This framework is highly simplified, designed to draw out the spectrum from purely algorithmic decisions through to human-first decisions augmented by AI, across different asset classes. A few comments below.


Click on the image for full-size pdf

AI-first decision-making requires ready availability of quality information, consistent context, and is greatly aided by liquidity and equitable market access, not intermediated by humna relationships.

From there, AI decisions are supervised or facilitated by humans, shifting to human-first decisions augmented by AI. There will no longer be any investment decisions with no AI role.

Human first decisions are characterized by complexity, limited or hard to interpret data, longer timeframes, unpredictable environments, high stakes, requireing stakeholder involvement, and where there may be human or social impacts.

One particular dynamic is that in venture capital and private equiry there is not only limited data that can be readily analyzed without strong relationships, or even with them, and in VC as well as private equity relationships are critical to be aware of the opportunity as well as to be able to invest in the face of investor competition.

This is a very high-level, simplified frameowrk designed to convey the scope of poential AI involvement in investment decisions. I will be sharing some of the layers behind this over time.

As AI agents grow in capabilities, individuals are delegating tasks to single agents that can perform tasks on their behalf such as research, curation, scheduling, travel planning, or email drafting.

Companies are “employing” agents to perform a wide range of functions, including customer service, content creation, sales outreach, invoice tracking, and internal communications.

And multi-agent systems are bringing together a variety of specialized AI agents to collaborate for better outcomes in more complex situations.

In simple configurations these agents have clearly defined tasks and responsibilities. However a critical component of the definition of an agent is that it has some degree of autonomy. As the systems we build become more complex, we will need to give more responsibility to our agents.

One of the most important thresholds is giving agents the ability and permission to make payments.

In Humans + AI multi-agent systems, agents could make payments to humans for particular tasks.

In 2014 I wrote about the potential of machine-to-machine payments, enabling autonomous agents or devices to make payments between themselves.

Scenarios included autonomous cars making payments to each other to facilitate paths, or systems negotiating bandwidth pricing with each other depending on demand.

A number of companies are now already enabling this key infrastructure for a massive explosion in the complexity of the economy.

Skyfire describes itself as “the financial stack for the AI economy”, enabling agents to discover and pay for the services they need to fulfil their tasks, applying specific business rules.

Payman, with a tagline of “AI that pays humans”, recognizes that humans will still play important non-substitutable roles, and enables agent systems to pay humans to do work to achieve its objectives.

This is just the very beginning. As I wrote in 2014, “an entirely new layer of the economy could open up” with agent to agent payments. The number of economic entities is likely dramatically multiply.

I’ll explore some of the potential implications in other posts.

I was honoured to be interviewed for the article alongside luminaries such as Brett King and Heathervescent.

We asked a handful of futurists to share some thoughts on a few scenarios [for the future of global currencies and the internet], mostly in the name of a fun thought-experiment. The real world consequences are serious, though. “This is not just fun, it’s critically important,” says futurist Ross Dawson. “The world of money could change fundamentally in the next 10 years, and the implications could be massive. This is something we really need to be actively thinking about.”

See the full article to explore all four scenarios.

I was quoted most on the ‘Blue Scenario’, in which a non-government-backed cryptocurrency (such as bitcoin) gains dominance.

One of the most fundamental questions on the future of money is the degree to which independent cryptocurrency plays a role in personal and business finance.

The current total market capitalization of all crypocurrency is just over 1% of US M2 money supply.

In 10 years will crypto have a massively larger share of traded currency? Or will it be similar than now or even smaller?

The reality is that Central Bank Digital Currencies CBDC) are rapidly developing – they will inevitably a large part of the future of money.

As a futurist I consider the potential paths by which an outcome may come to pass.

In the case of a massive rise in crypto, that will be essentially because governments do not do their job in overseeing a well-functioning financial system, or they are not trusted.

Right now the prospect of loss in trust in government and social disruption, potentially even civil war, in some countries over the next decade is certainly plausible, unfortunately.

In that event there’s a strong chance that non-government crypto will do well, generating multiple new economies. I’ll write more about this another time.

