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Startups
Built on the
ChatGPT API
Are Taking a
Huge Risk
Generative AI has emerged as a groundbreaking and captivating technology, capable of feats that have dominated press headlines and engaged a growing user base. Startups have raced to capitalize upon the fever-pitch surrounding ChatGPT and other generative AI models – and the recent release of OpenAI’s ChatGPT API has opened up the potential for countless niche products built on the back of the company’s super-powerful technology. But history shows longevity is uncertain – and risks plentiful – for companies too dependent on a closed-source API.
The ChatGPT API-based startup arena includes companies like Baselit (a so-called copilot for data analytics), Landbot (a no-code customer service chatbot builder), and BarriAI (APIs that allow you to build ChatGPT apps on the fly).
Many API-dependent companies of the past have suffered after having access revoked or usage costs suddenly increased. Thread Creator, a startup that offered users the ability to manage Twitter threads, which was unable to function when the company lost access to the Twitter API in 2021. Then there’s CanvasPop, which no longer connects with Meta-owned Instagram to print pictures straight from the app. Elon Musk recently announced a price hike for many users of Twitter’s API.
Users of the startups’ services also face risk, which could lead to regulatory or legal requirements to a degree that founders are not able to pivot to meet. The valuations of many companies rest on the value of data collected from users rather than simply the quality or demand of the product itself.
The problems with ChatGPT startups are multifold. For founders, building a product on top of another company’s proprietary model leaves developers vulnerable. “You’re dependent on somebody else’s platform and infrastructure, and then you have to pass that cost onto your own customers,” says Theo Priestley, author of “The Future Starts Now,” and long-time futurist,
If you use ChatGPT’s API, you pay based on a token system that reflects how “costly” it is to generate the text you need. This is important, considering that startups and developers using the API are held hostage if pricing shifts—which could trouble the overall competitiveness of industry pricing.
That issue melds with another, which is the replicability of ideas. “The ideas are ten a penny,” Priestley says.
What’s to stop another entrepreneur from taking a similar idea, improving it, and using it to launch something else—all while doing it cheaper? Alternatively, what’s to stop OpenAI from seeing demand for an API-dependent tool and simply replicating it as a feature for its own paying ChatGPT subscribers, effectively shuttering startups in that niche?
According to Dan Cunningham, CTO of AI-powered reputation management software Chatmeter, that market saturation is, in fact, possible to get past. “Startups have to ask themselves what value they are bringing, and where they can differentiate themselves,” Cunningham says. The emphasis here is on differentiation, or making ChatGPT API integration a slice of your offering, but not the whole pie. (Chatmeter has a new generative AI offering using the OpenAI Chat Completion API.)
[Generative AI chatbots] have become data hoovers very quickly and easily, and that’s worrying to me,” says Priestley.”
When startups rely too heavily on generated text as a substitute for human interaction, especially in cases where the AI’s influence isn’t readily disclosed, they run the risk of discomforting users and creating ethical concerns. California-based mental health nonprofit Koko rolled out a GPT-powered mental health support feature that it quickly shuttered due to ethical concerns. Koko co-founder Rob Morris wrote on Twitter about the shutdown. “We used a ‘co-pilot’ approach, with humans supervising the AI as needed,” he wrote. “Once people learned the messages were co-created by a machine, it didn’t work.”
Peter Relan, venture capitalist and founder of YouWeb Incubator, has experience investing in the generative AI space (including for Got It AI and MathGPT). “Failure is a distinct possibility,” Relan says, referring generally to investment in the generative AI space. He clarifies that there is broad-based investing going on at the seed stage, with investors hoping to find the winners in generative AI, much like the rest of the technology industry. However, with generative AI, the wheels are turning faster due to rapid advancement in the technology.
With all of these risks for founders, users, and investors, what are the alternatives to these kinds of API-based startups?
Aside from proprietary models using great training data sets, Relan says you can lean on open-source models or even fine-tuned LLMs for task-specific applications.
