Alex Kolchinski

I’m looking for a cofounder

Summary

I’m looking for a cofounder for my next company. I want to work on AI-powered B2B workflow automation software, but I’m not committed to a specific direction yet.

I’m currently working on a workflow automation product in the insurance space that just crossed $10K/mo in revenue. In the past, I’ve been the CEO of a YC-backed startup, a PhD student at the Stanford AI Lab, an APM at Google, and a software engineer.

I’m looking to either stay CEO and join forces with a technical CTO, or to become the CTO to an exceptional CEO. For more details, read on!

If you’re interested, or know someone I should talk to, please reach out — I’m at alex@kolch.in

My Background

I grew up programming, “turning pro” when I sold a Flash game in high school. I worked full-time as a software engineer for a year between high school and college, then earned a BS/MS in computer science at UChicago, concentrating on AI.

After college, I worked at Google as an APM, then went back to Stanford for a PhD program. Initially, I was planning to focus my research on AI-powered tutoring software, but came to the conclusion that the underlying AI technology wasn’t powerful enough yet to build what I wanted to build. So, I pivoted to doing AI research in natural language processing and generative models, publishing three papers.

I then got excited about startups generally and automating food service specifically, and dropped out of Stanford to co-found Mezli, which I led as CEO. We launched a popular autonomous restaurant, but went out of business after our Series A fundraise fell through. I raised $4M from investors including Y Combinator (but failed to raise the additional ~$10M we needed), led a team of ~30, and ran several functions including finance and marketing. Google Mezli for news, reviews, etc., and you can see a video of the tech here – https://www.youtube.com/watch?v=DV2I9XwcEZE

I spent the latter half of 2023 shopping around Mezli’s IP and shutting down the company. While doing that, I used the newfound free time on my hands to build and launch a couple of products solo. One is a B2C utility app (www.readtome-app.com); the other is a workflow automation product in the insurance space that just crossed $10K/mo in revenue.

I’m now evaluating whether to keep doubling down on this product or to pivot to a different niche that might be a faster place to grow to $1M+ in annual revenue. As I start that discovery process, I’m also on the hunt for a cofounder.

My Skills

While the previous section probably gives you an idea of what I can do, here’s a more specific breakdown of my skills:

Entrepreneurship: I’ve launched multiple products as CEO or sole founder, one reaching $20K in monthly revenue and another reaching $10K/mo. Between those experiences, several other exploratory projects, and spending a lot of time in the startup ecosystem, I’ve developed a pretty good sense for what it takes to grow a company from an idea to meaningful revenue — and more importantly, how to quickly discard the many ideas that prove unviable.
Software engineering: I’ve built a wide variety of software over the last 22 years (time flies…) and I’m very good at picking up new technologies quickly and getting things shipped. This includes the latest wave of AI tech — I’ve leaned heavily on modern generative AI for my last two products. However, I’m very much not a VP Eng who institutes best practices in a large team — I’m more on the “incur tech debt to get to product-market fit” side of the spectrum than the “pay down technical debt to make a product long-term maintainable” side of it.
AI research: I spent three years of my life largely focused on AI research at Stanford. While I haven’t trained a custom model in a few years, I still have a good understanding of what today’s AI can do, and of what’s likely to be possible soon. I actually don’t think most startups should be in the business of conducting AI research or even of commercializing techniques directly from research papers, but if that changes, I have the relevant background!
Sales, fundraising, and recruiting: I group these together because in a sense, they’re all manifestations of sales. I’ve run a sales or sales-adjacent process many times, including pitching hundreds of investors to raise $4M for Mezli, recruiting a number of Mezli’s team members, and now selling B2B software in the insurance space.
Marketing and PR: I’ve run marketing campaigns for Mezli’s robotic restaurant and more recently for the ReadToMe app. The Mezli campaign included local and national media hits and drove ~1M views and ~10K purchases for our brand. I’ve also had success reaching a broad audience with my blog, including multiple front-page posts on Hacker News and ranking #1 on high-volume Google search terms.
Finance: I have a good understanding of how the numbers work that make businesses tick, and of how the financial markets work as well. This has come from a variety of classes in college and grad school, an internship on Wall Street, and running the financial side of Mezli.

What I want to work on

In short: AI-powered B2B workflow automation software.

Like many others in Silicon Valley, I think that the current wave of progress in AI is a generational shift — we’re likely at the beginning of a change as big as the advent of the Internet.

Until now, computers have largely been able to transmit and process information only in rigidly-defined ways: humans have had to define exactly how data could be input into programs, stored and processed by them, and given back to users.

That’s now changing in a spectacular fashion. Thanks to transformer-style models, many tasks involving unstructured data (natural language, images, video, audio, etc.) that were previously the exclusive domain of humans can now be partially or fully done by software.

This includes a broad swathe of repetitive white-collar work that accounts for many trillions of dollars of value created per year. With AI tooling, humans in the affected industries — and that includes most industries — will be made vastly more productive. An analogy might be the transition from paper spreadsheets to electronic spreadsheets a few decades ago, or paper mail to email more recently.

That a lot of this automation is about to happen, and that a lot of economic value is about to be created, I haven’t heard anyone seriously contest. The more uncertain question is which companies are going to create that value and capture a share of the value they create.

Some important sub-questions:

How much of the action is going to be captured by incumbents vs. startups?
Which parts, if any, of the new landscape are going to be owned by a fragmented assortment of companies, vs. a small number of huge players?
How will the domains of different companies be carved up — by industries served, type of technology used, both, or neither?
Will some companies playing in the new AI-enabled landscape have significantly better economics than others, which might in turn look more like services businesses? How will that be determined?

The answers to most of these questions are currently unknowable, so my inclination is to jump in, get to $1M+ with a narrow “wedge” product that’s quick to sell, then grow outwards from there, responding to inevitable shifts in the competitive landscape of the software industry, including advances in the capabilities of AI, as they transpire.

I’m inclined to start out by solving a fairly narrow problem in a specific industry to begin with, as opposed to building tools for other companies to use “horizontally” across industries. This is for a few reasons:

A well-constrained customer profile can make the sales learning curve faster early on, allowing for a faster ramp-up in revenue.
I’m seeing way more founders jumping into the tooling layer right now than into the application layer. This likely implies less competition in at least some vertical-specific product areas.
The landscape of what’s possible with AI, and which tools are needed to do it, is changing at a very fast pace. Many tooling companies whose products are useful and popular today might be completely obsolete tomorrow. A company whose product is solving an application-layer need is more likely to be able to make use of improved AI tooling seamlessly and continue serving its customers instead of being replaced.

