For those of you who knew me 10 years ago, you’d be very surprised to hear that I’m getting married in 1 month! In 2016, I was a staunch environmentalist and feminist, a nonbeliever in the old tradition of marriage, tying a woman to a man. I didn’t believe in having a family; I thought it was a waste of my life and that I’d get more satisfaction out of a high-powered career. I believed the feminist rhetoric that freedom, autonomy, and self-determination would be the greatest things that could be achieved in life. 

Surprising to myself and to many who knew me then, most of all my parents, is that I no longer hold this belief (at least to the same degree). I still believe Women should be empowered, I still believe in self-determination, I still believe in fulfilling potential, etc. But there are a few things that made me change my stubborn mind, especially about marriage.

Not getting married is living a life of “optionality.” You haven’t given a life commitment to your partner, but you could still be life partners, you just “haven’t decided yet.” You never know - you might drift apart, have a change in life trajectory, and want different things. This is what I used to think, but I now see this as a scapegoat, avoiding the decision. As soon as you make the decision, it’s pretty liberating, it’s saying, “I accept you and all your quirks and brilliances, I’m committing to a life making our lives better every day.” There’s no more perpetual wondering “should we or shouldn’t we continue our lives together.” I’m going to be saying that in front of my closest family and friends, and so will my partner. A commitment device surrounded by our biggest supporters, what wouldn’t give a bigger chance of success?

I also think there’s something beautiful about commitment while you’ve both not succeeded, you’ve still got bold ambitions, and you help each other reach those bold ambitions together. There’s something humbling and deeply wonderful about loving someone even when you are nomadic and without a successful career, maybe this is just the romantic in me.

I think I was scared of staying unhappily married and not feeling I could leave, but I don’t believe this justified the torment of being half in and half out (for both parties). My partner and I started out in a very “we’re exclusive and committed to each other, but we don’t think marriage is necessary because we should choose to be with each other every day” train of thought. Though this is a nice adage in principle, in practice, it is much harder. There’s still this nagging thing in the back of your mind, when things get tough, that maybe it’s not worthwhile. But that’s a normal part of any relationship; things will get hard, and you’ve got to work through it. But who is the person you want to commit to and watch them grow through life with? Who do you want to spend every day with? Who do you feel at home with? What is worse - is to live a life avoiding failure and never taking any action, or just committing to things and giving things a go. 

Funny enough, in our relationship, I feel like a lot of our friends and family have been a glue that has kept us going when things have been rough, and this is one of the things that made me realise the importance of marriage. You are bringing all these people to surround you and watch you commit, and they also become a part of this commitment, to support you to work through things, to support each other, and build a happy life and community. I think the adage “it takes a village to raise a child” also applies to a relationship, to an individual, etc, and now we don’t live in villages with a mega online world, for us, the village will be those at the wedding. In fact we met through a mutual friend of ours, and we’ve both lived with each of our respective families for extended periods. Don’t underestimate the value of the people around you - they are the glue.

I know we aren’t married just yet - still 1 month away, but our engagement has been some of the most consistently upbeat, jovial parts of our relationship yet, and yes, there have been many over our +8 years together, but this has been the most consistently giddy, full of laughter, kindness, ease, simplicity. We’ve worked through many things together over the years, and we are now at a place where we know and accept each other's weaknesses, and there’s a playfulness and safety in how we interact. It makes me wish we’d gotten married 5 years ago! 

I used to think everything unconventional was good, that being conventional was boring and unnecessary, and uncreative. But I’ve largely changed my thoughts on this. I think some things are conventional for a reason - because it works. Reminds me of Chesterton's fence - Marriage has existed for at least 4,000 to 5,000 years, so why am I to say it’s a terrible system and doesn’t work? Why am I 0.0001% special to say so? I used to also dislike marriage because to me it had deep roots in religion, but heck, it was around before Christianity. I think the things I don’t like about marriage are the things that I don’t like about bad relationships, more than it is about marriage itself. E.g. I would be unsatisfied with a partner who expected me to do everything and didn't contribute himself, who didn't value my opinions etc. Whereas I am satisfied when we both respect and appreciate each other, we both contribute to making each other's lives better every day.

I used to also think all optionality was good. I still struggle with it, because of how good it is to have choices. However, the problem is that you can end up spinning your wheels, splitting your time, thought, and energy across too many problems, and spreading yourself too thin. The modern-day encourages optionality with non-committal relationships, nomadic living (guilty), and the ability to change careers without having to do a 5-year training before being qualified for the job (I’m thinking about you French bakers - but aren't the pastries phenomenal). But with optionality you risk losing compounding value, you switch location every few months and you don’t build close relationships with those around you, you don’t know your neighbours and so you feel you can’t ask them for a lemon because you’ve forgotten to get one from the shop and you need it in your recipe, you can’t ask your neighbours to babysit your kids in an emergency because you need to go the hospital. You keep switching jobs, so you don’t gain expert knowledge in one area, you end up just being mediocre at everything. 

It took me 10 years to figure this all out, but I think my 30s are going to have a lot more commitment and conventionality than 20-year-old Flo could ever imagine, but just as much creativity and curiosity. I’m still unconventional at heart, but I hope to be more intentional in where I choose to be unconventional, rather than just choosing to be unconventional about everything for the sake of it. I still believe in being curious, exploring the world, and understanding opinions, but I hope to be more focused in my time and energy, and here’s to my first big commitment, marrying the love of my life!

We (Flo & Rudolf) spent a month fleshing out the idea of an insurance-for-AI company. We talked to 15 people in the insurance industry, and did 20 customer interviews. We decided not to continue, but we think it’s still a very promising idea and that maybe someone else should do this. This post describes our findings.

The idea

Theory of change

To reduce AI risks, it would be good if we understood risks well, and if some organisation existed that could incentivise the use of safer AI practices. An insurance company that sells insurance policies for AI use cases has a financial incentive to understand concrete AI risks & harms well, because this feeds into its pricing. This company would also be incentivised to encourage companies to adopt safer AI practices, and could incentivise this by offering lower premiums in return. Like many cyber-insurance companies, it could also provide more general advice & consulting on AI-related risk reduction.

Concrete path

TL;DR: Currently, professionals (e.g. lawyers) have professional indemnity (PI) insurance. Right now, most AI tools involve the human being in the loop. But eventually, the AI will do the work end-to-end, and then the AI will be the one whose mistakes need to be insured. Currently, this insurance does not exist. We would start with law, but then expand to all other forms of professional indemnity insurance (i.e. insurance against harms caused by a professional’s mistakes or malpractice in their work).

Frontier labs are not good customers for insurance, since their size means they mostly do not need external insurance, and have a big information advantage in understanding the risk.

Instead, we would target companies using LLMs (e.g. large companies that use specific potentially-risky AI workflows internally), or companies building LLM products for a specific industry.

We focused on the latter, since startups are easier to sell to. Specifically, we wanted a case where:

• LLMs were being used in a high-stakes industry like medicine or law

• there were startups building LLM products in this industry

• there is some reason why the AI might cause legal liability, for example:

• the LLM tools are sufficiently automating the work that the liability is plausibly on them rather than the humans

• AI exceptions in existing insurance policies exist (or will soon exist)

The best example we found was legal LLM tools. Law involves important decisions and large amounts of money, and lawyers can be found liable in legal malpractice lawsuits. LLMs are close to being able to do much legal work end-to-end; in particular, if the work is not checked by a human before being shipped, it is uncertain if existing professional indemnity (PI) insurance applies. People who work in law and law tech are also, naturally, very liability-aware.

Therefore, our plan was:

• Become a managing general agent (MGA), a type of insurance company that does not pay claims out of its own capital (but instead finds a reinsurer to agree to pay them, and earns a cut of the premiums).

• Design PI policies for AI legal work, and sell these policies to legal AI startups (to help them sell to their law firm customers), or directly to law firms buying end-to-end legal AI tools.

• As more and more legal work is done end-to-end by AI, more and more of the legal PI insurance market is AI insurance policies.

• As AI advances and AI insurance issues become relevant in other industries, expand to those industries (e.g. medicine, finance, etc.).

• Eventually, most of the world’s professional indemnity insurance market (on the order of $10B-100B/year) has switched from insuring against human mistakes to insuring against AI mistakes.

• Along the way, provide consulting services for countless businesses switching to AI-based work, helping them reduce the chance of harm, and incentivising this with lowered premiums.

• Stay up-to-date on concrete AI risks, likely funding research focused on measuring it in the real-world. Also, for example the claims history that results will be an automatic, high-quality database of AI harms.

We thought this could be a multi-billion dollar company, a natural for-profit home for concrete AI risk research, and a reducer of existential risk from AI. 

How insurance works

Why insurance

Insurance is about cash-flow management. Sometimes, a low-probability accident happens, that either bankrupts the company or just puts an annoying dent in their accounting. If the expected value of such losses exceeded the company’s ability to pay, (competent) insurers would not be willing to sell the policy. But if it’s less, the company can benefit (by e.g. better weathering sudden shocks) by having insurance, and the insurer can make a profit in expectation.

Another way of describing the core function of insurance is as arbitrage between differently-locally-concave utility functions. Assume the policyholder faces a gamble between a good outcome \( x_2 \) and a bad outcome \(x_1 \). If the policyholder’s utility function is more concave than the insurer’s (for example, if it’s the red line below, while the insurer’s is just linear), then the policyholder cares less than the insurer about the difference between x2 and x2 minus the insurance premium rate r. If the policyholder's utility function is f, the policyholder’s utility change given insurance is a rise from \( pf(x_1)+(1−p)f(x_2) \) (where p is the chance of the bad outcome) to just always being \(f(x2−r)\) , while the insurer is exposed to upside risk \((1−p)r\) (green rectangle) and downside risk \(p(x_2−x_1−r)\) (red rectangle). The flatter the policyholder’s utility function is around the \(x_2-r\) to \(x_2\) region compared to the region before that, the better this trade can be.

There are other benefits of insurance too:

• Contracts sometimes require one party to have insurance, usually so that the other party knows they can sue and recover damages if necessary.

• Reputational benefits (“you can trust us because we managed to get an insurer to!”)

• Infrastructure for handling claims/losses (insurance companies may have non-financial advantages in processing or evaluating claims, so it can be good to contract such functions out)

• Credible loss prevention advice (“we’re not just a consultancy - our money is also on the line here, and therefore trust us when we tell you to do X”)

• Credible pricing of harms (similar to the above)

Capital efficiency in insurance comes from scale

Insurance depends on scale because of the central limit theorem. As a simplified example, let’s say you’re an insurance company selling flood insurance. Assume you sell flood insurance policies for £100, and there’s a 10% chance that a flood happens and you need to pay out £600 (so the expected risk is £60). In expectation, you make £40 per policy. However, if you sell 10 policies, and suddenly 2 or more of them trigger, you’ve made revenue of £1000 but a loss of £1200 and you’re bankrupt (assume you don’t have cash reserves). There’s a ~26% chance of this happening  \(∑1(i=0)[binomial pdfp=0.1,n=10(i)]≈0.26\). However, if you sold 100 policies, the probability that 2 or more in every set of 10 triggers is only 0.2% \(∑19(i=0)[binomial pdfp=0.1,n=100(i)]≈0.002\).

Therefore, even if the expected loss is the same between two insurers, assuming the same risk tolerance (a red area), the insurer with more policies is exposed to less variance in expected loss per policy and can set a lower price:

Therefore, the way you achieve good capital efficiency in insurance is to sell many policies across many different types of risk (ideally uncorrelated ones - so not just flood policies, which might all trigger in the case of rising seas, but diverse types of risks). 

However, in practice the balance sheets and the actual selling of the insurance products are not very tightly coupled in the insurance industry. You do not have to be a company with huge policy scale, you just have to work with such a company (for example, by becoming an MGA - see below).

The insurance industry is complicated and allows for startups

The straightforward consequence of the above incentive is that you get monopoly effects in insurance, where one firm achieves the largest balance sheet and then beats everyone else on price (or pushes everyone else into a daredevil game where they’re more likely to blow up than the big one is).

