In the first approach, the primary interface affordance is an input that directly (for the most part) instructs an AI model(s). In this paradigm, people are authoring prompts that result in text, image, video, etc. generation. These prompts can be sequential, iterative, or un-related. Marquee examples are OpenAI's ChatGPT interface or Midjourney's use of Discord as an input mechanism. Since there are few, if any, UI affordances to guide people these systems need to respond to a very wide range of instructions. Otherwise people get frustrated with their primarily hidden (to the user) limitations.

The second approach doesn't include any UI elements for directly controlling the output of AI models. In other words, there's no input fields for prompt construction. Instead instructions for AI models are created behind the scenes as people go about using application-specific UI elements. People using these systems could be completely unaware an AI model is responsible for the output they see.

The third approach is application specific UI with AI assistance. Here people can construct prompts through a combination of application-specific UI and direct model instructions. These could be additional controls that generate portions of those instructions in the background. Or the ability to directly guide prompt construction through the inclusion or exclusion of content within the application. Examples of this pattern are Microsoft's Copilot suite of products for GitHub, Office, and Windows.

they could be overlays, modals, inline menus and more. What they have in common, however, is that they supplement application specific UIs instead of completely replacing them.

In the near future, transforming text will become as commonplace as filtering images. A new set of tools is emerging, like Photoshop for text. Up until now, text editors have been focused on input. The next evolution of text editors will make it easy to alter, summarize and lengthen text. You’ll be able to do this for entire documents, not just individual sentences or paragraphs. The filters will be instantaneous and as good as if you wrote the text yourself. You will also be able to do this with local files, on your device, without relying on remote servers.

Initially, many of Photoshop’s capabilities were adaptations of analog effects. For example, “dodge” and “burn” are old darkroom techniques used to alter photographs. There are countless skeuomorphic names throughout digital image editing tools that refer to analog processes.

Text seems like it would be easier to manipulate than images. But languages have far more rules than images do. A reader expects writing to follow proper spelling and grammar, a consistent tone, and a logical sequence of sentences. Until now, solving this problem required building complex rule-based algorithms. Now we can solve this problem with AI models that can teach themselves to create readable text in any language.

With iA Writer 7 you can manually mark ChatGPT’s contributions as AI text. AI text is greyed out. This allows you to separate and control what you borrow and what you type. By splitting what you type and what you pasted, you can make sure that you speak your mind with your voice, rhythm and tone.

As a dialog partner AI makes you think more and write better. As ghost writer it takes over and you lose your voice. Yet, sometimes it helps to paste its replies and notes. And if you want to use that information, you rewrite it to make it our own. So far, in traditional apps we are not able to easily see what we wrote and what we pasted from AI. iA Writer lets you discern your words from what you borrowed as you write on top of it. As you type over the AI generated text you can see it becoming your own. We found that in most cases, and with the exception of some generic pronouns and common verbs like “to have” and “to be”, most texts profit from a full rewrite.

we believe that using AI for writing will likely become as common as using dishwashers, spellcheckers, and pocket calculators. The question is: How will it be used? Like spell checkers, dishwashers, chess computers and pocket calculators, writing with AI will be tied to varying rules in different settings.

We suggest using AI’s ability to replace thinking not for ourselves but for writing in dialogue. Don’t use it as a ghost writer. Because why should anyone bother to read what you didn’t write? Use it as a writing companion. It comes with a ChatUI, so ask it questions and let it ask you questions about what you write. Use it to think better, don’t become a vegetable.

This paper maps concepts from AI alignment onto a basic, three step interaction cycle, yielding a corresponding set of alignment objectives: 1) specification alignment: ensuring the user can efficiently and reliably communicate objectives to the AI, 2) process alignment: providing the ability to verify and optionally control the AI's execution process, and 3) evaluation support: ensuring the user can verify and understand the AI's output.

the notion of a Process Gulf, which highlights how differences between human and AI processes can lead to challenges in AI control.