I’m now on the hunt for the right vertical, and the right niche in that vertical, to get started. It’s possible that my current product in the insurance space will be that initial product; it’s also possible that I’ll find something else that I like better.

As I start that discovery process, I’m on the hunt for someone to do it with, so that we can shape the product direction of the company together.

Who I want to work with

I’m in the relatively unusual position of having a background in both the business and product side of entrepreneurship as well as in the technical side, both in software engineering generally and AI specifically.

However, rather than continuing as a solo founder, I’m looking for a cofounder for a few reasons. With the right cofounder,

Starting and running a company together is more fun than doing it alone.
Two heads are better than one.
Many hands make light work.

As far as who I’m looking to work with, I can see one of two arrangements working well:

I stay CEO and a CTO joins me who has a history of shipping software quickly. I focus on selling; the CTO focuses on building and I help build when appropriate. In this arrangement, we’d go through the discovery process together before committing to a direction for the company.
I become CTO to an extraordinary CEO. The CEO sells, I build. Because I’ve been the CEO of a startup that had some temporary success, I have a pretty high bar for this one: the CEO would either have to have had a previous exit as a startup CEO, or be a veteran of an industry that they can immediately start making sales in — or both.

Either way, personal and professional compatibility are key — we should get along famously, and working together should feel like much more than the sum of its parts.

Other preferences

There are a few other things I should mention about my preferences:

I feel very strongly about working in-person together, most days of the week, most weeks of the year, in or near San Francisco. I want to stay in-person forever, and I want to stay in the Bay Area indefinitely, with the possible exception of if we end up serving a customer base that’s heavily concentrated in a different city.
Founding a startup requires a level of intensity that’s alien to most people — when done right, it doesn’t leave room in your life for much else that takes proactive effort. You need to be prepared for this. However, some founders grind to the point of negative marginal returns, and that’s not a good idea either. I strive for, and expect from cofounders, a level of intensity that’s far beyond a 9-5 job, but that does (at least periodically) leave room for recovery and perspective.
At this stage of the game I’d be looking to split equity equally, with one extra share to the CEO to break ties. I also prefer a longer vesting schedule than 4 years to align founder incentives for the long-term.

Interested, or know someone I should talk to? Please reach out — I’m at alex@kolch.in

Announcing ReadToMe

Today, I’m announcing the public launch of the ReadToMe app, which turns paper books and other printed text into high-quality audio.

I originally built the app as a present to my fiancée, who has a reading disability but loves books. Often, she listens to audiobooks while following along in the same book on paper, but some books, especially older and less popular ones, are only available in paper form and don’t come in audiobook or even e-book form.

We looked for a way for her to turn paper books into audio, but all of the apps we found didn’t do a very good job. Many of them were very good at turning e-books and other digital text into high-quality audio, but made many mistakes when scanning paper books. Common issues included inserting page numbers and footnotes into the middle of sentences and getting words wrong or missing them completely. Overall, there ended up being so many mistakes that the books were very hard to listen to — and that was for the better apps.

As a Christmas present, I wrote an early version of ReadToMe for my fiancée. When she found it useful, and I had spare time on my hands while shutting down my last company, I built out the app into its current version — which is what you see here.

The app lets you scan up to 20 pages at a time and turns them into high-quality audio, with very few mistakes. The app costs $9.99/month for up to 250 pages/month — sorry it can’t be free; I estimate the $9.99/month will cover the costs of the pretty expensive AI technology it uses on the back end.

Known issues that I’ll be working on fixing if enough people end up using the app include:

Scans can take a few minutes to come back as audio, especially when scanning multiple pages at once.
Rarely, scans will fail to come back as audio at all and have to be retried.
The AI I’m using on the back end will sometimes “correct” the wording of a book, especially when an author deliberately uses incorrect grammar.

I expect the app might be useful for a couple of types of user, including:

People with reading disabilities or just age-related far-sightedness that makes reading hard.
People who want to seamlessly bounce between reading something on paper and listening to a few pages, e.g. while driving.

I’m also looking forward to seeing who else might find it useful.

If that’s you, or you know someone who might find ReadToMe useful, please give it a try and/or let them know! And I’d appreciate any feedback on things that the app does well or poorly — you can reach me at alex@yaksoft.net

2023 Reflections

This year, I had to shut down my autonomous restaurant startup Mezli — despite a successful launch — after I was unable to raise the money we needed to scale up.

This year, I’ve also re-entered the world of AI, which I’ve been away from since leaving Stanford in 2020. I’m now working on a new workflow automation startup and have gotten very excited about the opportunities created by recent advances in AI.

However, the emergence of true artificial intelligence is also shaping up to be the fastest-ever large economic and social change in human history, and tracing how it may reshape business and society is both intellectually interesting and strategically important for entrepreneurs (and just about everyone else, for that matter).

While chewing on the lessons I’ve learned from shutting down a hardware startup and re-entering the world of AI, I’ve summarized my thoughts into three essays, which I hope can be useful to the startup community and spark an interesting conversation:

A summary of my arguments is as follows:

It’s likely that AI is now only a small leap away from becoming as good as humans at most intellectual tasks. Even if that’s not the case, its current capabilities are already good enough to do broad swathes of knowledge work that are currently done by humans.

So, we’re likely entering an era of knowledge work automation that may parallel the last 200 years of physical work automation, but it’ll likely happen much faster this time. We’ll likely see far fewer people doing analytical work over the next ~20 years, just as fewer and fewer people did agricultural and other manual work over the past ~200.

This automation of knowledge work is likely to reduce the software industry to a shadow of its former self, as programming will become largely automated.

However, there are many opportunities to build software startups right now, and software presents a uniquely good opportunity for first-time founders to build companies that reward good execution and provide quick feedback.

Hardware companies, on the other hand, are very difficult for first-time founders because their unavoidable capital requirements make them vulnerable to running out of money, and because their slow cycle times make for a slow education in entrepreneurship.

So, I highly encourage today’s aspiring entrepreneurs to focus on software, especially because we may be entering the last era when the software industry, and software entrepreneurship, exists in its present form.