To some extent, this has happened. However, there’s a separate dynamic too. To quote from a blog post by Felix Stocker:

Most businesses, especially SMEs, buy their insurance from someone they know personally. Because it’s low on the list of priorities, but important to get right, the CEOs or CFOs responsible stick with people they trust - brokers that can answer questions, and be relied upon to bail them out in a tough spot. Personalities, not brands, are key. Because of this, the concept that best explains the structure of the insurance broking market is Dunbar’s Number - the idea that any one person can only hold a limited number of personal relationships. So each broker has up to about a hundred relationships - but no more than that. And since the end-customer relationship is owned by an individual broker, then the challenge becomes aggregating brokers, rather than the customers themselves.

There are also many ways to bundle and unbundle the different components of insurance. Felix Stocker writes about this here. In brief:

A common starting point for insurance startups is to be MGAs (managing general agents), that handle pricing (and, sometimes, distribution - i.e. selling), but are backed by the balance sheet of a reinsurer. This would’ve been our approach too.

General liability exceptions are key for new insurance products

There isn’t an insurance product for every niche risk, because companies often hold general liability insurance that covers basically anything.

However, general liability insurance often comes with exceptions. For example, professional liability (also called errors & omissions) is often left to a separate policy, and terrorism & war -related harms are excluded. Also, complicated new risks like cybersecurity have increasingly tended to get exceptions, and be left to specialised cyber policies.

Based on talking to insurance industry experts, we expect AI-related exceptions to general liability and professional indemnity insurance to be coming. In the meantime, the need for them seems somewhat complex and subtle.

Our ideas for pricing risk

We were loosely inspired by Epoch’s “Direct Approach” for forecasting human-level AI. Specifically, we’d make an argument of the form: if we can show that the outputs of the human and the AI are indistinguishable regarding some property (e.g. mistake rate as assessed by humans), then we should treat them as practically the same regarding related properties (e.g. the probability of causing a malpractice lawsuit).

Specifically, our guess for how to price legal professional indemnity insurance for an AI model/scaffold is:

• Collect a bunch of legal documents created by the AI, and comparable documents created by the human.

• Hire legal experts to assess the potentially-claim-causing mistake rate in the AIs’ and the humans’ work. (Note: legal experts cost over ~$100 per hour, so this would be fairly expensive)

• Apply a fudge factor to the number of AI mistakes caught, on the assumption that humans are better (having had more practice) at catching human mistakes, and to account for unknown unknowns.

• If the fudge factor times the AI claim-relevant mistake rate is lower than the humans’, offer the AI model’s outputs PI cover with the same rates as the relevant human PI for the same firm. If it’s higher, then either don’t offer it, or offer it at a fairly steep additional price, and probably with lower cover. Basically - price in the risk.

We would likely only go through this process if we had first done a more checkbox-style check of the AI workflow, including:

• Whether reasonable evaluations have been run

• Whether the AI’s access to protected information is reasonable (e.g. probably the AI should have zero access to customer X’s info while doing work for customer Y)

• The extent to which humans are in the loop or could intervene

• Susceptibility to prompt injection attacks (e.g. we might require companies to use something like Lakera Guard)

• Vulnerability to model updates (e.g. if OpenAI drops a new version of GPT-4 that is worse at specific things, does your workflow switch to it immediately without checks, or have you hardcoded the GPT version number?)

We expect there is room for fancier technical solutions to evaluate risk probabilities. However, we caution that the size of a loss is almost entirely not about the AI, but instead about the context of the case: what legal work was being done, what harm the legal error resulted in in the real world, what the judge’s mood was when they were deciding the case (if it went to court), and so on. Even the probability of risk is only partly about the AI; it also depends, for example, on whether the client who received the bad advice decides to sue in the first place. This is why the core of our approach is side-stepping the problem of evaluating legal malpractice harms from scratch, and instead creating an argument for why the AI lawyer does not have more risk (or has some specific factor more risk) than the human lawyer. We effectively want to import the human lawyer claims history used for existing insurance pricing to the AI case.

We did not prioritise thinking of technical approaches to risk evaluation, because we thought much more of the risk was on the market size (thinking the opposite is perhaps the most common failure mode of tech-focused entrepreneurs). However, having a “magical” tech demo would probably be a good way to get your foot in the door. Showing you could’ve accurately predicted past failure rates might be the type of evidence that insurers care a lot about.

Notes on professional indemnity insurance for law

• There isn’t a set of “cookie-cutter” templates that most claims fall into; it’s a diverse set.

• The split is roughly (taking a rough average over several sources):

  • 50% substantive claims (e.g. filing a motion in the wrong court, failing to raise a critical defense point, misapplying rules of evidence)

  • 30% administrative errors (e.g. missing a statute of limitations or court-ordered discovery deadline, forgetting to renew a client’s trademark, typos, filing documents for the wrong client, losing important documents, sending an email with confidential info to the wrong person, leaving sensitive documents visible on a desk, failing to properly redact info in public filings)

    • in particular, missing deadlines is a common one

    • many of these (e.g. missing deadlines, typos, and losing documents) seem like ones where AIs would be much lower

  • 10% client relation issues (e.g. settling a case without client approval, failing to disclose a past representation of an adverse party or a gift, not keeping the client informed, not explaining risks, failing to return calls or emails)

    • apart from some instruction-following or bad explanation -related harms, most of these seem far less applicable to AIs

  • 10% intentional wrongs (e.g. overbilling for work not performed, submitting false evidence, severe COI, selling information to a competitor, using confidentional info to benefit another client)

    • again, most of these are far less applicable to AIs

It seems reasonable that AI legal PI would therefore be even more tilted towards the substantive errors category than human lawyer PI.

Relevant Stakeholders

Munich Re has an AI insurance team. Here is their thinking on the state of the AI insurance market.

Orbital Witness, which accelerates legal due diligence in real estate, built their own custom AI insurance product with a real estate insurer called First Title. 

We won’t share details about the situations of specific startups that we talked to.

Findings

Customer demand

Common reasons for customers not needing insurance for their AI use cases included:

• not working in a regulated or high-stakes domain

• there is a human in the loop all the time, so the AI is just a tool and existing PI will probably cover it - for now

We did find several AI-for-law companies that did want an AI-specific insurance policy. Notably, one of them had seen the need to build their own custom insurance product, working with a specialist insurer in their area (real estate law). Several complained about not finding an off-the-shelf solution, and were willing to pay immediately for an insurance policy that addressed their problem. However, in at least one case this was more of a formality required by a contract.

One theme in many customer conversations was that being financially compensated by insurance is not sufficient to make up for damages, because the real damage is to the reputation of the company in question. This is much harder to insure against. Insurance could help indirectly here (e.g. the fact that you were able to get insurance for your product is some evidence that whatever you’re selling doesn’t blow up too often).

Another theme in many customer conversations that people just aren’t thinking that much about AI risks or harms yet. We think this is a consequence of AIs not being deployed in high-stakes use cases. Many organisations are conservative in their applications of AI and choose to start implementing in low stakes domains, such as internal products and answering simple FAQ questions. Nobody wants to be the first company to have AI publicly fail. 

Findings about insurance

The insurance industry is complicated. The legal industry is also complicated. Neither of us had any background in either. The lack of knowledge was generally fixable (thanks to Claude in particular), but the lack of relevant connections significantly slowed our momentum. Early-stage startup exploration is mostly driven by talking to potential customers. This was helped by the fact that we were mostly talking to AI product companies in these spaces, but still was slow going compared to both of our previous experiences getting customer interviews.

An MGA requires a reinsurer, and this takes a lot of time. This meant that, to get started, we would’ve needed not just customers, but a reinsurer. We did not find a reinsurer who was willing to work with us. If we had kept doing this, we would’ve talked to more reinsurers (perhaps starting with Allianz, who have previously reinsured a drone insurance product). The normal time for a new insuretech startup to get a reinsurer is on the order of 6-24 months.

The insurance industry moves slowly and carefully. This makes sense, since insurance companies that make rash and risky moves probably wouldn’t exist for very long. But it is still a very important cultural difference to, for example, the tech world.

Insurance is overwhelmingly about inductive, not deductive, evidence. Claims histories are the gold standard of evidence in the insurance world. If you don’t have a claims history, you will have a hard time.

Insurance is often reactive, and changes are driven by new types of big losses. The industry perks up and starts paying attention and figuring out how to deal with a given risk when a big loss happens related to that risk. In particular, once a big loss happens, lots of insurance actors will want to know how exposed they are to that type of risk, and either reduce exposure to that risk or make money by insuring against it.

Insurance for AI might only become something reinsurers care a lot about after a big event happens and causes harm.

The insurance industry is financially very large but does not have high valuations. Many insurance companies have extremely large revenues, but insurance companies are often valued at only a 1-3x multiple of their revenues (compared to 20x for tech companies). Allianz makes more revenue than Meta and has almost 10x the assets, but as of writing is valued at 1/10th of Meta.

Also, some vague things about insurance that struck us:

• Insurance is fundamentally on the financialization side of the financialization-v-building axis. Financialization is necessary in a complex world, but it’s perhaps harder to feel the hacker building ethos when that’s what you’re doing.

• At the claims stage, insurance is fundamentally adversarial: the claimant wants money, and the company is incentivised to not pay.

• Reducing someone’s exposure to risk can lead to them taking more risk.

Cruxes

Our rough standard was that if we saw a path to getting a reinsurer onboard in clearly less than 6 months, we would start this company. We had several reasons for wanting to move fast:

• Moving fast is the key to building big impactful things.

• We felt our opportunity cost was high; in, say, a year of talking to reinsurers before being able to sign our first customer, we could’ve gotten far building something impactful that isn’t an insurance product.

• The rate of AI progress is high enough that things that move slowly might not have time to matter.

We also were bottlenecked by not having insurance industry connections. Insurance, as mentioned above, is a very network-based field. It is true that many insuretech founders do not have insurance backgrounds, but it is still critical that some industry expert is involved very early on in advisory capacity, and probably the first hire needs to be someone with deep insurance connections.

In summary, we think that insurance for AI is a great idea for a team that is less impatient, and has either more insurance connections or great willingness to find networks in insurance.

Former AIG (American Insurance Group) CEO Hank Greenberg once said: “All I want in life is an unfair advantage”. Someone who - unlike us - does have an unfair advantage in insurance may be able to run with this idea, build a great company, and reduce AI risk.

Alternative approaches

Labs / Big Tech as reinsurers

Insurance requires a large balance sheet to pay out claims from. The standard way to solve this is with a reinsurer. However, who else has a lot of capital, and (unlike reinsurers) a specific interest in AI? Foundation model labs (FMLs) and their Big Tech backers.

This could also simultaneously align FML incentives. Incentive-wise, the natural place to park AI risk is at the AI companies themselves. There are two levels of this:

• When there are claims, an FML is involved in paying them out (directly or indirectly)

• When there are claims, all FMLs are at least partly on the hook, because the claims are at least partly paid out from a shared pool of capital that all FMLs are involved in. (This creates an incentive among FMLs to care about the safety of the others, making safety tech sharing more likely, and making it easier for the industry to negotiate a slowdown if needed.)

Why might FMLs want to do this? It spreads the risk of things going wrong and incentivises finding errors in other companies’ models early on. It could increase public trust in AI as a whole, which will make adoption easier. In particular, most people don’t know the difference between the top FMLs and so see them as “AI companies.” If one AI company causes a large harm, the public is likely to associate it with AI companies in general. It also seems good, incentive-wise, that the companies driving a technology are the ones who are involved in insuring the risk.

Why might FMLs not want to do this? There are lots of incredibly good reasons. 

• There are many better uses of capital than parking it in some risk-free place where it can only be used as backing for an insurance product. This includes paying the employee salaries and compute costs that may enable these labs to build AGI and take over the entire economy and then the world - potentially a much higher-margin business than insurance.

• (If going for the more ambitious version, where all FMLs participate in paying out claims:) By increasing the trust in all FMLs, pooled insurance might reduce a given FML’s competitive edge. For example, that OpenAI and Microsoft offer their Copyright Shield product is a specific advantage for them.