International uncertainty may drive cryptocurrency usage. “If we have a stable geopolitical structure, where most people and most nations feel secure, then that will not encourage a large rise of non-government digital currencies,” reasons futurist Ross Dawson.

“Whereas if we have a deep social division and disruption – and civil wars in developed countries in the next decade, that’s very plausible, depending on how you define ‘civil war’ – this will fracture societies and trust in government, and could lead to wholesale shifts to cryptocurrencies.”

With apologies to the crypto super-bulls, this is not necessarily the Lambo Scenario or Moon Scenario. Dawson imagines a potential world of “dueling economies” – even within the United States – if a cryptocurrency emerges dominant. One will be the official legal economy that’s regulated by the U.S. government (like today), and the other an unregulated “shadow economy” that’s dominated by the cryptocurrency.

“There will always be national currencies,” says Dawson. “We’re never going to have a time when the government says, “Okay, we give up, we’re not going to do this anymore.” (He later clarifies that maybe “never” is too strong of a word, but certainly not in the next decade.)

So the question is what’s the balance between the shadow economy and the regulated economy? He points to Italy’s shadow economy as an example, which by some estimates is more than 12% of the nation’s GDP – largely the result of tax evasion.

The ideas presented in the report include the rise of “fit-nance” tracking of holistic advice, financial advisors focusing on life coaching, and ubiquitous user ratings of advisors.

One of the key concepts in the report is that financial advice will be substantially provided over Netflix-like subscription platforms:

The “Netflixing” of advice
In 2030, up to 80 percent of new wealth management clients will want to access advice in a Netflix-style model — that is, data-driven, hyper-personalized, continuous, and, potentially, by subscription.

The emergence of a tailored and personalized model underpinned by data can be observed across industries, but is perhaps most prevalent in entertainment — in the shift from physical music recordings to unlimited streaming of digital music, or from seeing films in movie theatres to streaming them from home — or from anywhere. The “streaming giants” are using customer data to continuously and deeply understand preferences and develop hyperpersonalized recommendations.

For wealth managers, continuous access and automatic hyper-personalization could change the terms of success. Advisors can embark on the journey now by using data and technology on a more frequent and consistent basis.

Of course the 80% of “new” wealth management clients will on average be less affluent than the industry’s current client base. However from the advent of digital platforms, many private banks and wealth managers were surprised by the speed at which their High Net Worth clients chose to migrate to digital interfaces.

As I discussed at length in my first two books, Developing Knowledge-Based Client Relationships and Living Networks, a key challenge with digital platforms for high-value clients is the potential disintermediation of human relationships, and the design of integrated digital and human relationships to provide both exceptional client value and deep engagement.

The rise of “robo-advisors” that use AI to to provide personalized advice is now close to a decade old and gaining momentum.

What has been largely missing is the use of rich data to make both the advice and its delivery substantially more customized. This needs to include at a minimum behavioural data, and potentially even information on emotional responses to investing activities.

Invoking Netflix of course points to the potential for platform winners based on breadth of offers and greater customization from deeper levels of customer data.

As I have often noted, the end-game of AI-based assistants is indeed a winner-win-all market. That is less likely to happen in financial advice, but it points to the potential value of these approaches.

So will finance advice be delivered primarily through Netflix-like subscription platforms? The answer depends largely on whether today’s (or tomorrow’s) financial institutions are able to design and implement them effectively.

While current wealth management companies are coming from a history of advisors at the core of the business, tech-centric newcomers probably do not fully understand the role of human relationships in personal investment decisions, that by their nature are often emotional.

There are certainly opportunities aplenty as wealth management expands its value creation to far more diverse groups, leveraging technology while maintaining humanity.

The event kickoff brought together three featured speakers – physicist and futurist Michio Kaku, trend watcher Daniel Levine, and myself – who each delivered a keynote and then joined a panel discussion.

My keynote was on The Future of Finance. In my session I addressed a number of key issues, including the rise of distributed economies and finance, the evolution of ‘post-capitalism’, and the emerging future of work.

Efficient capital markets

One of the key themes of my keynote was the continued development of more efficient capital markets.

There are over $140 trillion in investable assets in the world seeking risk-balanced returns in cash, debt, equities, real estate and now a widening range of ‘alternative’ asset classes.

