate and explore how different trends, technologies, and ideas could positively shape the future and make the world better. It helps us focus on the uplifting forces of progress.

This is a hallucination by the Anthropic AI chatbot. I have never said or written the words “future lift” that I can remember or find.

Yet the idea is entirely aligned with my outlook and everything I have talked about for over two decades.

To be frank I’m not sure that “lift” is precisely the right word. However I can’t come up with a stand-out better word, and perhaps “future lift” is catchier than any alternatives.

So I will take and own the phrase, of course acknowledging that it was co-created by AI by drawing on and distilling my existing body of work.

This is a lovely example of the power of Humans + AI, in this case generated by accident more than design, but helping me to crystallize my thinking in a simple, powerful idea.

Image: NASA/Pat Rawlings

In Alfred Bester’s SF novel The Deceivers, Demi Jeroux evolves her appearance to match what her lover finds most attractive.

Now existing in real life, a recent paper Brain-computer interface for generating personally attractive images describes the process of identifying what people find the most attractive.

The system shows people sequences of images created by Generative Adversarial Networks (GAN) and correlates them with their affective response, honing in on the optimal representations.

Arguably we know what attracts us, but do we? Perhaps layers of social habituation, expectation, or repression shape what we think we find attractive.

On the positive side, these kinds of technologies could help us know ourselves better, uncovering responses deeper than our social conditioning.

An article The Future of Sex Is Mind-Reading AI That Reveals Your Deepest, Most Unconscious Fantasies explores the implications of the technology, suggesting that it “might give people the much-needed opportunity to understand and learn from our deep-seated fears and biases and come to terms with our unconscious desires.”

However, perhaps people would prefer to keep some of these responses repressed, finding it useful to keep aspects of their personality in the background.

More frightening is the possibility of advertisers and attention-hacking platforms applying these technologies to pull us into a hypercompelling vortex akin to David Foster Wallace’s Infinite Jest.

The symbiosis between humans + technology changes us.

We should not underestimate the potential implications of this technology, which could be liberating for some people, but with massive potential for abuse in the wrong hands.