• If it looks like insurance, or smells like insurance, or quacks like insurance, a horde of regulatory requirements immediately descend upon you. This makes a lot of sense; insurance is a very natural “picking up pennies in front of the steam roller” business. But FMLs understandably would prefer to not deal with this.

• It’s not their core competency, and companies generally do better when they stick to their core competency.

To try to get around these issues, we explored options for FML backing, including:

• FMLs / Big Tech simply partner with us, giving us credibility when we go to reinsurers.

• FMLs / Big Tech become reinsurers.

• FMLs / Big Tech create a captive insurance body. Captive insurance is when companies in a given industry get together to pool risks, allowing them some regulatory benefits over a normal reinsurer that deals with arbitrary risks, but also some limitations.

• FMLs / Big Tech enter into some complicated deals with reinsurers that makes it a better deal for them.

We soon reached the point where, with Claude-3.5 serving as our legal team, we were doodling increasingly complicated legal structure diagrams on whiteboards. Some of them were starting to look vaguely pyramid-shaped. That was a good place to leave it.

Selling a risk model rather than an insurance policy

Why not leave the entire insurance industry to the existing insurance companies, and focus on what we really care about: modelling concrete AI risks?

An example of a company that sells risk models to insurance companies is RMS (now part of Moody’s, after changing ownership a few times). They were started in the late 1980s and specialised in natural catastrophe (“nat cat”) risk modelling. They had a technical moat: they were better at modelling things like synthetic hurricane populations than others.

The main disadvantage of such a route is that selling to insurance companies is very painful: they have slow procurement processes, mostly don’t understand technical details, and generally need to see a long track record of correct predictions before they buy. Venture capitalists are also unlikely to be interested in supporting such a company, since their growth rates are usually not stratospheric. For example, RMS was sold to Moody’s in 2021 for $2 billion, but only after almost 30 years in existence, and after already having been sold to Daily Mail along the way.

Might there be a market apart from insurance companies for a risk modelling product? Maybe, but this is unlikely. For natural catastrophe risks at least, insurance companies dominate risk modelling demand by sheer volume - they want updates all the time, whereas governments might want an update for planning purposes once every decade. Given how fast AI changes, though, there may be more actors who have a high rate of demand for risk models and updates on them.

Should AI evaluation / auditing orgs do this?

We haven’t thought about this much, but an org with an AI evaluation/auditing background might be well-placed to move into the insurance (or risk-modelling) space.

Acknowledgements

We’d like to thank Felix Stocker for lots of great advice on how things work in insuretech, Ed Leon Klinger for sharing his insurtech journey, Robert Muir-Woods for a very helpful chat about RMS, Otto Beyer for a valuable initial conversation about the insurance space, Jawad Koradia for helping us get initial momentum and introductions, Will Urquhart for talking with us about underwriting niche risks, the team at Entrepreneur First (in particular Kitty Mayo, Dominik Diak, and Jack Wiseman) for hosting much of our exploration and offering advice & introductions, and various people scattered across AI startups and the insurance industry for taking time to meet with us.

This post was a collaboration with Rudolf Laine.

Reasons to make the positive case

Everyone who starts thinking about AI starts thinking big. Alan Turing predicted that machine intelligence would make humanity appear feeble in comparison. I. J. Good said that AI is the last invention that humanity ever needs to invent.

The AI safety movement started from Eliezer Yudkowsky and others on the SL4 mailing list discussing (and aiming for) an intelligence explosion and colonizing the universe. However, as the promise of AI has drawn nearer, visions for AI upsides have paradoxically shrunk. Within the field of AI safety, this is due to a combination of the “doomers” believing in very high existential risk and therefore focusing on trying to avoid imminent human extinction rather than achieving the upside, people working on policy not talking about sci-fi upsides to look less weird, and recent progress in AI driving the focus towards concrete machine learning research rather than aspirational visions of the future.

Both DeepMind and OpenAI were explicitly founded as moonshot AGI projects (“solve intelligence, and then use that to solve everything else” in the words of Demis Hassabis). Now DeepMind - sorry, Google DeepMind - has been eaten by the corporate machinery of Alphabet, and OpenAI is increasingly captured by profit and product considerations.

The torch of AI techno-optimism has moved on the e/acc movement. Their core message is correct: growth, innovation, and energy are very important, and almost no one puts enough emphasis on them. However, their claims to take radical futures seriously are belied by the fact that their visions of the future seem to stop at GenAI unicorns. They also seem to take the general usefulness of innovation not as just a robust trend, but as a law of nature, and so are remarkably incurious about the possibility of important exceptions. Their deeper ideology is in parts incoherent and inhuman. Instead of centering human well-being, they worship the “thermodynamic will of the universe”. “You cannot stop the acceleration”, argues their figurehead, so “[y]ou might as well embrace it” - hardly an inspiring humanist rallying cry.

In this piece, we want to paint a picture of the possible benefits of AI, without ignoring the risks or shying away from radical visions. Why not dream about the future you hope for? It’s important to consider the future you want rather than just the future you don’t. Otherwise, you might create your own unfortunate destiny. In the Greek myth about Oedipus, he was prophesied to kill his father, so his father ordered him to be killed, but he wasn’t and ended up being adopted. Years later he crossed his father on the road in his travels and killed him, as he had no idea who his father was. Oedipus’ father focusing on the bad path might have made the prophecy happen. If Oedipus' father hadn’t ordered him to be killed, he would have known who his father was and likely wouldn’t have killed him. 

When thinking about AI, if we only focus on the catastrophic future, we may cause it to become true by causing an increase in attention on this topic. Sam Altman, who is leading the way in AI capabilities, claimed to have gotten interested from arch-doomer Eliezer Yudkowsky. We may also neglect progress towards positive AI developments; some people think that even direct AI alignment research should not be published because it might speed up the creation of unaligned AI.

With modern AI, we might even get a very direct “self-fulfilling prophecy” effect: current AIs increasingly know that they are AIs, and make predictions about how to act based on their training data which includes everything we write about AI.

Benefits of AI

Since we think a large focus of AI is on what could go wrong, let’s think through what could go well starting from what’s most tangible and close to the current usage of AI to what the more distant future could hold.

1. AI will do the mundane work

2. Lowering the costs of coordination

3. Spreading Intelligence

4. AI can create more technology

5. Increased technology, wealth and energy, correlate with life being good

6. All of the above, and the wealth it creates, could allow people to self-actualise more

Already, AI advances mean that Claude has beocme very useful, and programmers are faster and better. But below we’ll cast a look towards the bigger picture and where this could take us.

AI will do the mundane work

First, there’s a lot of mundane mental work that humans currently have to do. Dealing with admin work, filing taxes, coordinating parcel returns -- these are not the things you will fondly be reminiscing about as you lie on your deathbed. Software has reduced the pain of dealing with such things, but not perfectly. In the future, you should be able to deal with all administrative work by specifying what you want to get done to an AI, and being consulted on decision points or any ambiguities in your preferences. Many CEOs or executives have personal assistants; AIs will mean that everyone will have access to this. 

What about mundane physical work, like washing the dishes and cleaning the toilets? Currently, robotics is bad. But there is no known fundamental obstacle to having good robotics. It seems mainly downstream of a lot of engineering and a lot of data collection. AI can help with both of those. The household robots that we’ve been waiting for could finally become a reality.

Of course, it is unclear whether AIs will first have a comparative advantage against humans in mundane or meaningful work. We’re already seeing that AI models are making massive strides in making art, way before they’re managing our inboxes for us. It may be that there is a transitional period where robotics is lagging but AIs are smarter-than-human, where the main economic value of humans is their hands rather than their brains.

Lowering the cost of coordination

With AI agents being able to negotiate with other AI agents, the cost of coordination is likely to dramatically drop (see here for related discussion). Examples of coordination are agreements between multiple parties, or searching through a large pool of people to match buyers or sellers, or employees and employers. Searching through large sets of people, doing complex negotiations, and the monitoring and enforcement of agreements all take lots of human time. AI could reduce the cost and time taken by such work. In addition to efficiency gains, new opportunities for coordination will open up that would have previously been too expensive.

Small-scale coordination

To give an example of this on the small scale of two individuals, say you are trying to search for a new job. Normally you can’t review every single job posting ever, and employers can’t review every person in the world to see if they want to reach out. However, an AI could filter that for the individual and another AI for the business, and the two AIs could have detailed negotiations with each other to find the best possible match. 

Coordination as a scarce resource

A lot of the current economy is a coordination platform; that’s the main product of each of Google, Uber, Amazon, and Facebook. Reducing the cost of searching for matches and trades should unlock at least as much mundane benefits and economic value as the tech platforms have.

Increased coordination may also reduce the need to group people into roles, hierarchies, and stereotypes. Right now, we need to put people into rigid structures (e.g. large organisations with departments like “HR” or “R&D”, or specific roles like “doctor” or “developer”) when coordinating a large group of people. In addition to upholding standards and enabling specialisation of labour, another reason for this is that people need to be legible to unintelligent processes, like binning of applicants by profession, or the CEO using an org chart to find out who to ask about a problem, or someone trying to buy some type of service. Humans can reach a much higher level of nuance when dealing with their friends and immediate colleagues. The cheap intelligence we get from AI might let us deal with the same level of nuance with a larger group of people than humans can themselves track. This means people may be able to be more unique and differentiated, while still being able to interface with society.

Large-scale Coordination

On a larger scale, increased coordination will also impact geopolitics. Say there are two countries fighting over land or resources. Both countries could have AI agents to negotiate with the other AI agents to search the space of possible deals and find an optimal compromise for both. They could also simulate a vast number of war scenarios to figure out what would happen; much conflict is about two sides disagreeing about who would win and resolving the uncertainty through a real-world test. This relies on three key abilities: the ability to negotiate cheaply, the ability to simulate outcomes, and the ability to stick to and enforce contracts. AI is likely to help with all three. This could reduce the incentives for traditional war, in that no human lives are needed to be lost because the outcome is already known and we can negotiate straight from that. We also know exactly what we are and are not willing to trade off which means it’s easier to optimise for the best compromise for everyone.

Spreading the intelligence

AI lets us spread the benefits of being smart more widely.

The benefits of intelligence are large. For example, this study estimates that a 1 standard deviation increase in intelligence increases your odds of self-assessed happiness by 11%. Now, part of this gain comes from intelligence being a positional good: you benefit from having more intelligence at your disposal than others, for example in competing for a fixed set of places. However, intelligence also has absolute benefits, since it lets you make better choices. And AI means you can convert energy into intelligence. Much as physical machines let the weak gain some of the benefits of (even superhuman) strength, AI might allow all humans to enjoy some of the benefits of being smart.

Concretely, this could have two forms. The first is that you could have AI advisors increase your ability to make plans or decisions, in the same way that - hypothetically - even a near-senile president might still make decent decisions with the help of their smart advisors. With AI, everyone could have access to comparable expert advisors. The effect may be even more dramatic than human advisors: the AI might be superhumanly smart, the AI might be more verifiably smart (a big problem in selecting smart advisors is that it can be hard to tell who is actually smart, especially if you are not), and if AIs are aligned successfully there may be less to worry about in trusting it than in trusting potentially-scheming human advisors. 

The second is AI tutoring. Human 1-1 tutoring boosts educational outcomes by 2 standard deviations (2 standard deviations above average is often considered the cutoff for “giftedness”). If AI tutoring is as good, that’s a big deal.

AI is the ultimate meta-technology

AI is special because it automates intelligence, and intelligence is what you need to build technology, including AI, creating a feedback loop. Some other previous technologies have boosted other technologies; for example, the printing press massively helped the accumulation of knowledge that led to the invention of many other technologies. But we have not before had a technology that could itself directly advance other technology. Such AI has been called PASTA (Process for Automating Scientific and Technological Advancement).

Positive feedback loops - whether self-improving AIs, nuclear reactions, epidemics, or human cultural evolution - are very powerful, so you should be wary of risks from them. Similarly, it is currently at best extremely unclear whether AIs that improve themselves could be controlled with current technology. We should be very cautious in using AI systems to improve themselves.