That said, given the rapid changes in AI capabilities, I also think it’s especially important for today’s AI founders to keep an eye on the long-term competitive advantages their companies develop, as technology built on top of today’s AI may become infinitely easier to build on top of tomorrow’s AI, negating the technical defensibility of much of the work being done by startups today. Other forms of defensibility are likely to be more important in the future.

I welcome all comments, especially those that point out things I missed or refute a claim I’ve made. Hopefully we can all learn from the debate.

Founders, Beware Hardware

This essay is part of a 3-part series:

Hardware startups are sexy. Building flying cars, nuclear reactors, or electric cars is more tangible and in many ways more interesting than writing software. It’s also possible to tackle a wider range of important problems with hardware than with software alone, from global warming to food security.

For these reasons, many founders and would-be founders dream of starting hardware companies, and some of them actually do. Occasionally, that decision works out reasonably well, but most of the time, it ends in tears, especially for founders without previous successful outcomes. This is due to factors inherent to hardware companies, which the startup community is somewhat aware of but perhaps not enough. In this essay, I’m hoping to make those factors clear, with the hopes of saving other founders future pain.

This warning comes out of my own story.

I’m from a software background — I started programming as a teenager, studied computer science and AI in college and in grad school, and have worked in different parts of the software industry since high school. But in grad school, while studying AI, I became enamored with the idea of automating food service.

I realized that automating fast-casual food service (think Chipotle, Sweetgreen, etc.) could bring down the costs of building and operating restaurants by so much that high-quality meals could be sold at half the price they’re sold for today. To realize this vision, I co-founded Mezli with two friends from Stanford, and a year and a half later, we successfully launched a fully-robotic restaurant. However, despite selling thousands of meals at near-100% uptime and significantly better unit economics than human-powered restaurants, we were unable to raise more money and were forced to shut down.

Some of this was due to the downturn in the funding market — it was much harder to raise money in 2022 than it had been even a year or two earlier. But in hindsight, just being a hardware company made us much more fragile than an equivalent software startup would have been.

One crucial factor that I underestimated at the beginning of Mezli was the mandatory requirement for raising escalating amounts of capital to keep making progress with a hardware startup. People sometimes talk about hardware startups being “capital intensive”, but this is only part of the picture — plenty of software companies also invest hundreds of millions of dollars into engineering, sales, and marketing before they become profitable. The difference is that most software companies can adjust the amount of capital they invest in product development and growth as conditions change. If investor money becomes unavailable, a software company can usually lay people off and coast on revenues until the economy improves — or never fundraise again.

This is simply not possible for most early-stage hardware companies. Hardware products typically require substantial scale to reach profitability, which means huge up-front investments are necessary before turning a profit becomes possible. This means needing to raise numerous rounds of external funding, with each being do-or-die. The immediate implications of this dynamic are bad enough, but there are second-order impacts as well — even if an investor likes a hardware company’s prospects based on its technology and economics, well-founded concern that a single missed fundraise at any point in the future would be enough to kill the company can easily be a significant-enough factor to torpedo the current fundraise as well — a sort of “Keynesian beauty contest” dynamic that sounds academic but is very real.

Even a hardware success story like Tesla suffered from this dynamic and would have folded if not for repeated bailouts by Elon Musk. And for every Tesla, there have been countless hardware companies with equally-promising products but without the deep-pocketed backers needed to see them through lean times.

A second crucial drawback of hardware startups, which I think is often underappreciated, is the glacial speed of iteration compared to software. A software company can launch and update its product(s) and go-to-market motion near-instantaneously, allowing for very fast iteration. This is a huge advantage to a startup, allowing for fast contact with the market and evolution of its product(s) in the direction of customer pull — essentially the essence of the “Lean Startup” philosophy. Many software companies have found product-market fit and grown large on the back of this approach.

But the value of the ability to iterate quickly is not only to the benefit of the startup itself; it’s also to the benefit of its founders personally. When it’s possible to experiment on a daily cadence, to see efforts succeed or fail, and then to try again the next morning, the rate at which it’s possible to become a more-skillful entrepreneur is very high.

Hardware companies present a completely different picture than this when it comes to the speed of iteration. Hardware products typically take months or even years to design, manufacture, and ship, so that much more has to be guessed up-front that is only proven right or wrong by the market a long time later. And with only one or several iterations possible before the company proves a success or failure, the founders have limited opportunities to learn from the experience of bringing a product into contact with the real world. This slower learning curve is especially bad for first-time founders, who need to spend years learning lessons they would have learned in weeks with a software product.

And one final disadvantage of hardware is that even if a hardware product proves a huge success, the slow cycle times of hardware mean that scaling up that success and enjoying its fruits takes far longer than it would for a typical software company.

Of course, there are some exceptions to these rules — hardware companies where mandatory requirements for capital and long cycle times are less of a factor. A common example are companies that use off-the-shelf hardware to deliver a product differentiated by software. This includes companies that use off-the-shelf drones accompanied by computer vision models to inspect bridges and pipelines, companies that use AI models to get robot arms to pack boxes or weld metal, and many such others. By not having to design and build their own hardware, these companies are subject to fewer — but still many — of the issues that dog hardware startups.

Notably, both of the factors that make hardware startups a risky move for first-time entrepreneurs — capital requirements and slow learning curve — are less of a problem for repeat entrepreneurs or industry veterans. People who have amassed capital and experience by starting successful software companies or playing significant roles in existing hardware companies are often the best-positioned to tackle a hardware problem with a new startup.

And one-in-a-billion technical experts whose niche knowledge presents a necessary edge in a “hard tech” category may also find that it makes sense for them to start a hardware company. Such cases are especially common in biotech, but can also be seen in fields like nuclear energy and aerospace. For someone who’s spent twenty years becoming the world’s leading expert in a space like gene therapy or nuclear fusion, starting a company in that space can make sense — though that company will still be subject to the risks of high capital requirements and slow cycle times.

But for first-time generalist entrepreneurs without experience and capital, it’s hard to beat the advantages of software startups. There’s a reason why Silicon Valley startup culture came into being with the rise of the software industry, and why the vast majority of founders who go from nobodies to big successes make it big initially with software companies. (Dalton and Michael from YC made a great video about this.)

The software industry, and the startup ecosystem that’s part of it, are going to undergo big changes in the coming decades under the influence of AI, and may soon present fewer and fewer opportunities for entrepreneurs to make a mark. But until that happens, my advice to aspiring entrepreneurs who are looking for the most promising problems to work on is to strongly prefer founding a software company over a hardware company.