Relan says he sees that with his conversational AI startup Got It AI, which built its own model, ELMAR (Enterprise Language Model Architecture.) “It is a suite of on-premise guard-railed language models that can be fine-tuned to enterprise-specific tasks,” he says. He claims that this approach protects all parties involved while retaining the utility of generative AI.
While we have yet to see the full breadth of alternatives, startups entirely dependent on ChatGPT APIs might have a short-lived outlook. However, that doesn’t mean generative AI innovation is a moot point. In fact, development in the space is essential, but it’s crucial to do so in a way that caters to longevity and value to all stakeholders.
Cunningham, of Chatmeter, believes startups that fine-tune models for their own purposes can succeed. “Smart startups will be looking to these technologies for future development, choosing the right model for their particular use case, and in many cases training these models on their own data and needs.”
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The future is experiential rather than transactional; immersive storytelling using innovative, sensory-based showrooms that connect with consumers through emotional narratives”
The Future
Of Retail
Imagine walking into the flagship showroom of your favorite brand, where your senses are immediately enveloped in a captivating manifestation of the brand’s identity—an immersive encounter that lingers in your memory long after you’ve departed. Here, you’re not focused on the product, but the experience itself. It’s a magical, elusive, and surprising journey.
You explore the sights, sounds, smells, textures, and tastes without worrying about size, color, availability, or fit. You’re on a journey to connect with yourself, and the eventual sale is merely the culmination of the brand experience. It becomes a cherished memento—a receipt capturing an unforgettable encounter.
In the meantime, you allow yourself to be guided by a mix of artificial intelligence-driven virtual reality and high-tech production, innovative technology, and authentic sensory explorations, a narrative that promises a more fulfilling life. All that is required is a deeper relationship with the brand so that you can become part of the story.
Is A Sensory
For years, the media has incessantly spoke of the decline of brick-and-mortar retail, resulting in the inevitable death of malls. Fortunately, to borrow a phrase from Mark Twain, reports of the mall’s demise have been greatly exaggerated.
While online shopping has grown and evolved, it still accounts for less than 15% of total retail sales in the US. The over-expansion of retail in America was decades in the making, so even a significant collapse of our physical retail inventory could be explained as nothing more than a healthy, long-overdue correction.
America is dramatically over-retailed compared to other markets. According to the CoStar Group, a leading supplier of data analytics to the commercial real estate industry, America currently has approximately 42 sf of retail per capita, whereas the United Kingdom, a much more densely populated market than the rest of Europe, comes in at a mere 22sf/pp.
A reckoning was inevitable.
Experience
So, what's next?
The evolution of the retail store has always been in a constant state of flux. We’ve seen the evolution from general stores to department stores, malls, big box concepts, and e-commerce. We now find ourselves navigating the fractured world of social media and subscription services. So, what’s next?
Human beings are social creatures, evolved to live in communities. We are drawn to shared experiences, often choosing to watch a performance, athletic event, motion picture, or exhibition with others, even though we can now experience these events with even greater production value in the privacy and security of our own homes.
We are also sensory beings, suited to gathering input about the world around us through touch, taste, smell, sight, and sound. We desire experiences, not just possessions, and we are far more driven by our hearts than our heads. Traditional retail was designed around product distribution. Madison Avenue sold us the dream, and Main Street offered us the opportunity to see the product in person and try it out. But that’s changing.
The future of retail will be brand showrooms, not distribution centers. As overnight and even same-day shipping has become ubiquitous, merchandise can be delivered to your door within days or even hours, using regional distribution centers, so there is no need for overstock, and no reason to lug things home from a central location.
Stores will become experiential destinations, meticulously designed to invite you to try on a new lifestyle. The brand experience will combine high and low-tech, textures and scents with virtual reality, video, and sound. Tablet-wielding brand evangelists will guide you through the seamless process of completing the order, enabling contact-free payment and hassle-free delivery.