The possible real value of that capital is immense if it is applied efficiently to those ventures that have the highest potential, both in financial returns and in social value.

Transparency and non-financial measures

The most important single enabler of more efficient capital allocation is transparency. The more information we have on the ventures we invest in, the better investment choices can be made.

A central part of this is in having information on non-financial measures. The most important clues to potential value creation usually come not from financial reports, but other information that give insights into issues such as customer engagement, innovation, organisational renewal, and culture.

This will help drive a gradual disaggregation of corporate reporting, where instead of highly aggregated financial reporting that hides much of the most important information far greater detail becomes available. This will spur new forms of analysis, sometimes AI-driven, that support better investment decisions.

Accounting for externalities

In addition, the investment climate is shifting to increasingly take into account what economists describe as ‘externalities’: factors that are outside an organization’s financial consideration or reporting, including environment and social impact.

Last year six of the world’s largest sovereign wealth funds with aggregate assets of $3 trillion stated they would only invest in companies that incorporated climate risk issues in their decision-making. We are still relatively early in a shift in investment decisions that account for factors outside traditional financial reports.

The demand from investors for this kind of information will drive innovation in accounting and non-financial reporting, and heightened attention to non-financial impacts outside the corporate entity.

The future of governments and society

The concept of ‘government as platform‘ suggests that governments should be enablers of social value creation, rather than focused on spending taxpayer money in direct service delivery, often inefficiently. Governments too allocate capital, and there is vast scope for improvement in how they do that.

In order to shift to being platform enablers governments need to be able to measure social value. Social impact bonds are an example of structures that draw directly on social impact data to price financial vehicles, often enabling NGOs to achieve social outcomes far more efficiently and effectively than governments can.

Better capital markets at the heart of our future

I am actively exploring the rich topic of ‘post-capitalism’ in how we address the flaws of capitalism as we know it (more on that in another post). However capital will always exist in some form, and the vast wealth of the world will continue to seek to create value, increasingly social as well as financial.

The possibility of a better future world will necessarily be founded on more efficient capital markets, founded on transparency and information flows, that are focused on wealth creation in the broadest possible sense.

Regarding Bitcoin:
When I was in financial markets I learned about the very plausible “perverse market theory” – the market acts to hurt the most people the most – draws the suckers in, then wipes them out, gives them hope, then tramples on them.

Many are going to get hurt.

— Ross Dawson (@rossdawson) December 8, 2017

I was later asked for more information about perverse market theory, and after digging around I have drawn a blank. The term “perverse market” is usually used to refer to unintended or unanticipated market responses, but that is a different meaning from the one I referred to here.

Perhaps my memory fails me on the concept’s name (let me know if you can instruct me on this!), but the idea really struck me when I heard about it in my early career working in financial markets.

Do markets want to hurt people?

The idea of perverse market theory essentially anthropomorphizes the markets, attributing it intent, not dissimilarly to how Kevin Kelly describes directional behaviors in the development of technology in his book What Technology Wants.

Of course a financial market does not have intentions. However its movements often do effectively hurt market participants, drawing increasing numbers of unsophisticated investors into speculative asset bubbles, giving people hope and getting them to buy more when prices bounce on the subsequent fall, and generally suckering people into extreme market mis-timing.

The manifestation of greed and fear

This makes sense in that a market is in fact an aggregation of human behavior, and specifically an aggregation of the primal emotions of greed and fear. As such its fluctuations are an expression of human emotion.

Greed pushes up prices which attracts increasing degrees of greed, meaning most purchases are made when prices are too high. When things go awry the fear is compounded, pushing down prices so that sales are made below true value, with bounces along the way allowing human fallibility and cognitive bias to weak the most damage.

It is an valid point that algorithms are increasingly driving markets, however these are all overlaid on the underlying human behaviors that drive markets, only adding variations, not fundamental dynamics.

Crypto-currencies have become almost purely speculative assets

Which bring us to Bitcoin and crypto-currencies. A number of people responded to my tweet saying that Bitcoin is entirely unlike traditional financial assets.