The End of the Software Industry

This essay is part of a 3-part series:

The AI revolution now taking place is poised to change the software industry in fundamental ways, and likely, to shrink the number of people employed in it. Historically, our industry has consisted of smart people painstakingly telling computers how to do specific tasks in the arcane languages that computers understand. We call this process writing software. AI is upending that model.

Already, generative AI tools have substantially increased the productivity of programmers, allowing for more niche or lower value software to be written than previously would have been justifiable economically. But even bigger changes are afoot.

We’re moving to a future where AI can conduct arbitrary information-processing tasks based on natural-language instructions from any reasonably intelligent human who understands the problem they’re trying to solve. Manually instructing the computers exactly what to do will no longer be necessary. Essentially, the aims of the no-code movement are coming into focus, though in a different way than might have been anticipated a few years ago — general-purpose AI models are making hand-coded Lego-style no-code tooling obsolete for many use cases.

What this means is that there are likely to be a lot fewer programmers soon than there used to be. There may, however, be an increasing number of people doing work along the lines of what product managers and salespeople currently do — understanding and solving customer problems, but with the details of the solutions being realized by the computers themselves instead of by teams of programmers.

This parallels the trajectory experienced by many other revolutionary industries, like railroads — after drawing in millions of people in an initial boom, it’s not uncommon for an industry to settle into a relatively stable long-term economic position while relying on fewer and fewer workers to sustain its position.

If the software industry experiences the same dynamic due to more and more of its work being done by AI, this development will also have significant implications for the startup ecosystem. The ability of smart, often young, people with little or no money to start businesses that grow to huge revenues in a decade or less has only really ever been possible in the software industry, which in turn has only existed in its current form for about 50 years. That era is likely to be coming to an end in the near future.

This is because a software startup is an organization in which a small group of smart people create new value in the market by building and selling new software — that is, by manually telling computers how to solve a specific human problem. As building software manually becomes less and less necessary, software startups are going to either change in fundamental ways or go extinct entirely. This extinction event, if it happens, will also take the venture capital industry down with the startup ecosystem that feeds it.

Of course, how long it takes until AI technology advances to the point where manual software engineering is as antiquated as programming in machine code is today remains to be seen — it could be next year; it could be 50 years from now. But it’s almost certainly coming this century, and even until the full extinction event is complete, the nature of what software is and what it does is going to be constantly changing.

This leaves software entrepreneurs, myself included, in a precarious position now. There is more value to be created with software right now than at any point in recent memory, perhaps ever. Trillions of dollars worth of business processes can now be automated, creating huge efficiencies, and trillions more will likely be possible in the next few years. Building and selling software to meet those needs still requires teams of highly-skilled people manually writing code, so there’s a bonanza taking shape in the startup ecosystem — many entrepreneurs are racking up eye-watering revenues with AI products this year, and I expect that to grow substantially in the near future.

But many of those startups are likely to be made obsolete almost as quickly as they came into existence. If AI technology advances to the point that a business need can be solved with little to no work on top of general-purpose AI models, startups’ products painstakingly built with manual effort on top of earlier generations of AI technology will quickly lose their value, and those companies will quickly bleed revenue and wither if they haven’t accumulated other advantages in the meantime.

Of course, it’s entirely possible that some companies founded in the current AI wave will build more durable assets than quickly-obsolete technology, like owning valuable data or setting themselves up as hard-to-circumvent intermediaries between other companies. But many others are likely to earn huge revenues for a few years by building and selling extremely valuable software, then see those revenues quickly dwindle to zero as their products become trivial to replace due to further advancements in AI.

I believe it’s wise for today’s AI entrepreneurs to take this possibility into account. In particular, the possibility of huge revenues up front, followed by a rapid decline, introduces a new dynamic into the cost/benefit calculus of fundraising. In the past, fundraising was usually necessary to get a software product off the ground, because of the significant amount of up-front engineering work normally required to build something that could be sold for meaningful revenue. However, once that revenue was achieved, it was generally durable, because differentiated and valuable business software does not usually become obsolete overnight. Thus, VC funding has been an appropriate source of capital for software companies, which have required risky up-front investment but which have then generated large and durable revenue streams, and commensurate liquidity events, if successful — providing payoffs down the road for both investors and founders.

This dynamic may be flipped for many of today’s AI companies. Many previously infeasible or impossible to solve high-value needs in business software are now suddenly solvable in weeks to months with a good team, thanks to recent advancements in AI, and those solutions can quickly generate hundreds of thousands or even millions of dollars in revenue. But that revenue is likely to only last for a few years until the underlying product becomes trivial to replace, unless the company selling the product creates a long-term competitive advantage in ways that are more resilient to rapid advancements in AI.

Unfortunately, that dynamic presents significant risks for founders taking venture capital. A founding team able to generate tens or hundreds of millions of dollars in revenue over 5-10 years with a product that then vanishes in a puff of smoke will see very different outcomes depending on whether or not it takes venture capital. Without external investment, those revenues, net of costs, will be the founders’ to keep. With external investment, the founders will be able to take a relatively meager salary, but will then be expected to plow those revenues into future growth, which may not be available if the product direction is obsolete. In this way, raising even small amounts of venture capital can turn a multimillion-dollar outcome into a zero for the founders involved.

My conclusion is that for teams building in today’s AI landscape, it may be wise to forego fundraising entirely, at least at first, and instead to focus on generating as much revenue as possible, as early as possible. In many cases, it may be possible to get to millions of dollars in revenues with a small, self-funded team, then swing for the fences and attempt to create a huge and durable company from there. But if that proves not to be possible, at least the founders of such a company will have a sizable, if temporary, profit stream to hedge the risk of a dead end.

And for the software founders involved, being able to guarantee personal financial security may be more important now than ever, as our ability to generate economic value is likely to decline or even vanish in the decades to come as AI becomes better at building software than we are. Now is the time to gather our rosebuds while we may — and to reduce the risk of a zero outcome if possible.

The End of Knowledge Work

This essay is part of a 3-part series:

For almost all of human history, the vast majority of people had to rely on their brains and brawn to survive, through hunting and gathering and then agriculture. Animal power provided some assistance, but things started to change much more fundamentally once people learned to harness non-biological energy: wind, water, and then most impactfully steam.

With the advent of more plentiful energy and the development of more capable mechanical technology, the world changed. Jobs that used to be done with muscle power became easier to do with machines, and the roles of people in society changed as well.