Beyond the inspiring technology and sensory displays, a significant expenditure will be devoted to brand ambassadors: highly-trained individuals capable of walking consumers through the brand experience, offering them a compelling emotional narrative and a satisfying sensory immersion. Think of actors putting on a show rather than mere salespeople. Cast members, not cashiers.
The fusion of immersive experience, cutting edge-tech and exceptional human interactions will set forth a new chapter in the evolution of retail.
Now imagine the best malls in the country, reinvented as awe-inspiring collections of flagship showrooms, featuring best-in-class brands. You plan your adventure using an app that schedules all your appointments and provides your digital dossier to the brand specialist in advance. Consequently, they receive you like the VIP customer you are, every time.
You depart these extraordinary spaces invigorated and satisfied, all your purchases sent to your home ahead of you, all your experiences captured in digital form to be shared with friends and family online, and all your likes and dislikes noted and cataloged for future reference.
Some might assume that traditional retail has run its course and is no longer practical when we can order anything in the world on our phones without leaving the house. But that ignores the vital human element, one that lies at the very core of human society. Time and again, we think we want the speed and efficiency of cold technology before realizing that we miss the unpredictable warmth of human culture and interaction.
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Who Cares
One of the major selling points of web3—at least among its most ardent proponents—is decentralization. Blockchain technology enables us to use smart contracts, build bankless assets, trade without friction, and much more.
Here’s the problem with the way we’re trying to sell web3 to people who aren’t already interested: Who cares? Who really cares about abstract-sounding concepts like identity ownership, bankless assets, and smart contracts?
About Web3?
From the conversations I’ve had with folks on the periphery (or way outside) of web3 spaces, the answer is: not your average person.
Let’s take decentralized finance as an example. The idea of bankless assets seems incredibly exciting to those of us on the inside of the discussion. Traditional banks fail consumers in many, many ways. Decentralized finance represents the future.
However, the majority of Americans are unable to cover a $1,000 emergency in cash.
You’re not going to get the average person excited about the idea of bankless assets, because the average person has no assets. Decentralized finance sounds meaningless to a person living paycheck-to-paycheck.
This is part of crypto’s current image problem. The far-reaching benefits of blockchain technology are exciting when you dig into the details, but the average person struggles to see how those benefits apply to their lives now when it matters to them.
The idealism behind these common selling points is good, and it’s something we need to foster (especially in light of scams, cryptocurrency collapses, and other high-profile scandals).
Instead, we need to start leveraging that idealism into creating and pointing out useful tools—not just using web3 projects to do web2 stuff. The more relevant we can make web3 to the average user, the faster we’ll be able to onboard the next 100 million users.
Part of that onboarding is showcasing how the blockchain can make everyone’s lives easier, using examples that people who aren’t interested in speculative finance can understand and relate to.
Take a simple activity, like trying to sell something online.
Under the current model, you might want to list that item on multiple sites to increase the likelihood that someone sees and buys it: eBay, Amazon, Craigslist. In doing so, you face a few risks that most online sellers are all too familiar with.
If two people try to buy it at the same time from different sites, you’re going to have at least one angry shopper on your hands. And there’s a risk that the person who says they’re buying it doesn’t have the money for it.
A smart contract eliminates those risks. You can list the item wherever you want and the smart contract ensures it can only be sold once. The smart contract checks the buyer’s wallet to make sure they have the assets to buy it. As soon as you scan the item at the UPS store or post office, the smart contract automatically validates the transaction and releases the funds.
No need to interact with a middleman. No need to monitor all your listings to update information about how much stock you have. No need to worry about scams.
Something this simple is still an enormous benefit of blockchain technology. And you might notice that it includes all the same selling points: decentralization, bankless assets, smart contracts, and frictionless trade.
Yet it’s an example that is far more likely to be relevant to the average person’s daily life. It’s more comprehensible than the idea of bankless assets on its own and showcases the concrete benefits of blockchain technology—not just the theoretical ones.
Crypto has an image problem, but one of the solutions is fairly simple: Meet people where they are. Show them how the blockchain can make their lives easier now and how it is relevant to their actual experiences.