While there were undoubtedly people who came in for the food, worked 3 hours a day, and enjoyed their early retirements, all the people I met were earnest, hard-working, and wanted to do great work. What beat them down were the gauntlet of reviews, the frequent re-orgs, the institutional scar tissue from past failures, and the complexity of doing even simple things on the world stage. Startups can afford to ignore many concerns, Googlers rarely can. What also got in the way were the people themselves - all the smart people who could argue against anything but not for something, all the leaders who lacked the courage to speak the uncomfortable truth, and all the people that were hired without a clear project to work on, but must still be retained through promotion-worthy made-up work.

Another blocker to progress that I saw up close was the imbalance of a top heavy team. A team with multiple successful co-founders and 10-20 year Google veterans might sound like a recipe for great things, but it’s also a recipe for gridlock. This structure might work if there are multiple areas to explore, clear goals, and strong autonomy to pursue those paths.

Good teams regularly pay down debt by cleaning things up on quieter days. Just as real is process debt. A review added because of a launch gone wrong. A new legal check to guard against possible litigation. A section added to a document template. Layers accumulate over the years until you end up unable to release a new feature for months after it's ready because it's stuck between reviews, with an unclear path out.

Many of the most pressing concerns around AI safety will come with these features, whenever they arrive. The fear is that when you tell AI systems to do things on your behalf, they might accomplish them via harmful means. This is the fear embedded in the famous paperclip problem, and while that remains an outlandish worst-case scenario, other potential harms are much more plausible.Once you start enabling agents like the ones OpenAI pointed toward today, you start building the path toward sophisticated algorithms manipulating the stock market; highly personalized and effective phishing attacks; discrimination and privacy violations based on automations connected to facial recognition; and all the unintended (and currently unimaginable) consequences of infinite AIs colliding on the internet.

That same Copy Editor I described above might be able in the future to automate the creation of a series of blogs, publish original columns on them every day, and promote them on social networks via an established daily budget, all working toward the overall goal of undermining support for Ukraine.

Which actions is OpenAI comfortable letting GPT-4 take on the internet today, and which does the company not want to touch?  Altman’s answer is that, at least for now, the company wants to keep it simple. Clear, direct actions are OK; anything that involves high-level planning isn’t.

For most of his keynote address, Altman avoided making lofty promises about the future of AI, instead focusing on the day-to-day utility of the updates that his company was announcing. In the final minutes of his talk, though, he outlined a loftier vision.“We believe that AI will be about individual empowerment and agency at a scale we've never seen before,” Altman said, “And that will elevate humanity to a scale that we've never seen before, either. We'll be able to do more, to create more, and to have more. As intelligence is integrated everywhere, we will all have superpowers on demand.”

Fundamentally, Diffusion Models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process. After training, we can use the Diffusion Model to generate data by simply passing randomly sampled noise through the learned denoising process.

We still believe that there will be a separation between the “application layer” companies and foundation model providers, with model companies specializing in scale and research and application layer companies specializing in product and UI. In reality, that separation hasn’t cleanly happened yet. In fact, the most successful user-facing applications out of the gate have been vertically integrated.

We predicted that the best generative AI companies could generate a sustainable competitive advantage through a data flywheel: more usage → more data → better model → more usage. While this is still somewhat true, especially in domains with very specialized and hard-to-get data, the “data moats” are on shaky ground: the data that application companies generate does not create an insurmountable moat, and the next generations of foundation models may very well obliterate any data moats that startups generate. Rather, workflows and user networks seem to be creating more durable sources of competitive advantage.

Some of the best consumer companies have 60-65% DAU/MAU; WhatsApp’s is 85%. By contrast, generative AI apps have a median of 14% (with the notable exception of Character and the “AI companionship” category). This means that users are not finding enough value in Generative AI products to use them every day yet.

generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value. As our colleague David Cahn writes, “the $200B question is: What are you going to use all this infrastructure to do? How is it going to change people’s lives?”