In the long run, however, most of the value of AI will likely come from their effects on technological progress, much like the next industrial revolution. We can imagine AIs slashing the cost and increasing the speed of science in every field, curing diseases and making entire new veins of technology available, in the same way that steam engines made entirely new veins of coal accessible.

In particular, AIs help de-risk one of the largest current risks to future human progress. One model of the feedback loop behind humanity’s progress in the past few centuries is that people led to ideas led to wealth led to food led to more people.

However, greater wealth no longer translates into more people. The world population, which was exponentially growing for much of the 19th and 20th centuries, is likely to be in decline by the end of the 21st century. This is likely to have negative consequences for the rate of innovation, and as discussed in the next section, a decline in productivity would likely have a negative impact on human wellbeing. However, if AIs start driving innovation, then we have a new feedback loop: wealth leads to energy leads to more AIs leads to ideas leads to wealth.

As long as this feedback loop does not decouple from the human economy and instead continues benefitting humans, this could help progress continue long into the future.

Wealth and energy are good

If you want humans to be well-off, one of the easiest things to do is give them more wealth and more energy. GDP per capita (on a log scale) has a 0.79 correlation with life satisfaction, and per-capita energy use (again on a log scale) has a 0.74 correlation with life satisfaction. Increased wealth and energy correlate with life satisfaction, and we should expect these trends to continue.

Above: GDP per capita (x-axis), energy use (y-axis), and life satisfaction (colour scale) for 142 countries. There are no poor countries with high energy use, and no high energy use countries that are poor. There are no countries with high average life satisfaction that are not high in both energy use and average GDP per capita. The axes are logarithmic, but since economic growth is exponential, countries should be able to make progress at a constant rate along the axis. Data source: Our World In Data (here, here, and here).

(It is true that energy use and economic growth have been increasingly decoupling in rich countries, due to services being more of the economy, and efficiency gains in energy use. However, the latter is effectively increasing the amount of useful energy that can be used - e.g. say the amount of energy needed to cook one meal is now enough to cook two meals, which is effectively the same as gaining more energy. However, efficiency effects are fundamentally limited because there is a physical limit, and also if demand is elastic then efficiency gains lead to increased energy use, meaning it doesn’t help the environment either. Ultimately, if you want to do more things in the physical world, you need more energy).

A wealthy, energy-rich society has many material benefits: plentiful food, advanced medicine, high redistributive spending becomes feasible, and great choice and personal freedom through specialisation of labour and high spending power. A wealthy and energy-rich society also has some important subtler benefits. Poverty and resource constraints sharpen conflict. Economic growth is intimately linked to tolerance and liberalism, by weakening the cultural status and clout of zero-sum strategies like conflict and politicking.

One clear historic example of how increases in energy correlated with improved quality of life was in the industrial revolution, arguably the best and most important thing that ever happened. Before it, trends in human wellbeing seemed either stagnant, fluctuating, or very slow, and after it, all the variables for which we can find good long-term series that are related to human well-being shoot upwards.

Above: variables correlated with human well-being over time. Source:  Luke Muehlhauser

Therefore, it’s worth keeping in mind that boosting energy and wealth is good, actually. And the most powerful way to do that is through inventing new technologies that let us use energy to serve our needs.

The heart of the industrial revolution was replacing part of human manual labour with something cheaper and more powerful. AI that replaces large parts of human mental labour with something cheaper and more powerful should be expected to be similarly transformative. Whether it is a good or bad transformation seems more uncertain. We are lucky that industrialisation happened to make national power very tightly tied to having a large, educated, and prosperous middle class; it is unclear what is the winning strategy in an AI economy. We are also lucky that the powerful totalitarian states enabled by industrial technology have not triumphed so far, and they might get further boosts from AI. Automating mental labour also involves the automation of decision-making, and handing over decision-making to the machines is handing over power to machines, which is more risky than handing the manual labour to them. But if we can safely control our AI systems and engineer good incentives for the resulting society, we could get another leap in human welfare.

Self actualisation

Now say we’ve had a leap in innovation and energy through Transformative AI (TAI) and we’ve also reached a post scarcity world. What happens now? Humans have had all their basic needs met, most jobs are automated,  but what do people spend their time actually doing?

Maslow’s Hierarchy

Maslow’s hierachy of needs is a framework of understanding human needs and drivers for human behaviour. Maslow suggested that in most scenarios people need to mostly satisfy one level before being able to focus on higher-level needs. 

The top level of the hierachy is self-actualisation. The peak of human experience is something that few can currently reach - but maybe everyone could get there.

There is a possible path the world takes in which all humans can reach self-actualisation. With increases in technology & wealth, such as with TAI and a Universal Basic Income (UBI), we would be able to provide the basic needs of food, water, shelter, and clothing for all humans, enabling people to easily meet their basic needs. Humans can now spend more time on the things they want, for example moving up through Maslow’s hierarchy to focusing on increasing love and belonging, self-esteem and self-actualization.

Say you are in a post scarcity world, what would you do if you didn’t have to work?

Would you be spending time with loved ones, engaging in social activities that provide a sense of connection and belonging, self-esteem? Would it be honing your craft and becoming an expert in a particular field? Or would you spend the whole time scrolling on your phone?

Say hypothetically a wealthy billionaire gave you a grant to work on anything you wanted, would you be happy with having the complete freedom to spend your time as you wished?

Often people assume that others will be unhappy with this world, but would you? There is a cognitive bias where people tend to judge themselves as happier than their peers, which could nudge you to think people would be less happy in this world, even if you would enjoy this. 

In this post-scarcity world, humans could spend more time on creative pursuits such as art, music, and any other hobbies – not with the goal of making money, but to reach self-actualisation. 

With AI being better than humans in every dimension, AI can produce the best art in the world, but there is intrinsic value in honing your craft, improving at art or expressing your feelings through it, in and of itself. The vast majority of art is not created to be the best art in the world but for the journey itself. A child that paints a finger painting and the parent who puts it on the wall does not think “my child’s art is better than Van Gogh’s”. Instead, they feel a sense of excitement about the progress their child has made and the creative expression the child has produced. 

Another example is the Olympic games. Nobody needs to win the olympic games to survive, but it lets people express pride in their country, hone their craft, attain status, and so on. But the actual task is just a game, a social construct. More and more tasks will look like social constructs and games we create to challenge each other.

Examples of post-scarcity scenes 

Since this is quite theoretical, let's consider examples where we’ve had “post-scarcity” microcosms to explore. 

The French Bourgeoisie 

The French leisure class, or bourgeoisie, were a class of wealthy elite that emerged in 16th century France. Many had enough money to pursue endeavours like refining their taste in arts and culture. Salon culture was a cornerstone of bourgeoisie social life. Gatherings featuring discussions on literature, art, politics and philosophy. 

Upper Class in the Victorian Era

The upper class in the Victorian era enjoyed a variety of leisure activities that reflected their wealth, status and values. They attended social events and balls, fox hunting and other sports, theater and opera, art and literature, travel, tea parties and social visits, gardening and horticulture, charitable work and philanthropy. Several undertook serious pursuits in science or art. 

Burning Man

Burning Man is an annual festival where people take all the basic things you need with you for a week of living in the desert:food, water, shelter. People have a week to create a new community or city that is a temporary microcosm of a post-scarcity world. They pursue artistic endeavours and creative expression, music, dance and connecting with others. People often talk about Burning Man events being some of the best experiences of their lives. 

Successful Startup Founders in The Bay Area

In San Francisco, there is a crossover with hippie culture and tech, and many people with excess wealth and resources, resulting in many looking for more in life. They try to reach self actualisation, by pursuing many arts and creative pursuits. Hippie movements often encourage communal living, and a sense of connection with those around you. Many may raise eyebrows at the lifestyles of some such people, but it’s hard to claim that it’s a fundamentally bad existence.

More pessimistic views about humans?

It is true that not all cultural tendencies in a post-scarcity world would be positive. In particular, humans have a remarkable ability to have extremely tough and all-consuming social status games, seemingly especially in environments where other needs are met. See for example this book review about the cut-throat social scene of upper-class Manhattan women or this one about the bland sameness and wastefulness of nightlife, or this book review that ends up concluding that the trajectory of human social evolution is one long arc from prehistoric gossip traps to internet gossip traps, with liberal institutions just a passing phase.

But the liberal humanist attitude here is to let humans be humans. Yes, they will have petty dramas and competitions, but if that is what they want, who is to tell them no? And they will also have joy and love.

Would a post-scarcity world have meaning? Adversity is one of the greatest sources of meaning. Consider D-Day, when hundreds of thousands of soldiers got together to charge up a beach under machine-gun fire to liberate a continent from Nazi rule. Or consider a poor parent of four working three jobs to make ends meet. There are few greater sources of meaning. But adversity can be meaningful while involving less suffering and loss. A good future will be shallower, in a sense, but that is a good thing.

Finally, it is unclear if we would get a happy world, even if we had the technology for post-scarcity, because of politics and conflict. We will discuss this later.

Radical improvements

AI might also help with radical but necessary improvements to the human condition.

People die. It is a moral tragedy when people are forced to die against their will, as happens to over 50 million people per year. Medicine is making progress against many causes of death and disability; in the limit it can cure all of them. We should reach that limit as fast as possible, and AI can likely help accelerate the research and deployment of solutions.

One of the greatest inequalities in the world is inequality in intelligence. Some people struggle to perform in simple jobs, while others (well, at least one) are John von Neumann. In the short term, AI might help by making cognitively demanding tasks more accessible to people through AI tutors and AI copilots. In the longer term, AI might help us enhance human intelligence, through brain-AI integration or new medical technology.

Reasons to worry

Though there are many potential upsides for AI and AGI as argued in this post, that doesn’t mean there aren’t risks. 

The plausible risks of AI go all the way to human extinction, meaning this shouldn’t be taken lightly. Since this piece is focused on the upside risk, not the downside risk, we will not argue this point in depth, but it is worth revisiting briefly.

Existential risk from AI is a serious concern

It is intuitive that AI is risky.

First, creating something smarter, faster, and more capable than humans is obviously risky, since you need to very precisely either control it (i.e. stop it from doing things you don’t like) or align it (i.e. make it always try to do what you would want it to do). Both the control and alignment problem for AIs still have unsolved technical challenges. And that’s assuming that AI is in the right hands.

Second, even if the AIs remain in our control, they are likely to be as transformative as the industrial revolution. Eighteenth-century European monarchs would’ve found it hard to imagine how the steam engine could challenge their power, but the social changes that were in part a result of them eventually wrested all their powers away. In the modern world, a lot of power depends on large educated workforces of humans, whereas sufficiently strong AGI might decorrelate power and humans, decreasing the incentive to have people be educated and prosperous - or to have people around at all.

Apart from object-level arguments, consider too the seriousness with which the AI doomsday is discussed. Many top researchers and all top AI lab CEOs have signed a statement saying “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war”. Nuclear war and pandemics are the only other cases where similarly serious predictions have been made by a similarly serious set of people (though arguably climate change is close: the science on the effects is more established and certain, but while catastrophe is more likely, literal human extinction from it is much less likely).

Side-effects of non-existentially-bad AI might be large

Consider the internet, a widely-successful technology with a lot of benefits. There are credible claims that the internet is responsible for harms ranging from massively increased depression rates among teenagers to political polarisation to widespread productivity loss through addiction and distraction.

In the same way, the success of AI might lead to bad side effects, even if all the existential risks are avoided.