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What Web3
Got
Wrong
The media cycles surrounding the 2017 ICO explosion and the 2021 NFT craze put the spotlight on web3 and made every CEO ask how their company could be in on the action. We’ve seen the world’s biggest brands, from Starbucks to Nike, invest heavily in web3. But even with the massive media hype and brand activations, web3 hasn’t caught on with the general consumer.
So what will it take for these technologies to finally find traction? The first is a shift from Asset Class to Experience Layer, and the second is a shift from On-Chain to OmniWeb.
A shift from Asset Class to Experience Layer
Most consumers only understand web3 through a simple number, and that is generally the price of Bitcoin. Web3 has unleashed a new asset class on the world, which has driven all of its hype to date, and is one of its biggest barriers to mass adoption.
Most consumers are afraid of crypto. It is hard, complex, and scary. Even when new applications make it easy to ‘on-ramp’ into crypto there is still the radical volatility of the asset class. Beyond these issues is the basic fact that most consumers don’t care about asset classes.
The focus of web3 as an asset layer is also problematic for brands. Most brands are now holding off on creating web3 tokens (NFTs) due to the unknown regulatory environment. They simply don’t want to be creating assets as that will likely classify them as a financial institution, taking them away from their core business and opening them up to a massive new world of regulation. The tiny market that would even care about these assets doesn’t even come close to justifying the risk of additional regulation.
A new asset class is good but comes with a large set of issues to drive mass adoption. To reach the masses we have to focus on what they care about. Consumers care about experiences. They want them to be faster, easier, and better. Web3 can do that!
Web3 can create a world where logging in is obsolete. A new study commissioned by Nordpass found the average person has over 100 passwords to remember across their digital lives! Yes, there are password management solutions like Nordpass or Onepassword to make this easier, but those are band-aids to the real problem. Identity is centralized and not extendable. Web3 can easily solve this by leveraging a tokenized identity layer where verified credentials are held by consumers. This could grant them instant access to services and could eliminate the need for logging in altogether, even eliminating the need for downloading the app in the first place.
This is not a “password solution” but rather a new way of verification. Apps use passwords and usernames as a way of proving you are who you say you are. Tokens can do that. Tokens can prove you are who you say you are, and services can use them rather than making users create a new username and password. Those tokens can also carry data, eliminating the need for the app to store data. Rather the consumers bring their data with them.
Beyond just being an easier way to log in, web3 can eliminate the need for apps and the creation of new profiles altogether as the tokens themselves can execute functions.
Imagine you have a car token given to you when you purchased the car. Now let’s say you want to do something with your car. From starting it to renting or selling it. Each of those would require at least one other application to be downloaded, a new profile to be set up, then have to manage all of those apps. This is how the web currently works, but in a tokenized (web3) world the tokens can be the applications themselves.
Progressive brands like Karma have already done this for their high-end electric cars. Each car is given a token, and that token is added to your wallet. You then open the token and execute these functions from there directly. No need to download another app, or sign up for a new service. Want to sell the car? Click a button in the token, put in your terms, and then simply pick where you want to sell the car. All of your information from your terms to the car’s data and history is then sent to those marketplaces, and you interact with them through the token itself. Making selling a car easier, faster, and more trusted as the data is all verified.
The biggest barrier to this kind of implementation of web3 technology is the focus on web3 as a new asset class rather than an infrastructure to help provide better digital experiences. We have expanded the aperture of how we think about tokens. Tokens are not just assets, they are also stores of data, and enable a new world of frictionless digital experiences to take place.
A shift from On-chain to “OmniWeb”
Web3, past being a speculative financial asset class, also has another big problem: the blockchain. The blockchain is a powerful database able to solve “double spend,” a potential risk to cryptocurrency in which a user might be able to spend the same digital currency twice. Through consensus mechanisms and the recording of transactions on a ledger, blockchain solves this flaw, enabling blockchain-based systems to be a transaction layer of assets and allowing for crypto to even exist. It’s a big deal! Despite this powerful feature, putting things on-chain comes with many issues that stifle the adoption of web3.