Up until the 19th century, most workers’ jobs were simply to make food. But over the following century, agriculture became highly mechanized, and the fraction of people in industrialized countries that worked as full-time farmers plummeted:

At first, this agricultural revolution freed people to become other kinds of manual workers — especially in manufacturing and transportation, where they made the wide variety of physical implements needed for the new industrialized economy, and moved them to where they were needed.

But over time, increasingly capable technology has caused the non-agricultural blue-collar part of the economy to require less and less labor as well. A factory that took 1000 people to run might now run with 30 technicians keeping an eye on the machines doing most of the work; a train that used to take 10 people to operate might now run with one.

Instead, in the industrialized world, people have increasingly become economically useful for their brains rather than for their brawn. A majority of US workers are now in white-collar fields, where they are paid for their knowledge and intellectual skills rather than for their strength and dexterity.

This process has had a wide range of effects on society. Physical strength and skill used to be a necessity for survival for most people. Now, an unusually strong American is as likely to be a hobbyist weightlifter as a manual laborer. Muscles used to be a tool; increasingly, they’ve become an ornament.

Instead, in developed countries like America, most people’s brains have become their most economically valuable assets. We’re becoming an economy where our interactions with the physical world are carried out by increasingly capable tools that require less and less physical effort from us — but where the management of an increasingly complicated world rests more and more on highly capable human brains.

Even our identities, to a significant degree, are tied up with our intelligence. We are homo sapiens, the wise human — we ascribe our preeminence as a species to our ability to understand and analyze the world around us.

It is that aspect of our existence that is now starting to change in the same way that our physical interactions with the world have been changing for the last 200 years.

Much like with draft animals before the Industrial Revolution, we’ve had some external assistance for our minds before now. Technology for communicating knowledge, from books to the Internet, has become more and more advanced over the centuries, giving us a longer and longer lever for our intellectual efforts. And computational technology, from abaci to today’s computers, has become more and more capable of doing routine but voluminous informational work quickly for us.

This has already changed the composition of the white-collar labor force: human “calculators” have been replaced by spreadsheets, and draftsmen by CAD programs. But bigger changes are now afoot.

We’ve reached a point in the development of artificial intelligence where its capabilities are extending into previously unimaginable territory. Computers used to need precise instructions for every task they could carry out, and they could only accept those instructions in their own arcane languages. Over the past year, this has changed — computers can now accept instructions in human language, written or spoken, and carry out those instructions successfully for even quite complex tasks.

Those capabilities are still far from perfect, but even if the abilities of AI were to be frozen at today’s level, white-collar work being performed by the full-time equivalent of many millions of people can, and will, be automated in the years to come using the technology that’s already available.

And future advances are inevitable as well. To date, most improvements in AI capabilities have come from deploying increasingly large amounts of computational power to train AI models on increasingly large amounts of data. Roughly speaking, every meaningful increase in AI capabilities has resulted from multiplying the amount of data and computational power used to train AI models by a factor of 10.

It’s likely that this approach will continue to bear fruit for at least one more “10x” cycle, which will likely take one to several more years. After that, data availability is likely to become a bottleneck — at that point, the latest AI models will have been trained on most extant codified human knowledge (books, the Internet, etc.) Feeding another “10x” cycle by synthesizing fake but realistic data, or gathering much more data from the real world, appears possible, but will likely be much slower than the current approach of using data that’s already available.

But in theory, it should also be possible to massively improve the data efficiency of training AI systems. Human brains have about 100 billion neurons, and humans can acquire a respectable education by learning information equivalent to the contents of a few hundred to a few thousand books. But state-of-the-art AI models, still far less capable than human brains, require many GPUs to train, and each GPU contains roughly as many transistors as the human brain contains neurons. And those state-of-the art models are being fed the equivalent of millions or even billions of books, far more than any human could ever read.

Thus, it seems likely that advances are possible both in the architectures and training methods we use to make AI models, as well as in the silicon hardware we train them on.

So, if and when significant advances in training efficiency happen, we’re going to be able to train much more capable AI models using the data, and likely the hardware, that’s already available. And soon enough — whether it’s in one year or twenty — the capabilities of AI in all analytical tasks are going to exceed those of most or all humans.

The consequences of this revolution — even extrapolating the capabilities of the technology that’s already available today, let alone that of more advanced systems — are going to be enormous, and are going to happen much more quickly than the Industrial Revolution. Unlike physical machines, AI technology can be deployed instantly to the whole world, meaning each advancement can be adopted as quickly as people can figure out how to use it, rather than being limited by the speed at which physical machines can be built and deployed.

What this implies is that much of the white-collar work that currently consists of analytical tasks — essentially, manipulating information — is going to be automated in the blink of an eye. A million people might work in data entry today — next year, that number might be zero. In a few years, when the capabilities of the technology advance, chemists or programmers might be similarly affected, and the process will continue until humans are only doing a small fraction of the analytical work that we do today.

In reality, in most lines of work, the AI won’t fully automate an entire profession — rather, it’ll reduce the amount of human effort required in that line of work by 10x, or 100x, or 1000x, just like what physical automation achieved in factories.

As a consequence, we’re going to see the raw information-processing abilities of human brains become less and less economically valuable. Technical specialists like programmers and traders, who work with self-contained purely-informational tasks, are going to see some of the biggest changes as soon as AI’s abilities exceed theirs. But most white-collar jobs contain a significant component of information processing, and are going to see that quickly handed over to the machines.

Interestingly, it seems that interpersonal relationship-oriented work is likely to be much less affected. Jobs ranging from bartender to bond salesperson rely heavily on interpersonal relationship-building, and much of the value people create in those jobs is dependent on them being people and not machines. In addition, high-end jobs that have as much to do with brokering ownership of valuable assets, power, and influence — think politicians, investment bankers, and influencers — are going to continue to be done by the people who care about accumulating that ownership, power, and influence, although those people are going to be increasingly assisted in their work by powerful AI, just as they are currently assisted by human workers.

Physical work will also be initially unaffected by the current revolution in AI — rescuing someone from a burning building or making a bed is still only possible with human hands. But the current AI revolution is increasingly looking like it’s going to unlock advances in robotics that will have a significant impact on the automation of physical work as well.