For example, AI could replace human connection. Human friends and partners might increasingly be replaced with AIs. However bad it was in other ways, at least on pre-AI social media you at least interacted with humans (or simple algorithms), but with AIs it’s possible to have what looks like deep emotional relationships. Just look at the Replika subreddit from a year ago when they changed the algorithm to only allow “PG-rated interactions”. Many users were upset. The film “Her” doesn’t seem far off, as Sam Altman acknowledges. Such relationships give the human much more safety and control than in human relationships, which might both be very attractive to humans, while also excessively coddling them. Given that much human happiness and meaning comes from human relationships and bonding, widespread AI substitution of them could mean the destruction of a large part of all human wellbeing and meaning in the world. On a more prosaic level, society might atomise into individuals hoarding compute credits to spend on running their AI companions without connecting with other humans, with severe effects on society’s functioning, or humans might stop having children and human populations might crash. Humanity has flourished through collaboration and socialisation. If we use AIs to replace this in an overly thoughtless way, the fabric of society could crumble.

Apart from being superhuman at forming relationships with humans, AIs might be superhuman at persuasion. We can imagine AIs producing the vast majority of content that people consume. We can imagine a totalitarian world where the governments with the greatest compute resources can dominate the conversation forever. Instead of humans having ideas and sometimes persuading other humans to adopt them, driving social progress, any human-generated ideas might be swamped by a greater quantity of superhumanly persuasive counter-arguments that support the status quo. We can also imagine a dystopian decentralised world. Already, many online memes (in Dawkins’s original sense of the word) are maladaptive, spreading not by having good effects on their hosts but by being incredibly good at spreading from person to person. AI might make us much better at searching the space of ideas for the most viral ones. Ideas that aren’t maximally viral might be outcompeted. Eventually, our institutions could become mere puppets that serve as viral hosts for the most transmissive memes, as part of an endless tug-of-war where AI-generated memes compete to compel humans to spread them.

Seems bad.

Not good nor bad, but some third thing.

Many debates turn into mood affiliation debates. Are guns bad? Is more government good? But remember: politics is the mindkiller. Navigating a complicated world requires more than the ability to stick the label “good” or “bad” on entire domains. If you were seated in the control room of a nuclear power station, you wouldn’t ask yourself: uranium, good or bad? Instead, you want to steer towards the small set of states where the reaction is perched between dying out and exploding, while generating useful clean power.

We’ve also seen again and again that technology and social change have strong effects on each other, and these are often hard to predict. We’ve discussed how industrial technology may have led to democracy. There is serious academic debate about whether the stirrup caused feudalism, or whether the Black Death was a driver of European liberalism, or whether social media was a significant cause of the Arab Spring. The birth control pill was a major influence of the sexual revolution, and the printing press helped the Protestant Reformation. Often, the consequences of a new technology are some obvious direct benefits, some obvious direct harms, and the shifting of some vast social equilibrium that ends up forever reshaping the world in some way no one saw coming. So far we’ve clearly ended up ahead on net, and maybe that will continue.

Humanity has spent over a hundred thousand years riding a feedback loop of accumulating cultural evolution. Over the past few hundred, the industrial revolution boosted the technological progress feedback loop. Human wellbeing has skyrocketed, though along the way we’ve had - and are continuing to have - close calls with nuclear war, totalitarianism, and environmental issues. We’ve had a healthy dose of luck, including in generalities like the incentive structures of industrial economics and specifics like the heroism of Stanislav Petrov. But we’ve also had an enormous amount of human effort and ingenuity spent on trying to chart a good path for civilization, from solar panel subsidies to the Allies winning World War 2.

For most of this time, the direction of the arrow of progress has been obvious. The miseries of poverty and the horrors of close-up totalitarianism are very powerful driving forces after all. And while both continue ravaging the world, developed countries have in many ways gotten complacent. There are fewer obvious areas of improvement for those lucky enough to enjoy a life of affluence in the developed world. But the future could be much better still.

Know where to aim

We think it’s important to have a target of what to aim for. We need to dream about the future we want. A strong culture needs a story of what it is driving towards, and humanity needs a compelling vision of how our future turns out well so we can work together to create the future we all want. AI seems like the biggest upcoming opportunity and risk. We hope we can avoid the risks, and realise the positive vision presented here, together with a hundred other things we can’t yet imagine.

It seems bright as day to me that lighting makes us more productive and alert. In winter when the sunsets at 4pm, often my body thinks it’s bed time around 7pm. Although intuitively understood, there's a surprising lack of research quantifying this effect, most research focuses on the mood impact rather than productivity impact. So how much more productive are we with longer daylight hours? If daylight enhances our alertness and productivity, what is the extent of this impact? Anecdotally, I think I probably sleep an extra hour in winter time with the lack of sunlight. I also feel less alert and quick thinking. I do think this (case study of one) hugely affects my productivity.

“Low mood and low energy levels during the short dark days of winter may have always been an expected part of life for those living far from the equator, they were first identified as a treatable clinical condition during the 1980s” - Seasonal Affective Disorder: An Overview of Assessment and Treatment Approaches

SAD’s was first identified as a treatable clinical condition in the 1980s, but the winter blues have existed as a part of humanity's understanding of the seasons. According to the Seasonal Affective Disorder: An Overview of Assessment and Treatment Approaches, in the United Kingdom, 20% of people experience winter blues every year. 

Last year in January I did a trial of spending one month in the sunniest place in close proximity to Britain, the Canary Islands. I felt SO energised and incredibly productive. Compared to this year, where I decided I’d try tough it out in the British winter and just take more mid day walks and diligently take my vitamin D supplements - the personal difference is stark. Obviously there are many other factors here but say even if we are able to stay alert and focused 20% more with longer daylight hours than in winter, this seems like a huge opportunity for progress.

After hearing wellness guru - Andrew Huberman - talk about morning sunlight being one of the biggest impacts on wellbeing and productivity, I decided to give it a go myself, yet my daily winter walks in London at 200 lux weren’t cutting it (considering on a bright summer's day you can expect ~100,000 lux), I thought there must be other answers! I then read various blog posts on lighting to beat the winter blues. So next I tried to imitate sunlight indoors. I installed a light-splitter and 4 x 100W equivalent bulbs, providing 8x the amount of lumens I previously had in my office. Again, anecdotal evidence, but I do feel much more alert. I feel like I can keep working past the day time hours, much more akin to my summer schedule. 

Say that we were able to artificially create the impact of sunlight through bright lights in workplaces (totally doable) and we ended up with a 20% increase in productivity across the nation for the winter months, an average of 10% across the year. I'm not suggesting that people work 10% longer; rather, they could be 10% more alert and focused, they'd have 10% more focused time in their day. Say theoretically if this directly transfers to the UK GDP this would be a huge impact 10% (though of course it doesn’t work quite that easily). 

This may be a big jump but Britain has experienced a significant stagnation over the past decade, with our GDP consistently hovering around 3 trillion pounds per year (graph below). With this level of stagnation we’ve got to try all the quirky ideas, and I don’t even think this one is that farfetched. A 10% increase would be truly incredible. Even if it is an order of magnitude less than my hope of 10%, a 1% increase in GDP would still be more economic progress for Britain than we’ve had over the past 17 years. Something I think is worth at least a shred of research. Once there is evidence of the bright path forward for Britain, it’s in every employers direct interest and incentive to implement brighter lighting if the research does show sufficient productivity gains, so all that’s really needed is the research to exist in the first place.

British GDP per Capita - data.worldbank.org/

Say bright indoor lighting does increase GDP by the conservative 1%, that would be the equivalent of 31 billion USD economic value created, seems worth the one study to me.

It is completely possible I’ve missed some key research on the impact lighting has on productivity, if I have, I’d love to see it - please do comment below! I am genuinely curious how much an increase in productivity there is to be had with better lighting for the general population, and not just the current study of one (me). I propose someone in Britain does a randomised control trial for the impact of brighter lights in winter on productivity - it might be the spark Britain needs to get out of this stagnation.

This blog post is a submission to the TxP Progress Prize. 

Britain is stuck. A new movement around science, technology and economic progress offers a way forward. It champions abundance, not scarcity; state capacity, not decline; and supply-side action alongside demand-side subsidy. Above all, it is proudly solutionist. It recognises that golden ages don’t happen by accident: they are made by political choices.

You don't have to be a sports star or an elite athlete to learn valuable life lessons from sports. I'm the kind of person who learns by doing, mistakes and all. Recently, I've been getting back into tennis and surfing and it hit me—sports teach me things I can apply to real life. Unlike typical advice, these lessons stick because I've lived them.

I think it’s particularly useful to “experience” a lesson because it makes it more memorable and relatable, so while reading this, I encourage you to consider what are some lessons you’ve learned from sports?

Here's what I've learned.

Surfing

1. Being in the right place at the right time.

When surfing, it's all about timing the wave. You've got to be in the right place at the right time. Right where the wave is about to break, right at the edge of it. If it's already broken, you've missed the opportunity to ride the face and if you're too far away from the break, you likely won't be able to catch it, there's a real sweet spot. It's right in the curl of the wave that is right below where the wave is breaking. See the image above.

This also applies to starting a business. You've got to be at the right place at the right time, and working really hard to get there!

2. Keep paddling even when you think you've missed the wave.

When surfing, some of the best waves I caught, I truly thought I'd missed, but I kept paddling just in case I would make it. I learned to always try more than you think, to just keep paddling!

Tennis

1. Put the ball in their court.​​

In tennis, it's tempting to always aim for winning shots, but a consistent return game gives you a 50% chance of victory. Let your opponent make the mistakes and put the ball in their court. Missing every serve means losing 50% of the points. If you do an average serve but it goes in every time, you are hugely increasing your chance of winning. Consistency > Hard Shots. You can always play some winning shots, but only when you’re in "the right place at the right time."

2. Keep running even when you think you're not going to make it.

Similar to surfing, if you give up running to make the sneaky tennis shot because you think you aren't going to make it, you are definitely not going to make it. With tennis, there are many times in every game where I think I won't be able to return the ball, but I just think, "screw it let's give it a go." Then I often make it to the ball and return it - even when I didn’t think it was possible. It always feels even better when this happens, exceeding your expectations, doing a hard thing.

In life, often you're so close to success, you've just gotta keep running to make it, even when you think it's not going to work. This is very similar to the idea from surfing of "just keep paddling."

Basketball

1. Hanging around the hoop.

Similar to being in the right place at the right time, hanging around the hoop in basketball is all about putting yourself in the middle of the action. When your teammate takes a shot and he misses, you just grab the rebound and get it in. These are the easy shots, but they are still worth the same amount!

Running

1. It's much easier in a pack.

We humans are pack animals, we do things in groups, we naturally form tribes, we don't survive in complete solitude. When running with a group of friends or in a race, the momentum of everyone around you carries you forward. I often feel like my feet just move with a life of their own when I'm in a race. The energy, rhythm and forward momentum just makes it feel pretty effortless to move forward. I used to think it was a waste of money to pay for running events, but I now get it. It's worth it for the energy and momentum it gives you.

2. Setting goals.

Running a 10 km, half marathon, marathon or ultra. Whatever it is, often people will sign up for a race months out and set that as their goal. They’ll have a training plan that keeps them focused and disciplined. If they hadn’t set the goal, they most often wouldn’t have been able to run that distance. Though there is a caveat, if someone is consistent and keeps running for a long period, but not a huge amount; they can likely do a race without the goal setting. I’ve been running consistently 5-15 km/week for 3 years and now I can often just spontaneously go on a long run with friends! It gives me peak optionality, but if I wanted to really hit a race time, I’d need to train.  

3. Consistency.

You can’t go from zero running to running a marathon in 30 days. You need the discipline to train for a long time. Consistency brings rewards in running. Tim Ferris, the guru of shortcuts to success, tried to put together a shortcut to ultra running and infamously failed. He put together a plan in his book "the Four hour body" yet never actually completed the Ultra himself.

These are some of my recent reflections on life lessons from sport. I plan to keep adding to these reflections for myself. What are some life lessons you've learned through sport? I'd love to hear them in the comments!

I recently decided to step away from working on Thought Saver, though it will continue to run with George, CTO at the helm. It’s an idea I was and still am very excited about. Improving the way the world thinks and cognition. My vision for Thought Saver was to build a tool so that you can take away and really embody the key ideas from books or blog posts that you read using spaced repetition. I’ve always been curious by nature and I enjoy exploring my curiosities, Thought Saver was the perfect excuse to double down on that.