Putting things on-chain makes transactions public. This is good for solving transparency in financial situations, but it’s bad for a lot of other general use cases. For example, brands don’t want their entire customer database visible to their competition. Additionally, blockchains are not as fast or scaleable as other databases. Blockchains are designed to be a transaction layer, and they are great at that. They are not great at storing a lot of information. So a slow, public, and expensive database has very limited use cases, and most brands and consumers find it too scary to engage.
Web3 has to embrace a hybrid world where both on-chain and off-chain tokens work together. I call this “omniweb architecture,” where on-chain tokens can be extended via off-chain code and off-chain tokens can be leveraged by on-chain and off-chain applications.
Off-chain tokens, better known as “attestations” are exactly the same as the on-chain token – verifiable, decentralized, trusted, and programmable – however they come with the added benefit of no wallet being needed. They’re also private and are not seen by regulators as an asset. They can unlock the value of web3 with none of the hassles.
For example, Taylor Swift could issue an off-chain token to all of her fans who follow her on Twitter, Spotify, and have been to a concert in the last year. Smart Token Labs is an Australian web3 startup that I currently advise and which is currently working on this technology. Their CMO Brent Annells believes that fans want new ways to connect. “Fans want to be rewarded for being fans but in a way they value. They don’t want to be paid or have a digital asset they can sell. They want access and to be recognized by the artists as fans,” he said.
Verified fandom allows fans and artists to unlock a new era of fan experience.”
With modern web3 technology, there is no digital wallet and no new login required for the fan to receive the credential, just an email. Now the fan can prove anywhere that they are a top fan of Taylor Swift and new experiences can be unlocked. For example, brands can give rewards to fans directly on their site, and ticket sales can now be gated to allow superfans to get first dibs. In both of these situations, the token, not backend databases, is the integration point. This makes new experiences easier and faster to create and allows consumers to derive greater value from their online data.
Not all things require a blockchain, but all things can be better when leveraging web3 ideas and technology. Off-chain tokens are web3, and a great way to create new experiences that are frictionless while delivering on the core values of interoperability, decentralization, and ownership. For us to drive mass adoption we have to think past just on-chain and look towards an omniweb world.
The entire current conversation about web3 is focused on the financial aspects of the technology, and mostly that is simply the token prices of Bitcoin. This limited view is too scary to consumers and brands. When we let web3 fade into the background and leverage it to create better experiences both of those fears go away, and a greater value can emerge. To do this we also have to reduce the complexity of the experience, which means embracing off-chain tokens to expand on these use cases and further reduce consumer friction.
By shifting the focus from web3 as an asset layer to an experience layer, and embracing a wider understanding of web3 technology beyond just the blockchain, we can deliver to consumers truly what they want: a better digital experience.
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Before starting,
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- I hereby apply for membership with ON_Discourse. I agree to be bound by the rules of membership.
- All information shall be treated in confidence.
- Membership fees are subject to applicable taxes and services fees.
- While we are exclusive, we are not exclusionary. We do not discriminate on the basis of race, ethnicity, gender expression, age, sexual orientation, religion, or disability, but we are committed to ensuring that our members are aligned with our values.
The
Open
Source
AI
Revolution
In the late 20th century, the technology world witnessed a seismic shift as open-source Linux rose to prominence, challenging the dominance of proprietary operating systems from the era’s tech giants. Today, we are on the cusp of a similar revolution in the realm of AI, as open-source language models gain ground on their closed-source counterparts, such as those developed by Google and OpenAI.
In the 1990s, the UNIX ecosystem was dominated by proprietary solutions from major players like Sun Microsystems, IBM, and HP. These companies had developed sophisticated, high-performance systems tailored to the needs of their customers, and they maintained tight control over the source code and licensing. However, Linux, an open-source operating system created by Linus Torvalds, started gaining traction, ultimately disrupting the market.