Currently, most physical-work automation is done by highly-specialized machines — walk into a reasonably-modern factory and you’ll see a number of large and expensive machines custom-tailored for jobs like bending rods or forming cans. This kind of special-purpose automation is only feasible for tasks where huge scale can be achieved in a single place — easier for tasks like manufacturing and agriculture than for tasks like house-cleaning and food-service, the need for which is naturally dispersed throughout the physical world and cannot be performed in a single place.

That said, mechanical technology is already advanced enough to make it possible to automate many of these highly-dispersed physical tasks that are still being done mostly by humans. The missing factor standing in the way of automating those tasks has rather been the intelligence available to the machines — robots have just not been smart enough to be able to do things like reliably navigate a house and manipulate a wide variety of objects.

That is all likely to change soon as the capabilities of AI continue to advance, and while the consequences will take longer to be realized in physical-world automation than with knowledge work, as they involve the manufacture and deployment of physical robots, the process will likely be faster than people expect.

This is because AI is going to unlock the wide use of a new type of automation: automation done by general-purpose hardware, whether that hardware takes the shape of humanoid robots, robot arms mounted on quadrupedal platforms, or something else entirely. And when one-size-fits-all robotic platforms powered by AI become broadly capable, they will be much cheaper and faster to manufacture and deploy than a heterogenous variety of specialized machines. This is exactly the same process thanks to which cars are now so cheap and ubiquitous — when you’re building a billion of something, you get a lot of efficiencies of scale. And when you’re building a billion general-purpose robots, which can do a wide variety of work thanks to AI, you can automate a lot of that work very quickly.

In this way, much of the work being done by people today — physical as well as analytical — is going to be increasingly done by machines. This process has already started, and is likely to accelerate over the next decade or two.

Where that leaves us humans in the economy is another question. It’s possible that a parallel process to the last 200 years may take place. Since industrialization, the physical abilities of people have become steadily less in-demand as machines have taken our places in manipulating the physical world, but through that process, the demand for our brains has only increased, causing more and more people to make a living by knowledge work as fewer and fewer make a living by physical work.

In a similar way, we may see less and less economic demand for our brains as analytical machines over the next decade or two, but to see more and more demand for our ability to relate to each other. In that kind of world, most of us will have jobs talking to each other about our needs and looking for ways to solve them — everyone a salesperson or therapist. In a world like that, brainpower will still be important, but more as a way to relate to each other than as a tool to solve complex puzzles. Incidentally, one theory of human evolution states that we evolved big brains as a result of social selection pressures that favored the survival and reproduction of those of us who were better at relating to other people — after a few hundred years of using our brains in more-analytical ways than they evolved for, we may return to a world in which our brains become useful primarily for the interpersonal tasks that they were honed by evolution to do.

But it’s also possible that AI will do such a good job at facilitating and even replacing human interactions and relationships in economic contexts (sales, customer support, etc.) that the number of humans needed for that kind of work will be far less than the number of people who will need to earn a living. In one sense, that’s a scary world — most of us will no longer be able to sustain a decent standard of living through the work we can do. But if that’s the way the world develops, the only politically feasible outcome I foresee, whether in democracies or dictatorships, is a welfare state where the work done to fulfill human needs is mostly done autonomously, and people are able to enjoy a high standard of living without working. Instead, most people would be able to spend their time on things that might not have paid the bills before, whether taking care of their families, competing at sports, traveling, painting, or any number of other things that mostly exist outside of the market economy today.

Which of those two scenarios comes to pass, or whether another one entirely will transpire, only time will tell. And whether the whittling-down of the need for knowledge work by humans takes half a decade or half a century will remain to be seen as well. But our need to work with our minds is about to go through the same winnowing process as our need to work with our bodies has experienced for the past 200 years — and it remains to be seen what the consequences will be.

How to talk to ChatGPT through Siri

Recently, I wrote the post How to: Talk to GPT-3 Through Siri, describing how to significantly upgrade Siri using OpenAI’s recent davinci-003 model.

The iOS shortcut in that post is already a big upgrade to Siri, but davinci-003 isn’t as capable as OpenAI’s latest models, which are also what is powering ChatGPT behind the scenes.

Until now, those models weren’t available for API access, but today, OpenAI opened API access to their gpt-3.5-turbo model, and I’ve updated the shortcut so you can now talk to the equivalent of ChatGPT directly through Siri.

gpt-3.5-turbo is about 10x cheaper to use than davinci-003, and seems to return better answers for some questions, but worse answers for others. Your call on which to use! I’ve switched to gpt-3.5-turbo, personally.

You can download the shortcut here – to start using it, you’ll just need to input your OpenAI API key. Then, talk to ChatGPT by saying “Hey Siri, GPT Mode” (or whatever you rename the shortcut to), then your question.

If you want to have Siri consistently read the responses out loud, it’s also best to change Siri’s settings in Settings->Accessibility->Siri->Spoken Responses to “Prefer Spoken Responses”:

If you want more detailed instructions on how to get this working, please see my previous post.

PS – No pressure, but if you’ve found this shortcut useful, I’d appreciate it if you buy me a coffee!

How To: Talk to GPT-3 through Siri

Note: it’s now possible to talk to a newer OpenAI model (gpt-3.5-turbo) through Siri – if you want to use the newer, and much cheaper, version, see my updated post here. The new version seems to do better on some questions but worse on others.

Like many others, I’ve been incredibly impressed with OpenAI’s ChatGPT and how far language models have come since I was working on natural language processing research a few years ago.

But, also like many others, I’ve been regularly frustrated with Apple’s Siri and how it often fails to give useful answers to even the most basic of questions. This week, after a few too many unsatisfying Siri answers in a row, I started to wonder if it would be possible to solve the problem once and for all by querying ChatGPT directly through Siri.

It turned out that while the ChatGPT API isn’t officially available yet to pose questions to programmatically, the recent GPT-3 model is, and it’s also very powerful.

It also turned out that several people have written Siri shortcuts to interface with GPT-3, but I had a couple of issues with them when I tried them out:

Siri wouldn’t always read the answers out loud
The answers often started with a stray question mark, which was distracting when Siri did read the answer out loud, starting with “Question mark…”

I tried fixing the first issue by making a shortcut that included steps to explicitly read the answer out loud, but it turned out that a far simpler solution was just to change Siri’s settings in Settings->Accessibility->Siri->Spoken Responses to “Prefer Spoken Responses”:

Make sure you turn on “Prefer Spoken Responses” if you want to use this shortcut and have Siri read the answers out loud to you!