It is true that crypto-currencies come from a completely different intent to existing financial assets, and are substantially different kinds of assets.

However once people start to treat an asset as a speculative investment, its behavior will be exactly the same as any other asset, be it shares, property, or tulips.

Only a tiny minority of people today are buying Bitcoin for transactions. In fact it is now being used less for financial transactions – which is what it was created for – due to its volatility and increased transaction costs, with one major online sales channel Steam stopping accepting Bitcoin earlier this month.

As such Bitcoin today has become almost purely a speculative asset – a complete perversion of its original intent.

The true value of crypto

Vitalik Buterin, the founder of Ethereum, last night tweeted a thread beginning:

So total cryptocoin market cap just hit $0.5T today. But have we *earned* it?

— Vitalik Buterin (@VitalikButerin) December 13, 2017

(Click on the link to see the full thread)

His conclusion was that crypto-currencies have created real social value, but nothing like the financial asset value attributed to them.

As Bitcoin creator Satoshi Nakamoto, Buterin designed his crypto-currency to create “real” value, not financial value. The two have become fundamentally misaligned through speculation.

The future value of Bitcoin

Financial analysts refer to Bitcoin going through a “price discovery” process, fluctuating as we find what its “true” value is.

When I was faculty on a Singularity University program a few years ago, Bitcoin Foundation President Brock Pierce was asked what he expected the value of Bitcoin to be in five years (it was around $400 at the time). His answer was “either zero or $50,000”.

Thinking in terms of scenarios from today, it is certainly possible that the long-term value of Bitcoin will substantially exceed $100,000. This could happen if it eventually becomes a widely used currency, or if it is perceived as a store of value.

It is also possible that the long-term value of Bitcoin is significantly less than it is today, with the recent spike proving to be a classic asset bubble that will go down in history.

It is impossible to know today. I am not fundamentally pessimistic about the long-term value of Bitcoin. However its extreme volatility today is making that less likely.

People will get hurt

Which comes back to today’s bubble. It is frightening to see highly unsophisticated people pulled into buying Bitcoin with absolutely no idea what they are doing. Some of them have done well, and some of them may continue to do well.

But markets often hurt people, particularly those driven by greed, which is the only way to describe the motivation of the latecomers to the Bitcoin party.

Whatever happens, many will get hurt.

And the really sad part is that all this is taking away from the dream and the potential of Bitcoin and crypto-currencies.

Source: Commonwealth Bank

A few years ago I wrote The new layer of the economy enabled by M2M payments in the Internet of Things, envisaging the potential of machine-to-machine payments.

I described how micro-payments between machines could lead to far greater efficiencies as for example cars, drones, or farms negotiated directly with each other to achieve their objectives without the necessity for human involvement. I noted that the cost efficiency of crypto-currency payments made them a strong contender for a platform for the M2M economy.

Commonwealth Bank is one of the banks globally pushing forward in the space as these ideas move from the future into the present. The report notes:

Our research has confirmed that many high value physical assets in business are significantly under-utilised. A key observation is that the different and complementary capabilities of various machines will inevitably lead to a market for machines to share services with one another.

To date, our research has identified over 20 potential use cases for machine-to-machine economy experimentation. The implications could be especially significant for sectors including manufacturing, logistics, resources, agriculture, energy and infrastructure.

We are currently working with our partners and customers to understand how banks can provide value added services to smart machines by leveraging Internet of Things data and automating processes and marketplaces using blockchain-enabled smart contracts.

There are many implications to be explored before this goes mainstream. This includes governance and ethical considerations:

In the short term, it’s easy to envisage smart contracts with built-in guardrails embodying essential regulatory and business rules – from reporting requirements to transaction value limits. But as artificial intelligence (AI) begins to arrive at scale, the question is whether these mechanisms will be enough. That’s why it’s important for intelligent machines to learn about regulation and socially responsible behaviour, as well as about business. In the same way as we educate our children to follow social norms, we need to ensure our machines are not only smart, but ethical.

Another key issue is that as independent systems negotiate with each other there can be highly undesirable unintended consequences.