The Linux revolution was propelled by three key factors: rapid community-driven innovation, cost-effectiveness, and adaptability. By embracing a decentralized development model built off the x86 personal computer, Linux empowered developers worldwide to contribute to its growth. This allowed it to evolve more quickly than its rivals and adapt to a diverse range of applications. Furthermore, Linux’s open-source nature made it significantly more cost-effective than proprietary alternatives, which relied on expensive licensing fees.
Fast-forward to the present day, and we are witnessing a similar upheaval in the AI landscape. The past two months have seen open-source AI projects such as EleutherAI GPT, Stanford Alpaca, Berkeley Koala, and Vicuna GPT, make rapid strides, closing the gap with closed-source solutions from giants like Google and OpenAI. Open-source AI models have become more customizable, more private, and pound-for-pound more capable than their proprietary counterparts. Their adoption has been fueled by the advent of powerful foundation models like Meta’s LLaMA, which was leaked to the public and triggered a wave of innovation.
The Linux saga offers important lessons for the AI community, as the similarities between the rise of Linux and the current open-source AI renaissance are striking. Just as Linux thrived on rapid community-driven innovation built off the backs of the x86 PC, open-source AI benefits from a global pool of developers and researchers who build upon each other’s work in a collaborative manner off the backs of gaming GPUs. This results in a breadth-first exploration of the solution space that far outpaces the capabilities of closed-source organizations.
Another parallel is the cost-effectiveness of open-source AI. Techniques such as low-rank adaptation (LoRA) have made it possible to fine-tune models at a fraction of the cost and time previously required. This has lowered the barrier to entry for AI experimentation, allowing individuals with powerful laptops to participate, driving further innovation.
Moreover, open-source AI models are highly adaptable. The same factors that make them cost-effective also make them easy to iterate upon and customize for specific use cases. This flexibility enables open-source AI to cater to niche markets and stay abreast of the latest developments in the field, much like Linux did with diverse applications.
The implications of this open-source AI revolution are profound, especially for closed-source organizations like Google and OpenAI. As the quality gap between proprietary and open-source models continues to shrink, customers will increasingly opt for free, unrestricted alternatives. The experience of proprietary UNIX-based systems in the face of Linux’s rise serves as a stark reminder of the perils of ignoring this trend. In fact, with image generation bots, OpenAI’s Dall-E and Google’s various closed models are barely a point of discussion as the world flocked to open Stable Diffusion models.
To avoid being left behind, closed-source AI organizations must adapt their strategies. Embracing the open-source ecosystem, collaborating with the community, and facilitating third-party integrations are crucial steps. By doing so, these organizations can position themselves as leaders in the AI space, shaping the narrative on cutting-edge ideas and technologies. Companies like Replit, MosaicML, Together.xyz, and Cerebras are doing just that, releasing open-source models, but offering services, finetuning, or operations as a service instead.
“ The implications of this open-source AI revolution are profound, especially for closed-source organizations like Google and OpenAI. As the quality gap between proprietary and open-source models continues to shrink, customers will increasingly opt for free, unrestricted alternatives.”
The flip side of the argument is that this is only possible for a certain model size. There are many emergent behaviors that have only been witnessed on the largest models. While open-source AIs that are an order of magnitude smaller than GPT-3 have already surpassed GPT-3’s quality, this does not necessarily apply to models of the scale of GPT-4 and beyond. With continued scaling in sequence length, parameter count, and training data set sizes, it is possible the gap between open-source and closed-source widens again.
Furthermore, while models are free to use, services that are built on top will still require significant investments. Google, Microsoft, and Meta are able to build these closed-source services for use in people’s everyday lives due to the moat of their platforms. Lastly, the cost of inference is a significant barrier given most consumer devices do not have the horsepower required for models larger than 7 billion parameters (GPT-3 is 175 billion parameters, GPT-4 is over 1 trillion), and it is possible that only the largest organizations can afford to scale their model out to billions of users.