Thought Saver is part of the SparkWave foundry, where it’s funded by SparkWave and there is support through mentorship from Spencer and expertise from various SparkWave employees. I enjoyed my time working with Spencer and the others in SparkWave.

What went wrong?

Problem Discovery

This didn’t happen first, the product was built first. It meant it was hard to properly analyze this. To really know if this was a real problem that people were willing to spend money to solve it.

A vitamin not a mineral

The problem was too vague and all encompassing. “Improving people’s lives'' is far too vague of a problem. You need specific, tangible problems, otherwise it feels like you are playing 5D chess all in your head.

I think vagueness in a problem statement is likely an indication that there actually isn’t a problem in the first place. To contradict this though, there are some companies that have vague problems and users (Twitter, Reddit, Roam etc are a few examples of incredibly successful companies like this).

Tell tale Signs

Though there were tell tale signs from the beginning. I struggled to pitch the idea to people with the confidence and enthusiasm that I naturally convey. I felt somewhat robotic or stressed to word it correctly. I felt incredibly sensitive to other people’s opinions or lack of enthusiasm around it. Though I thought “if you stop it’s one sure way to fail.”

We also didn’t feel comfortable charging people for the tool. We never collected money for the app, this also strikes me as a bad sign.

Needing transparent analytics

We didn’t know exactly how users were using the app in a very transparent manner. We wasted a lot of time just “walking through mud” because we didn’t really know what was going on with our users. Yet it’s fundamental to know how your users are using your product. We did do lots of user interviews though, for more qualitative information.

Why did it take me so long to figure this out?

• I wanted to believe it would work (motivated reasoning).

• I had troubles with engineering progress initially and buy in, I thought having a cofounder and CTO would solve this problem for me! Having the ability to build quickly and test what we are building. However this didn’t resolve the problem.

What would I do differently next time?

• Get people to pay for your tool ASAP and you will waste much less time!

• As a founder, the person spending their day to day on an idea, you need to start with the idea from the very beginning. You need to have emotional buy-in to the idea, like you’ve “birthed it.” You can adopt another child but it’s incredibly hard to have the same level of attachment then if it was your own.

• I’d make sure my incentives are fully aligned with the growth of the company.

• To trust my gut and intuition about whether I really love the idea and if I am compelled by the idea. I think my gut knew it wasn’t a winner, and this can also be self fulfilling. I couldn’t convince myself or others to convey the vision (and I was subconsciously thinking this but trying to suppress it because I knew it’d only lead to a demise of my efforts and the company). I will make sure to listen to my gut and trust my instincts much more.

Next steps

I’m going to explore problem spaces that I am particularly excited about and that harness my individual skills, focusing on how I can provide as much value as possible to the world. How can I truly and tangibly improve people’s lives? I see monetized products and companies as a crucial part of this, because people paying money for a product to solve a problem concretely shows how much value it is providing them.

Problems I am currently exploring

1. Reinventing mass transit: I believe the world has not seen how good mass transit could be.

I'd love to reinvent the way we design buses to design user-centric experiences and smart self-driving buses. To get my foot in the door I could start by working on announcements on buses. This is something I have experience implementing and have networks within the industry and can easily get started on. It’s also something that is in demand with new UK legislation changes. I would next move onto bus payment systems, something that has a lot of inefficiencies and money in, then I will move onto redesigning the entirety of the bus. I also have relevant experience working in public transport entities and startups which makes this particularly relevant.

2. Leveraging TTS AI models for self-published authors: given the booming audiobook and self-publishing market, and convincingly good LLMs, I’m excited to use TTS AI models to assist self-published authors to create audiobooks and other online content.

I personally love listening to audio-books, and I’m excited to see more lesser known books on audio. There is an open source model that few people are using that is substantially better than any of the existing models (Sample of a book I generated here - https://publishing.florencehinder.com/). I think this would make a whole range of books and ideas that aren’t currently read to be more easily consumed.

3. Career advice for high school students: I have been working with high schools and developing a tool that utilizes job demand data, individual skills, and interests to match students with suitable careers and pathways to consider.

I think the existing solutions are outdated, inaccurate, and impersonal. It’s something that I’d have greatly appreciated when I was at school, instead of having several different career pivots before realizing the technology products with fast paced and adaptive teams were my bread and butter. Understanding users and creating user centric products (whether that's for public transit agencies for fast growing startups).

4. Housing Crisis: high quality housing should be a basic human right.

I think current housing sucks. It’s low quality, sprawled and unaffordable. I have a background in Civil Engineering and I am slightly obsessed with good housing design. I’m mostly curious to just explore this space! Understand the problems and see if there are any problems I could help solve.

If any of these ideas or projects sound particularly interesting to you, please reach out! I’d love to talk :)

I learned a lot working on Thought Saver about the importance of emotional buy-in to an idea as a founder, as well as the need for specific and tangible problems to solve. I also got insights into the influence of power dynamics, signalling and social dynamics on decision-making. I look forward to my next adventure and I hope I take all these lessons forward with me on my next endeavour!

In 2020-2021 I ran a team that implemented on-bus audio announcements at Auckland Transport, the transit authority for Auckland.

Why Audio Messaging?

Audio messaging on buses is a way to make bus journeys not only equitable but delightful. It provides the necessary support for Blind Citizens to navigate, whilst also providing assurance and confirmation for anyone navigating their transport journey that they’re on the right path. In the UK in 2023 government legislation has changed, which means that announcements on buses are mandatory by 2026. See more information here. This is exciting news for people with disabilities, but also those wanting easy journeys.

This being said, designing audio announcements on buses is all about execution. If you do it wrong, you could end up with lots of customer complaints and a bad customer experience. Public transport journeys, especially when unfamiliar, can be anxiety-inducing and stressful. One bad experience can substantially change people’s public transit experiences (1). Good transport audio has the opportunity to reassure and guide customers throughout their journey in a concise and enjoyable manner.

Design Principles

To design really good audio announcements we made sure to consider the following design principles. These principles were developed starting from international best-practice and tailored through engaging local customers, and blind citizens.

• Easy navigation

• Accessible

• Dual Language - Te Reo Maori & English

• Don’t irritate - not repetitious

• Concise

• Delight Customers

• Effortless to maintain

• Consistent

Process

We applied the “build-measure-learn” process to our development of audio announcements on buses. We ran design sprints at various stages of customer testing to make sure the experience was as delightful as possible for users. Though before we even started, there was a small problem… our bus stop names in Auckland were inconsistent. We had one set of names for physical signage for example:

Physical Signage: Grey Street

Then a different set of names for digital tools, such as AT mobile - Auckland Cities public transport app. For example:

Digital Signage: Opp. 329 Onehunga Mall

For the announcements we decided on core messaging, which includes just the short bus stop name 300m before a bus stop e.g. “Customs Street West” and then for key transport interchanges there is additional messaging. We discovered the pitch must not be too high as it can be difficult for the hearing impaired. Through audio testing, we settled on a volume of 60/70 dB.

Outcomes

With the rollout of the audio system, there have been lots of positive press and social media posts about the on-bus audio announcements for Auckland Transport. From people sharing audio recordings on social media about how great it is, to the overwhelmingly positive responses in the comments, news articles, and press releases about the improvement!

“It means that we can relax, without wondering where we are on the journey, and that means we can enjoy the journey.”

In my biased opinion as someone who implemented this project, audio announcements on buses in Auckland have been a great step in the right direction of making journeys easier for everyone.

Conclusion

I feel proud of the impact we have made and the tangible results we have achieved with delightful on-bus audio announcements.

If you know of any local authorities that could benefit from updating their audio messaging systems, I would be more than happy to lend my expertise and support.

💡

Contact me - florence@futurejourneys.co

Let's work together to create inclusive and enjoyable public transport journeys for everyone. Feel free to reach out, and let's make a difference together.  

Find out more here

There is something special and empowering about being able to transport yourself wherever you need, just through the love and kindness of humanity. It feels like some kind of magic.

Despite my admiration for hitchhiking, I didn't intend to start hitchhiking; it just kinda happened. I was in France in 2018 with my partner, Lachy, and we were planning to do a 3-month "tour of France," visiting small villages in-dispersed with WOOFING (Working on Organic Farms) and couch surfing to experience "la vie de France." This was originally meant to be via bike, but we weren't organized enough and hadn't got all the gear for a biking tour. So instead, we figured we'd take a combination of buses and trains around the place. Except for the first place we stayed, there weren't any good bus options. Our host offered to drive us to this village where he was doing a concert, it was in the direction we were hoping to go, and his friend would be able to take us another hour in the other direction. Then from there, we could hitchhike.  He confidently told us that hitchhiking wasn't a problem in France, that he'd got all around the country via his thumb!

After some skepticism and disbelief that it would be as easy as he claimed, I figured we might as well try because we didn't really have any other options except walking a long way and catching many meandering buses. We took up his offer and went to his concert in a small french town. The concert was great, even if it continued all the way till 2 am.

Feeling lucky, we wrote our destination on a piece of cardboard we acquired and stuck out our thumb. In less than 60 seconds, we had a ride; the second car that drove past just kinda stopped in the middle of the road, and a friendly woman invited us to get a ride in her small hatchback. It was at this point I was convinced we were going to continue hitchhiking for the rest of our trip.

Hence driving in a car is likely a far greater danger in France and the thing to be concerned about! If I ever noticed a driver that seemed under the influence or of questionable character we would turn them down. You haven't got much time to make a judgement but you've just got to do it!

Why I love Hitchhiking

• It's a great way to learn a language, practice communication skills. You are going to be spending hours in someone's car, probably speaking to them for the entirety of the time. I soon realised that this was the true trick to language emersion, putting yourself in a 6-hour-long conversation that you can't get out of.

• Understand a culture. You meet people in an intimate setting, in their car for hours at a time; we were recommended many things to do in the places we visited. We ended up going hiking in this beautiful spot in the Pyrenees on the recommendation of one of our kind drivers. They put on this new-age concert nestled right in the mountains of the Pyrenees, and he thought they might be on this time of year. Sure enough it was, and it was something I will forever remember!

1×

• It forces you to smile (sometimes for hours on end as you wait patiently for a ride!)

• It feels like this great social challenge, and I love a challenge!

• It's liberating to know if you get stuck somewhere, you can always catch a ride. For example when Lachy and I were doing a cycling trip in NZ, and Lachy's bike broke, we didn't have cellphone reception. His bike broke so badly that we couldn't even wheel the bike up and down hills; we'd have to pick it up and carry it. Though we were 40 km from where we started. We hitched a ride with a couple driving a ute that drove past and got to put the two bikes in the back of the ute. We talked for the rest of the hour's drive, heard about the couple's lives and shared stories. They even gave us two delicious craft beers to take home with us, while also just saving us from a tricky situation!

• One time we went on a long run and I severely underestimated how much food I would need and how far I could go (I didn't look at the elevation) so we ended up hitchhiking our way down straight to the local cafe.

• You meet interesting and generally chatty people and you are forced to leave your bubble.

• You learn boundaries very quickly; if you feel like something is a little off, you need to act quickly and ideally not get in the car or have the plan to get out of the car. We never had to get out of a car earlier than planned but we have turned down several rides because we didn't feel comfortable.

If you feel uncomfortable taking a ride from someone, you need to learn to say no, come up with an excuse or just flat say this ride won't work for you. Just because someone is offering you something doesn't mean you wave your right to say no.  

Types of people that picked us up

Most of the rides we got were from women with children a similar age to us. I felt like they were a mother of a friend of mine; they felt very familiar and nurturing. It wasn't infrequent that they'd never picked anyone up before, and it was their first time! I do think that people somewhat similar to you are the types of people who most commonly pick you up.  

People We've Picked Up

One of the first times Lachy and I met, he had picked up a Hitch Hiker from his parents town of the Coromandel, a German backpacker traveling around NZ named Richard. He wanted to head south, Lachy was heading south to our friends New Years gathering in Taupo, and he gave him a ride. They ended up chatting the whole way, and Lachy ended up inviting him to have New Years with our group of friends. A classic example of how hitchhiking is a shortcut to integrating into the culture of a country. Richard spent the next 4 days hanging out with us by the river in the sunshine!