In the 1990s I closely followed the work of Pattie Maes and her MIT Software Agents group, which researched the possibilities and implications of an agent-based economy. Much of the research of the MIT team and their peers is only now becoming relevant as independent, relatively intelligent software agents are on the threshold of building a substantial non-human economy.

We now need to investigate and understand how that autonomous economy may actually work. The potential for economic and resource efficiency is enormous, but we need to be aware that autonomous multi-agent systems just might run amok.

Image: Ricardo Rels

The Global Economic Policy Uncertainty Index is currently the highest it has been since the beginning of the period analyzed starting at the end of 1996.

Not surprisingly the US, EU and UK were at all time highs at the most recent index point on October 31, 2016, just before the US elections and with no clear direction post the Brexit vote, but Chinese economic policy is also highly uncertain.

Uncertainty should be a critical input into decision-making, yet there have been no good measures of uncertainty available. Stockmarket volatility is a proxy for economic uncertainty, but is often driven by market factors more than macro-economic outlook.

The Global Economic Policy Uncertainty Index only covers a portion of business and even economic uncertainty, but when economic policy is highly uncertain, that uncertainty flows through to the broader economic outlook.

Analysts at French bank Société Generale have pointed out that while sovereign credit spreads have usually closely tracked the index, they have decoupled to an exceptional degree over the last months, leading to SG’s (often bearish) global strategist Albert Edwards describing this as “one of the scariest charts I have seen for a very long time”.

As remarkable are the increases in US and some other stockmarkets in the face of extremely high uncertainty on not just economic policy, but also trade and geopolitics.

Global uncertainty seems unlikely to abate in the near-term, though it is feasible that the Trump administration will move to a degree of predictability over the next half-year, and we will see more clarity on not just Brexit, but the broader implications for the EU.

One of the values of the index is simply allowing decision-makers to frame economic uncertainty as a factor in their strategies and decisions, and to support the use of scenario planning and other foresight tools to make effective decisions in uncertain times.

In the video we begin by addressing the question: Why do professional services leaders need to think about the future? and go on to discuss what to expect at the conference.

Points we make in the video include:

* Professional services is one of the most important industries on the planet, and its role and scope will continue to increase.

* In many ways for professional services firms, the future is already here in some form. Firms need to be acting now, otherwise it will be too late.

* Globalisation, new competitors, connected work, and artificial intelligence are among the forces dramatically shifting the professional services landscape.

* How clients buy and meet their needs for professional services may be very different from today, with many of these shifts already well in play.

* Digitisation, including the crowd and the cloud, enabled firms to compete in new ways across geographic domains.

Please share the video if you think professional services leaders in your network may be interested.

We already have excellent registrations for the conference in terms of both quality and quantity.

Having even more of the right people in the room will help us to engage the conversations and thought processes at the event that will shape the future of professional services.

However much of the big data discussion is about how to market better to customers, gathering data ABOUT them so companies can sell more to them.

This seems to me to be the wrong way to think about it. Big data should be used to CREATE VALUE FOR CUSTOMERS. From that good things will flow to everyone, including of course attracting the most customers.

In the mid-1990s I became involved in knowledge management. I thought the ideas were immensely valuable, but I could see absolutely no consideration of the customer. So I wrote my first book, Developing Knowledge-Based Client Relationships, to frame knowledge as primarily about creating value WITH the customer.

Today it is back to the same story. In big data there are fantastic tools and capabilities, that are being applied to selling to rather than creating value for the customer.

In a number of my recent executive briefings for clients the financial services sector I’ve used the example of Simple, the startup online bank recently acquired by BBVA for US$117 million. It uses rich customer data to provide insights for the customer on their financial situation, including “Safe-to-Spend” recommendations.

Another example from the financial sector is driver feedback apps. Insurance companies such as State Farm in the US and Ingenie in the UK provide mobile phone apps that use the accelerometers and other sensors in phones to provide detailed analysis and recommendations on driving style. This is of course useful not only to drivers, but to the parents of newly-minted drivers. The data is valuable to the company, but the way it can be captured is to provide this in a useful format to customers.

Across all industry sectors the first question in thinking about big data should be: “How can we use data to create value FOR our customers?”

Answering that will drive competitive differentation and value for everyone.