The stray leading question marks were an easier fix – I just modified an existing shortcut with a step that strips them out.

With those two fixes, the shortcut started working very seamlessly – I can now tell my phone “Hey Siri, GPT Mode”, then a question, and quickly get a response from GPT-3 read back to me by Siri.

You can download the Siri shortcut here to add it to your phone (and you can see the original shortcut that I modified it from here).

The shortcut itself is free to use; you’ll just need to create an OpenAI account here, create an OpenAI API key and paste it into the text field in the shortcut that says “Replace this with your OpenAI API key!” You can see more detailed instructions for how to do this in this article explaining how to use a similar shortcut. Right now, OpenAI is offering three months of API credits for free when you sign up for an account, and they don’t charge much once you run out of your free credits.

Once you’ve installed the shortcut onto your phone and pasted your OpenAI API key into it, it’s ready to use! You can use it under the name I gave it (“GPT Mode”) or rename it to your taste, to anything that doesn’t conflict with Siri’s existing commands.

To use the shortcut, say “Hey Siri”, then “GPT Mode” (or whatever you renamed the shortcut to), then say whatever you want to ask GPT-3. Siri will then, after a delay, display the answer and read it out loud.

Much like with ChatGPT, the answers aren’t always right, but they usually are, and I’ve found that the ability to ask my phone complex questions and get reasonable answers back is incredibly useful as long as I keep the imperfections of GPT-3 in mind.

Hopefully, the day is coming soon when Siri will have this kind of functionality built in natively! In the meantime, there’s this Siri shortcut.

PS – No pressure, but if you’ve found this shortcut useful, I’d appreciate it if you buy me a coffee!

Crypto is an Unproductive Bubble

This post sparked a great conversation on Hacker News! See the comments here.
Another update: Scott Alexander just (12/8/22) wrote the best counterpoint to this that I’ve read – see it here

“The four most expensive words in investing are: ‘This time it’s different.'”
John Templeton

I’m writing this essay not so much to convince anyone of my point of view as to outline my current thinking around cryptocurrencies and the mania surrounding them. Because this is a controversial subject over which smart people disagree, I’m making these claims in public so that I might be proven either right or wrong later.

If you disagree with any of my claims here, or have more to add, I’d appreciate you leaving a comment with your thoughts, both for my benefit and for that of other readers.

Claim 1: Crypto is a Bubble (Confidence: High)

The hallmark of a bubble is people buying an asset primarily in the hopes of future appreciation driven by other buyers, rather than because of any notion of its long-term value. Cryptocurrencies have satisfied this criterion starting a number of years ago, and from what I’ve seen continue to do so. The trouble with bubbles is that once the supply of hopeful buyers runs out, prices stop rising and start falling as fear replaces greed. I predict that this will happen to all cryptocurrencies, including Bitcoin and Ethereum, within a decade. This goes doubly for double-bubble assets like NFTs.

Crypto boosters claim that cryptocurrency has long-term value as a digital currency, but I disagree for the reasons outlined in Claim 3.

Claim 2: Blockchain technology has no non-monetary applications (Confidence: High)

While this mania seems to have calmed down as of late, I’ve seen a number of claims that blockchain technology – i.e. the ability for a network of people to maintain a distributed ledger without trusting a central authority or authorities – unlocks non-monetary uses, e.g. supply chain transparency. However, any use of blockchain technology in which the ledger is not self-contained, but is instead tied to the physical world, has the fatal flaw that there can be no trust-free link between the ledger and the physical world. If you don’t trust anybody to enter a delivery of wheat into your ledger, then your supply-chain ledger will be empty, but if you do trust certain people, then you are relying on the honesty of specific people or on other safeguards outside the ledger itself, undermining the purpose of a distributed ledger.

Claim 3: Future monetary use of blockchain technology will be minor (Confidence: Medium)

This is where it gets more tricky. Blockchain technology does unlock an interesting use case, which is the ability of participants in a network to maintain a ledger, or record of holdings, without trusting a central authority. In theory, this approach could replace fiat currency, which is controlled and tracked by trusted authorities like banks. However, I see two major roadblocks to the widespread adoption of cryptocurrencies:

Inefficiency – Requiring every participant in a network to interact with a distributed ledger imposes substantial transaction costs which make cryptocurrency transactions more expensive than transactions in fiat currency, where the network is supported by trusted central authorities like banks and the government. To my knowledge, nobody has yet found a way to correct this flaw and it’s likely a feature of the way distributed ledgers work. If that’s the case, then cryptocurrency adoption will be limited to marginal use cases where fiat currency is impractical, e.g. online black-market transactions, since people prefer paying lower rather than higher transaction fees.
Control of monetary policy – When the world was on the gold standard and central banks had limited ability to control the money supply, extreme and frequent booms and busts were a major feature of the economy and a constant detriment to people’s lives and livelihoods. Fiat currencies administered by competent central bankers have the attractive feature that their governance can be used as a tool to cool excesses and calm panics when the credit cycle goes through its inevitable revolutions. Cryptocurrencies, to my understanding, dispense with this notion of central control on purpose, and their adoption as the primary currency in an economy would subject that economy to pre-fiat-era booms and busts or worse.

Unless both of these factors are somehow addressed, I predict that cryptocurrencies will play a marginal role in the future, if they exist at all. Even if they continue to grow in value as more speculators pile in, their usage as currency will be limited unless the inefficiency problem is solved. If that does somehow happen as well, we may see widespread use of cryptocurrencies in at least some countries, until a reckoning with the subsequent booms-and-bust dynamics, which may take decades, forces a return to fiat currency or its equivalent. (This could take the form of something which is a cryptocurrency in name only, with a “ledger” maintained by the government and banks.)

I also believe that it’s unlikely we’ll get even that far, as governments will not be keen to lose control over monetary policy. Authoritarian governments are likely to restrict or ban cryptocurrencies if they get much bigger than they are now, and even democratic governments will have to weigh voters’ enthusiasm for cryptocurrencies with the importance of being able to control monetary policy.

Some bubbles are caused by over-enthusiasm over genuinely innovative and productive assets, like the Railway Mania of the 1840s or the Internet Bubble of the 1990s. When those bubbles pop, they leave behind large amounts of investment in assets like rail and software that can be used for productive purposes later. (For more on this, see Carlota Perez’s Technological Innovations and Financial Capital, or a summary.) Other bubbles are triggered by enthusiasm over assets which have much less enduring value, like Tulip Mania, the South Sea Bubble, and Beanie Babies. It’s my prediction that the cryptocurrency bubble will turn out to have been another one of those unproductive bubbles, fueled by low interest rates and speculative enthusiasm from a wide base of retail investors rather than by a genuine advance in financial technology.