My most impressive rides

• A taxi driver pulled over in the Allzas region of France drove past us, and we jokingly said, wouldn't it be great if he gave us a ride. The taxi pulled over just past us, theatrically popped open his boot and his side doors, whistled at us and waved his hand out the side of his car to indicate he wanted to give us a ride! Evidence of his business card he gave us at the end of the trip.

• e lady that drove us to her fancy BMParabough office in Luxembourg then offered us to come back and stay with her another time we visit the area.

• Thomas, who picked us up, offered to drive us another time for 5 hours, and then we visited him six months later in Paris.

My tips for giving it a go

• Go early in the morning

• You need to be comfortable with saying no to rides you don't want, even if it's just based on a vibe.

• Bring an A4 book and a vivid, where you can write your destination, or somewhere on the way to your destination if it's a particularly long way.

• Check out the guides on hitchhiking in the country/area. Often some places are far easier than others (France is particularly effective)

Hitchhiking is a beautiful thing, and it really restores my faith in humanity. It reminds me that so many people are kind and humble, wanting to make others' lives better. I recommend giving it a go, once you've done your research!

Any questions about hitch-hiking? I'd be more than happy to answer them!

Last year for my 27th birthday, I ran 27 km with my friends Adam, Nina, and Lach! Last year I ran 26 km, and this year I'll be scouting out some 28 km buddies.

For those of you that haven't already realized, I enjoy running! I enjoy getting around on my own two feet, the feeling of achievement with every step I take, and the mental clarity it provides me. I enjoy having the time to listen to an audiobook and think. I enjoy running with friends, running by myself, up mountains, around parks, and through cities. In the sun or rain. But mostly, I love the feeling after I've run and showered where I feel smug that I've done something good for myself, putting one step in front of the other.

Following are all the many reasons I love to run and why I plan to run my age in km's every year!

Mental Clarity & Zen

It's a great time to think, process and plan. When it's just you and your own two feet, you plod along at this consistent melody of thud thud thud. It gives you lots of time to think and process or to listen to your favorite audiobooks. Slowing down if I need to do particularly deep thinking.

Going for particularly long runs also does something great to relieve stress in me. I loaf around for the rest of the day but feel zen inside. I feel satiated that I've completed something great for me, a sense of accomplishment and tranquility.

It Feels Great to Feel Fit

It's just a really nice feeling to know you are fit and healthy and that you can outrun a stalker or sign up for a 47 km adventure with friends the next day (both happened). When I am fit and healthy, I also feel much better mentally; I feel much less sluggish and far more energized for life.

Longevity

According to a meta-analysis in the British Journal of Sports Medicine, running participation is associated with 27%, 30%, and 23% lower risk of all-cause mortality, respectively, compared with no running. Analysis showed no significant dose–response trends for weekly frequency, weekly duration, pace and the total volume of running, suggesting that consistency is more important than quantity. (British Journal of Sports Medicine, Is running associated with a lower risk of all-cause, cardiovascular and cancer mortality, and is the more the better? A systematic review and meta-analysis)

Another literature review (a study of many studies in the research field) showed that all-cause mortality decreased by about 30 to 35% in physically active as compared to inactive subjects. Eleven studies included confounding risk factors for mortality and revealed an increase in life expectancy by 0.4 to 4.2 years with regular physical activity. This review also looked at the difference between different sports and showed consistently greater life expectancy in aerobic endurance athletes (e.g., running, cycling) but inconsistent results for other athletes. (National Library of Medicine, Does Physical Activity Increase Life Expectancy? A Review of the Literature)

Achievement and progress every year

I am excited about running an additional 1 km every year because it means every year is one little bit further. It means every year, I am setting myself a new, slightly harder, yet hopefully manageable challenge. Who knows what year I will make it to, though I know some 80-year-olds are running 80 km! I'll aim high. By posting this publicly, I'll have a much higher sense of commitment to my goal. So thanks for your social pressure and support from reading!

Consistency - Not just breaking records

You can run almost anywhere with running shoes (and a sports bra). It's simple and effective, just one step before the other. No matter where you are, you can generally go for a run. I enjoy occasionally getting faster, breaking records, and running further, but it's not what drives me. I run with consistency in mind, with the hope of many more years to go on my feet. Knowing this, I don't aim to always beat my PB; I strive for consistency, and that's why I am running my age every year from here on out. I want to continue putting one step in front of the other for as long as possible. I want to live a long, healthy, and enriched life, and running is a way to point me in that direction.

Getting Out the Door

Running's not for everyone, but it's great for me. Though I love running, and I have since I was young, I'll be the first to admit that I too often struggle to take my first steps out the door. It's easy to make excuses for why you aren't going today, especially for the non-routine among us (and I have a natural aptitude for being non-routine). By setting a goal for my age every year, I am focusing on consistency, not speed, and creating a commitment to putting my shoes on.

Women weren't able to run Marathon in this Era.

Often when I put on my shoes, and I am reminded that women alive today were told not to run. Kathrine Switzer was the first woman to run the Boston Marathon as an officially registered competitor in 1967. She entered the race under her initials, so to avoid the officials noticing she was a female. Once she started running, the race official tried to tackle her to the ground, but luckily she managed to run away. That was only 55 years ago! I feel thankful to her and the others pushing for women's rights, and feel empowered to run.

Running with Friends

There's something magical about running with a group of friends. You feel like a pack of animals running along together, like a pride of Lions, a cackle of hyenas setting out across the savanna, giggling gleefully together for your silly, nonsensical adventure. So I hope that every year I run slightly further, and  I get to enjoy this with friends, new and old.

Why I want to Run my Age every year

I believe running is truly good for me and wonders for my mental health and clarity. Though I am also not very routine, I know it's easy to run out of steam and lose a habit. I want to run the distance of my birthday every year, commit to a long-term goal, and be fit and healthy for the rest of my life. To continue pushing and challenging myself and always have something challenging me a little more every year. Every year this will be my Birthday treat to me, one km more each year.

After a long run, I always have this feeling of having been on holiday, I've spent a long time just thinking, and it provides this sense of mental clarity like no other. What more could you want for a birthday treat?

Want to join me for a 28 km run next year? Send me a message! Why do you like to run?

I'd love to get to 80 years old and still be able to run 80 km!

What's your goal and are you climbing the right mountain to get there?

Say you are on a mission to build a startup, and you want to provide a whole lot of value to the world with the company that you build. You've got your vision and your goal, but there are many things that can point you in the direction of the biggest success, your customers, your market, and your customer's problem that you are trying to solve.  

Now, I like to imagine the biggest success you could reach as the biggest possible mountain in the world, the global maximum. I also like to imagine a success but not the biggest one, as a  small mountain or a local maximum. If your strategy is that you are only moving upwards, you might not know what mountain you are climbing. You might find yourself at the top of the smallest mountain, with nowhere further to climb, and realize you've reached your local maximum. You can optimise almost anything, but if you choose the wrong thing to optimise you might be wasting your time.

Once you pick a mountain you can't just keep switching, you've got to make sure you are climbing Mount Everest.

Local Maximum

• Eating the same foods. Your parents served you rice and three veg growing up, and that's all you've known.

• Doing the same hobbies/activities as you've always done? You have played tennis since school, your parents gave you lessons, and you played with friends in school and university, you haven't really thought about whether you even like tennis! You aren't even sure what is most important to you about what sport you play, because you haven't even tried enough variety to know!

• Staying in the same friend group as at school without considering who is serving your needs? You might still get along, but actually, you realize that talking about what clothes to buy and makeup to wear isn't hugely satisfying, and you feel somewhat out of place, but you might stay because you don't know anything else. It seems daunting to try another mountain and start climbing from the bottom again.

• Staying in the same degree your parents did or working in the same role as your degree, without considering all the options. Your parents might be engineers, so you decided also to study engineering. Everyone you went to University with is suddenly an engineer, and you are now on one very clear path up a mountain, and you aren't even aware of what other mountains there are! Do you really want to be an engineer? Is this the best use of your natural aptitude? Is this what brings you energy?

• Say you are fretting over getting paid the most compared to your friends/colleagues, and your goal is to get paid more. But you've never considered looking for employment outside of your local country. You might be looking for the largest mountain in NZ, but you need to be broadening your search if you want to be hitting the global maximum (if that so happens to be your goal).

*Note: you can get paid ~4x salaries by moving to the US in many high-paying jobs. Software engineering, data science.

• Not exploring enough startup ideas or feature ideas, not enough high-risk bets. You could be listening to customer feedback, iterating on your product, but not trying enough risky moves to see if there's a bigger mountain out there. Casting out a net to see if it sticks to a higher slope.

Some of these don't really matter, but some might matter a heck of a lot! It depends on a heck of a lot on what's most important to you; what are your goals? What is your vision for the future?

Global Maximum

For each of these local minimums there is a global maximum!!

• choosing the best country for you to live in -  you want to earn enough money to be incredibly comfortable, so you choose to make your way to America.

• choosing the best career for you to work on - if you want to optimise for earnings, you'd probably choose software engineering.

• choosing what customers you are targeting.

• choosing what market your startup is targeting.

If you notice yourself not exploring enough of your options and seeing the same choices again and again, without being intentional about it, you might be at a local maximum.

How do we get from the Local Maximum to the Global Maximum?

• Look up from your feet and explore more of the world. Understand what possibilities there are past your mountain.

• You can't always keep trying to find the right mountain; if you keep trying to find the right mountain, you end up in an optimization loop, which unfortunately gets you nowhere!

Failure Mode

You can "explore" too much! A telltale sign of too much exploration is the optimisation loop! You are trying to optimise everything so much that, much like when a computer's ram is overloaded, you get stuck with too many options to optimise in a loop with your wheels spinning, and you aren't able to make any progress. Climbing a mountain isn't easy and all the time you spend exploring rather than exploiting is time that you aren't spending climbing. Even achieving a local maximum in life is a big achievement, so there's plenty of reasons to pick something and run with it.

In life it's not always clear if one thing is better than another, you won't know if one mountain is really taller or not, unless you climb both, and there isn't enough time in life to climb all the mountains! Life is nuanced and it's not easy to know if your choice was better than another.

via GIPHY

What's the right balance?

There's some kind of balance between explore/exploit that will allow you to find the right maximum for you. Depending on the stage of your project or of your life will depend on the amount of exploration vs. exploitation. If you are early on in your life and career, lots of exploration will help you better understand what is out there. Similarly, if you are at the early stage of your startup, lots of exploration of different feature ideas are more useful. Later down the line, when you have a clearer product and feature set, and a proven path to success, it makes sense to explore less and exploit less.

Inspiration for this blog post came from walks with friends around the beautiful city of San Francisco and on the Scottish West-Highland Way.

I often find it amusing when people focus stubbornly on optimizing one particular dimension; maybe it's someone frugal making sure they save on their food bill by ensuring they cut out takeaway coffees, or maybe it's someone booking cheap flights for their holiday destination before booking an Airbnb.

I've got some bad news, buddy, but housing is your biggest cost (unless you have wealthy benefactors, in which case, why are you reading this blog post??). The classic rule you hear is to spend 1/3 of your income on housing, but why? Do you have to spend excessive amounts on rent? When you go on holiday, unless you are flying to New Zealand for a day, almost everywhere you go, accommodation will be your biggest dollar bill.

There's something about paying excessive amounts for rent that just makes me uncomfortable. Though only one thing makes me even more uncomfortable, and that's the tied-down nature of owning a house! I choose to keep my freedom over having a mortgage; thank you!

I choose to keep my freedom over having a mortgage.

The median annual earnings in London are 39,000 pounds, while the median monthly rent is 1,500-2000 pounds for a 1 bedroom flat, a total of 18,000 - 24,000 pounds/year. That's ~50% of your earnings going straight into rent, let alone food, transport, or any joys in life!! Because you know what, money does provide you with a little freedom.

Clearly, this is a pretty illogical system, but I love London. I love big cities and all the interesting people that they suck in. One of my favorite blog posts by Paul Graham talks more about this with Cities and Ambition.