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The
Shift from
Knowledge
Work
to
Direction
Work
The worst piece of advice you could give today to a college freshman hoping to work in tech is to tell them to major in computer science, math, or engineering. Same for coding, which is about to go from being a surefire way into the industry to immaterial.
Automation has been replacing manual labor for decades and now artificial intelligence is ready to take over the bulk of knowledge work. We are on the precipice of a great shift that will drastically change which workers will be the most valuable recruits.
Knowledge workers who, at the turn of the century, were described as the most important workers within a modern, thriving organization, will be replaced by what we’re calling direction workers. The evolution in technology isn’t so much going to eliminate high-end human jobs, it’s going to change what high-end human jobs look like and require.
But the shifts in these sectors do not just show AI replacing human skills. They show a need for a new kind of human skill set. This is where the direction worker comes in.
For the last 60 years, knowledge work has been used to describe a kind of intellectual work that demands a high degree of specialization or training, and the ability to perform non-routine tasks like problem-solving, analysis, decision-making, and the creation of new information. Knowledge workers were the upper crust of all white-collar workers: financial analysts, architects, lawyers, data scientists, and engineers.
That was before.
Across many disciplines, knowledge work is already being replaced. In the financial sector, AI systems are able to analyze vast amounts of data and make sophisticated investment decisions. In healthcare, AI systems are able to diagnose medical conditions and recommend treatments with a high degree of accuracy. AI systems don’t take days off; they do not call in sick. They can work 24 hours a day.
But the shifts in these sectors do not just show AI replacing human skills. They show a need for a new kind of human skill set. This is where the direction worker comes in.
I use “direction” not so much to convey the management work of a director in a company but more to refer to the literal act of directing, as in instructing or conducting. It could just as easily be called “Instruction Work.”
The image of an orchestra conductor comes to mind, expertly guiding musicians and instruments to produce the right sound. The image of a NASCAR driver may even be more appropriate. The engine may be beautiful, but it won’t win the race without the expertise, the direction, of its driver.
In finance just as in healthcare, human workers are needed to provide direction to AI systems even as they are no longer required to crunch the data themselves. On the tail end, human workers also need to evaluate the results, use critical, lateral thinking, and offer follow-up instructions.
Ferenc Huszár, a machine learning professor at the University of Cambridge, tweeted last year that the current version of OpenAI’s large language model, ChatGPT, would be a good teaching tool in mathematics, precisely because its answers are sometimes wrong. “Give it a problem, it generates convincing-looking but potentially bullsh*t answer, ask the student if they are convinced by the response,” he wrote.
What Huszár is suggesting here to me is not just teaching students to simply produce an accurate answer, but to develop an ability to go past the appearance of a fact and, with a critical eye, evaluate whether it is indeed accurate. If it isn’t, that eye needs to figure out why not, edit the original question, and do it all over again.
As systems progress, there may be less need for correction and editing, but the need for direction will not disappear. Ever-improving technologies will only call for more excellent direction.
Where to find and how to train these direction workers then becomes the question.
I am not sold on telling young people to just go to business school. Sure, we need a good generation of leaders who understand how to manage this new landscape, but more than managers we need critical thinkers who can ask the right questions, look for blind spots, understand connections, and have the creativity and humility to rethink the problem at its end and its base.
Direction workers are likely going to be people who can juggle different skill sets all at once: dual majors in math and anthropology PhDs who have trained quantitatively and qualitatively, journalism majors who work with Python, law school graduates willing to engage with the practicalities of coding and ethics. In short, we are going to need what David Epstein called “generalists” in his best-selling book, Range.
Realizing that to be competitive in the marketplace in the next ten years is going to look totally different than it did in the last ten is not just something young professionals need to do. Those of us in business should also be paying attention: The biggest cost to businesses over the next decade will be hiring the wrong people with the wrong skill sets.
As Max Penk put it in a post on LinkedIn earlier this year:
“Good news: AI will not replace you. Bad news: a person using AI will.”
Do you agree with this?
Do you disagree or have a completely different perspective?
We’d love to know