Regulating Resiliency in Supply Chains

COVID has exposed just how vulnerable to shocks the systems we depend on for physical goods are. Just about every good – from lumber to computer chips – has been subject to shortages, and each shortage has reverberated downstream through supply chains to cause shortages in many other goods, as with the millions of cars that aren’t being made because of chip shortages. All these shortages are having a meaningful effect on consumers’ quality of life and on firms’ ability to produce goods and conduct R&D – both a short-term and long-term hit to our economy and well-being.

Why are physical supply chains so fragile? Part of the reason is just that they’re so complex. A firm making a physical good likely sources components and tools from dozens, if not hundreds or thousands, of upstream suppliers. Often, many of those upstream suppliers are in a different country than the firm in question. It only takes an issue with one of those upstream suppliers – or with the ability to transport one of their products – to throw a firm’s operations off-kilter. In some cases, a shortage of a component or tool means scrambling to find an alternative supplier, but in some cases – as with chips – there may be no alternative, and the firm’s operations become limited by the quantity that a single supplier is able to deliver. Either way, production slows or stops, having cascading effects on the downstream customers that depend on the firm in question’s products.

So, any link in a supply chain can cause disproportionate harm to the entire supply chain relative to its own size. But in many cases, the links – firms producing physical goods – are actually incentivized to be efficient to the point of fragility. Many physical goods, whether toilet paper or lumber, are essentially fungible commodities in near-perfectly competitive markets, meaning firms producing those goods must eke out every possible efficiency to defend their already-thin margins. This means running production equipment 24/7 at full capacity and maintaining stocks of inputs and inventory that are as small as possible to minimize capital outlay and storage costs.

This is, of course, a fragile way to do things, but it’s often the only economically-viable one, and firms are forced to risk being unable to meet a once-a-decade crisis in order to preserve day-to-day profitability. The trouble is that when a firm is forced to slow down or shut down by such a crisis, only a small fraction of the economic costs of the shutdown are borne by the firm itself – many more are borne by the supply chain and economy within which the firm operates because of goods left unproduced and unused. (a negative externality, in economic terms)

There are also cases where a physical good, rather than being produced by many near-identical firms, is produced by a single firm, or only a few. This is the case with both computer chips and chip production equipment, with top-end supply dominated by TSMC in Taiwan and ASML Holding in the Netherlands respectively. Just as with the paper-thin operational slack in commodity goods, the concentration of production in goods like chips makes sense: for goods that are extremely expensive to produce in terms of fixed costs of intellectual property and physical infrastructure, a natural monopoly or oligopoly is the expected result.

The trouble is that when only one or a few firms are at a critical supply chain chokepoint, a single event can cause a worldwide disruption, as with the 2011 floods in Thailand that upended the hard drive industry. Moreover, firms operating expensive capital equipment are also incentivized to run at 100% capacity just like a toilet paper producer, meaning that there’s little room to absorb spikes in demand – as with TSMC struggling to meet chip demand during the COVID recovery.

These kinds of vulnerabilities might seem inevitable, but there is room to reduce them with regulation.

Another industry, finance, is concerned with moving money rather than physical goods, but has similar vulnerabilities – individual financial firms are also incentivized to operate in risky ways that make sense for each of them individually but not for the system as a whole, since any firm’s failure can be magnified through the whole financial system and real economy.

And indeed, the world of finance used to suffer from catastrophic booms and busts every decade or so, impacting the real economy of goods and services in major and painful ways. However, regulation in the last century has reduced, though not eliminated, the frequency of cascading failures in the financial system. A similar approach might be useful for physical supply chains.

Of course, there are limitations to the analogy. In a financial crisis, the government can inject money it creates out of thin air to improve liquidity. Unfortunately, the Fed is not able to materialize computer chips or toilet paper out of thin air in the same way that it can manifest new dollars. So, regulation for physical supply chains should be aimed at making them more resilient before a crisis strikes.

There are a few ways in which this can be done.

One is the “too big to fail” principle, or ensuring that no single firm is so big, or so much the exclusive supplier of some critical good, that its failure would cause the whole system to crumble. In addition to sheer scale, in physical supply chains, there is also the importance of international borders – even if there are many suppliers of a good, but all are located in a single foreign country, then it is that country’s government’s willingness to export that good that becomes a single failure point.

Thankfully, the US is already starting to wake up to this concern. Projects like the new Intel plant and new TSMC plant, both to be located in the US, are reducing vulnerability in chip supply chains. Similar projects, supported by the state both politically and with investment capital, could reduce vulnerabilities in other key supply chain chokepoints.

Another issue in physical supply chains is somewhat analogous to bank reserve requirements. Banks are incentivized to keep relatively little money on hand – what is not lent out is not earning interest. But low reserves increase the fragility of the whole financial system, since banks with low reserves are more vulnerable than those with ample ones, and a single bank failure can cascade through the whole system.

Similarly, firms producing physical goods in highly-competitive spaces are incentivized to maintain very low inventories of both inputs and finished goods, and very low if any excess production capacity. But just like with banks with small reserves, goods-producing firms with small inventories and small excess production capacities are vulnerable to crises and cannot meet bursts in demand. Just like with banks, this makes the whole system more fragile.

One can imagine the government stepping in and instituting similar “reserve requirements” for goods-producing firms as for banks. For example, firms over a certain size might be required to hold a larger inventory buffer of inputs and outputs on hand than would be economically rational, and to be subsidized by the government for the additional costs incurred, just like banks are paid interest by the government on their reserves.

In addition, the government might incentivize or require firms over a certain size to maintain excess production capacity. This could take the shape of a Federal program where firms were periodically required on short notice to produce an extra 20% of their annual production of a good within some number of months, which would then be bought at above-market rates by the government. That’s one example, but I’m sure those more familiar with industrial regulation could imagine even better ones.

Regulating companies producing physical goods is no easy matter, and any regulation that improves supply chain resiliency would impose costs on taxpayers. But if done properly, it could also reduce our vulnerability to future supply chain shocks and serve as a form of worthwhile insurance to our economy. As we navigate the COVID supply chain crisis, it’s worth considering how to reduce the severity of the next one.