Alongside this thought, many people talk about "you become more and more like the people you spend the most time with," which is why I don't live in the countryside. I want to be surrounded by ambitious people challenging the status quo and willing to make sacrifices. You don't end up in cities like SF or London by mistake; you end up there because you are willing to pay the price for the people you spend time with.

💡

Couldn't there be a way we could have all the fun people, but for a smaller cost? I just hate spending money :S

A friend of mine once said something along the lines of...

often if you think a little more creatively, you can probably figure out a way to achieve whatever you want.

And I am simultaneously proud and embarrassed by the level of creativity in my quest for cheaper housing. So here are Flo's top ways to cut costs.

1. Inherit a mansion (honestly, we all know this would be the easiest way, though not a tip I have succeeded at)).

2. Do you work a remote job? Do you enjoy looking after pets? Try housesitting. I've spent the last 6 months not paying rent. Create a profile, ask some friends for references, and then apply for some obscure sits to start with (somewhere that is a less desirable location). The more reviews you get, the easier time you will have at finding new places. I've housesat in the South of France, London, and I am currently spending 3 weeks in Berlin, with no housing costs and an adorable cat to hang out with! *Note: I am very privileged being a white female; I probably have an easier time finding sits because of these two factors.

3. Buy a cheap house in the south of France, do it up, and invite all your friends!!
   This is the real dream, to have a 6-month holiday with friends in cheap locations with good food and weather. HMO if you are interested. This might happen! You can buy a 100-year-old homestead in the South of France for ~100K euros for a lifetime of joy and friendship?!

4. Or just rent an Airbnb in a hotter climate for a month or two of your winter and invite all your friends!!

5. Buy a house (or rent one in your central base e.g., London) and then list it on Airbnb for 3 months a year during the peak season (and go to France and hang out with me??). At peak London rates, you can make 250 pounds a night --> that's around 15,000 - 21,000 pounds from just 3 months of rentals!! Then you can enjoy your time in London for the rest of the 9 months. Plus we all know that you should never spend the entirety of winter in London, it's not good for anyone!

6. Message people on Airbnb and set up a 9-month/year unofficial contract on the down low. There's a law for Airbnb rentals that says you can only rent a home out for 3 months a year, and 7% of London homes are listed on Airbnb each year! What happens to them all the rest of the year? a) either they cheat the system, or b) they might be empty?? Try messaging a few and see if they'd be interested in doing a 9-month-a-year deal, then go somewhere sunny and cheap for 3 months a year (or go housesitting?!)

*Caveat: my parents live in London, so it makes this lifestyle particularly easy for me, if anything falls through, I can stay at there's, and it's honestly kinda nice to be forced to visit in some monthly-ish cadence. This is very privaleged though, and if you didn't have somewhere to stay as a backup, housesitting might be much more stressful!

So not paying rent and splurging on food and transport reduces your cost of living by 2/3. I spend ~700 pounds/month on all my expenses. This leaves me lots of room to save and also enjoy life! Despite being on a founder salary. I can eat out without a feeling of restriction or needing to save money.

You are probably thinking, why are you sharing such great (unscalable) tips and tricks to the entirety of the internet?? Once everyone knows your ideas you'll lose all your IP! I hate to break it to you, but you are probably not going to follow any of my ideas because people are notoriously bad at following through. It's not the ideas that matter. It's the execution.

TL;DR

I urge you to consider, how can we in our day-to-day help encourage ourselves and others to make choices which are best for themselves, to build a city, to build a society with the decision architecture that connects people, and helps people live lives with higher levels of satisfaction and flourishing. It’s time to consider and measure:

• What is best for people?

• How might we set the default option?

• How might we make the path of least resistance one we can be proud of?

What is Nudge Theory?

It’s not just elbowing people out of the way, perhaps it’s elbowing people in a direction that is better for themselves. Actually, it doesn’t even have to be elbowing; physical touch is not required. It’s about how we can create initiatives that nudge people in society to make better decisions for themselves, while still giving people choices. Nudge theory is a concept in behavioral economics, political theory, and behavioral sciences that was first popularized in 2008 by the book "Nudge: Improving Decisions About Health, Wealth, and Happiness" by Richard Thaler. I am intrigued as to how we can connect nudge theory with active modes of Public transport.

People notoriously don’t do things that are good for them. We all know exercise is good for us and has all these great health benefits, but how often do you delay it to the “next day?” How many of you don’t get enough sleep but continue to watch one more episode, or scroll on your phone.

People often know what is good and bad for them but we struggle to get the continuous motivation to succeed in these efforts, we hit resistance and road bumps (sometimes literally) along the way.

An example of something that is notoriously good for people, is walking and cycling as a way to get around. There are numerous benefits of using active modes of transportation.

The benefits of active modes of transport

There are so many benefits of active modes of transportation, I will just point out a few key thoughts I have on this, but the list is endless!

On a societal level

• Better health outcomes

1. For commuters who cycle to work there is a 46% reduced risk of developing cardiovascular disease and a 52% lower risk of dying from the condition.

2. A University of Auckland study from 2010 estimated that shifting 5% of vehicle kilometers to cycling would avoid 116 premature deaths annually as a result of increased physical activity, which is the equivalent of $200 million in economic savings.

• Decreased pollution with fewer cars on the road

1. Levels of harmful nitrogen dioxide gas emissions halved in London during covid lockdowns due to fewer cars on the road.

On an individual/company level

• Exercise improves mood and sleep and reduces stress and anxiety.

• Parts of the brain that control thinking and memory have greater volume in people who exercise versus people who don’t.

• On average, cycling increases productivity by 15% by exercising before work.

• On average there are 15% fewer days off work through illness, for those that cycle to work.

• Cyclists are the happiest commuters. Do you know that warm, happy-inside feeling that often comes with a bike ride? Keen cyclists have known about it for some time. Now, a new piece of research from the University of Auckland has thrown more light on it. Some of the key reasons that cyclists get so much satisfaction are as follows.

1. A high degree of commuting control and ‘arrival-time reliability’

2. Enjoyable levels of sensory stimulation

3. The ‘feel better’ effects of moderate-intensity exercise

4. Greater opportunities for social interaction

Active modes of transport are a prime example of something which provides infinite good for society. The more people cycle and walk as their form of transportation, the more physical activity people are doing in society, and the more social interactions, connections, and increased mental well-being in society. The reduced cost of medical bills reduced costs of road infrastructure and the reduced cost of environmental degradation.

How many of you think biking to work would be a good idea? Well, 65% of Aucklanders believe cycle lanes are good for the community, yet in 2018 only 1% of Aucklanders cycled to work. Why is there such a discrepancy?

There are too many roadblocks, to people joining active transport modes. To change this we need to make walking and cycling the “path of least resistance” or the “default option”.

The Path of Least Resistance

path of least resistance

Nudge theory is already applied in many technology companies although with often the wrong motives, I think it’s time we consider applying this in governmental organisations. An example of the path of least resistance in the tech industry is when purchasing goods or services online. Amazon or online retailers often have an option for “one-click payments”, allowing you to purchase a good or service, with the least amount of effort required. That means, fewer opportunities to consider, do I really need this, could I buy this cheaper somewhere else? It’s so fast it doesn’t even let you consider your other options.

Like water and electricity, humans too take the past of least resistance.

How instead can we apply “the path of least resistance” to active modes of transport?

Here are some of my ideas and thoughts on how we could make active modes of transportation the path of least resistance.

• E-bikes have been a massive win, as suddenly the thought of cycling doesn’t seem so hard when you have something to assist you up Auckland’s mountains.

• Removing barriers of people not feeling safe, by building walking and cycling lanes, increasing street lighting (as AT is doing)

• My personal favorite: traffic light signals that prioritise walkers, then cyclists in the CBD, then buses, then cars (quite literally intentionally creating active modes to be the path of least resistance)

• Having safe and sufficient bike parking, especially at transport hubs

• Having sufficient space and places to carry bikes/scooters on public transport, such as on trains, buses, and ferries.

• Paying people for every km they cycle.

The Default Option

Default options are pre-set courses of action that take effect if nothing is specified by the decision-maker (Thaler & Sunstein, 2008). Setting defaults is an effective nudge when there is inertia or uncertainty in decision-making (Samson, 2014). There are many examples of this with very successful technology companies, and a large reason as to why they have succeeded, but I think it’s about time we started using that effective nudging to encourage people to do what is actually good for them.

An example of the default option applied to technology companies and something you interact with in everyday life is when you subscribe to a “free trial” but you still have to enter your card details, and if you don’t cancel in 7 days you will be charged by default. (I personally have been caught out numerous times on this).

For many people in Tāmaki Makaurau, the default option is to drive. How might we shift this default to active modes of transport?

How can we “set the default option” for active modes of transport?

• having miro-mobility integrated within public transport apps so that it doesn’t require customers to have to use two apps, instead, it’s all in one place. This is something that is currently in beta testing for Auckland transports AT Mobile.

• “hireable bikes within the CBD that you can pay for with your transport card, so you don’t need another payment option” by default you are eligible to use it.

• as an individual, making walking or cycling your default commute method, meaning you don’t have to change how you get to work every day, you are just on autopilot.

• sending a notification on AT mobile in the morning for the weather for the day, to help you prepare for walking or cycling trips.

These are all examples of walking and cycling, but we can apply this thinking across any organisation we work in and any life that we live. How can we build a better society by being intentional about the social architecture we are creating?

I used to think “I’ll just get rid of my phone and never use it again because the social media apps don’t serve me” I felt shackled by apps that were addictive. Then I realised, there are other apps out there to help you take control of your device and serve your needs. Here is my list of favourite apps that actually IMPROVE my life! Maybe they will help you too :)

1. News Feed Eradicator - Honestly this one is definitely at the top of all my apps. It works especially well in combination with focusmate. I am blocked from all the addictive social media sites, and I have to go through two more steps (extra levels of friction) if I want to access them, and even then, I select how long of a break I want, which I generally select “5 minutes” (including Twitter) and then it gives you all these epic and inspiring quotes.

2. Focus mate - allows you to virtually cowork with people to hold yourself socially accountable to the tasks and schedule that you set.

3. Strava - Keeps me motivated to keep running, through the social element of seeing other people's adventures and getting “kudos” on my own adventures. As well as being able to look back over your year and see how consistent you were with your exercise goals.
   
   I always feel sooo much better when I do plenty of exercise and so I am happy to use any psychological nudges and tricks on myself to make sure it’s a part of my life.
   
   Feel free to follow me on Strava, or if you are nearby, hit me up for a run!

4. Just recently I stumbled across IFTTT and now I have all my text messages accessible in my email inbox, and I can send text messages via email too. This seems great because I can have way less reliance on my phone. All my texts come to my email inbox!

5. Libby - Unlimited free audio and digital books from Virtual Library accounts and sometimes has more content than is available from Audible. You can also link it up to your kindle so you can still read books on an E-reading device.

6. Thought Saver - a shameless plug! Helping you remember your most important ideas and prime your brain to think in the ways you want to.

7. Snipd - let’s you listen to podcasts and then snip sections you are most interested in. It then sends you emails with all the sections you’ve snipped! Great for reminding me to add the content to Thought Saver.

8. GTD in Gmail - I use Gmail as my all-encompassing to-do list! It has every task I need to complete in one place. It’s weirdly stress-reducing because of how simple it is!

9. Canva - I was a little skeptical at first like surely it’s not that much better than PowerPoint/publisher/Gslides etc. Until I created a pitch deck in it within 1 hour from one of their templates and it looked SO good. I thought I hated slide transitions but I realized I hated BAD slide transitions!

10. HeadSpace - helps to provide a low-effort meditation. I don’t need to prepare what I want to meditate on, they provide me with all the guidance I need!

11. Calendly - allows you to let people book meetings with you when you are free. It automatically integrates seamlessly into your calendar and updates time zones according to the person viewing. Feel free to book a time with me!

12. When2meet - When you are always working with coworkers in multiple time zones, it allows you to schedule time from different time zones easily!