A person can’t mimic another voice perfectly (impressionists don’t have to pay licence fees) but they can listen to a thousand hours of music and make something in that style - a ‘pastiche’, we sometimes call it. If a person did that, they wouldn’t have to pay a fee to all those artists, so if we use a computer for that, do we need to pay them?

I think most people understand that if I post a link to a news story on my Facebook feed and tell my friends to read it, it’s absurd for the newspaper to demand payment for this. A newspaper, indeed, doesn’t pay a restaurant a percentage when it writes a review.

one way to think about this might be that AI makes practical at a massive scale things that were previously only possible on a small scale. This might be the difference between the police carrying wanted pictures in their pockets and the police putting face recognition cameras on every street corner - a difference in scale can be a difference in principle. What outcomes do we want? What do we want the law to be? What can it be?

OpenAI hasn’t ‘pirated’ your book or your story in the sense that we normally use that word, and it isn’t handing it out for free. Indeed, it doesn’t need that one novel in particular at all. In Tim O’Reilly’s great phrase, data isn’t oil; data is sand. It’s only valuable in the aggregate of billions,, and your novel or song or article is just one grain of dust in the Great Pyramid.

it’s supposed to be inferring ‘intelligence’ (a placeholder word) from seeing as much as possible of how people talk, as a proxy for how they think.

it doesn’t need your book or website in particular and doesn’t care what you in particular wrote about, but it does need ‘all’ the books and ‘all’ the websites. It would work if one company removed its content, but not if everyone did.

What if I use an engine trained on the last 50 years of music to make something that sounds entirely new and original? No-one should be under the delusion that this won’t happen.

I can buy the same camera as Cartier-Bresson, and I can press the button and make a picture without being able to draw or paint, but that’s not what makes the artist - photography is about where you point the camera, what image you see and which you choose. No-one claims a machine made the image.

Spotify already has huge numbers of ‘white noise’ tracks and similar, gaming the recommendation algorithm and getting the same payout per play as Taylor Swift or the Rolling Stones. If we really can make ‘music in the style of the last decade’s hits,’ how much of that will there be, and how will we wade through it? How will we find the good stuff, and how will we define that? Will we care?

With the comprehensive application of Artificial Intelligence into the creation and post production of images, it seems questionable if the resulting visualisations can still be considered ‘photographs’ in a classical sense – drawing with light. Automation has been part of the popular strain of photography since its inception, but even the amateurs with only basic knowledge of the craft could understand themselves as author of their images. We state a legitimation crisis for the current usage of the term. This paper is an invitation to consider Synthography as a term for a new genre for image production based on AI, observing the current occurrence and implementation in consumer cameras and post-production.

The proliferation of AI-generated images in online environments won’t eradicate human art wholesale, but it does represent a reshuffling of the market incentives that help creative economies flourish. Like the college essay, another genre of human creativity threatened by AI usurpation, creative “products” might become more about process than about art as a commodity.

Are artists using computer software on iPads to make seemingly hand-painted images engaged in a less creative process than those who produce the image by hand? We can certainly judge one as more meritorious than the other but claiming that one is more original is harder to defend.

An understanding of the technology as one that separates human from machine into distinct categories leaves little room for the messier ways we often fit together with our tools. AI-generated images will have a big impact on copyright law, but the cultural backlash against the “computers making art” overlooks the ways computation has already been incorporated into the arts.

The problem with debates around AI-generated images that demonize the tool is that the displacement of human-made art doesn’t have to be an inevitability. Markets can be adjusted to mitigate unemployment in changing economic landscapes. As legal scholar Ewan McGaughey points out, 42% of English workers were redundant after WWII — and yet the U.K. managed to maintain full employment.

Contemporary critics claim that prompt engineering and synthography aren’t emergent professions but euphemisms necessary to equate AI-generated artwork with the work of human artists. As with the development of photography as a medium, today’s debates about AI often overlook how conceptions of human creativity are themselves shaped by commercialization and labor.

Others looking to elevate AI art’s status alongside other forms of digital art are opting for an even loftier rebrand: “synthography.” This categorization suggests a process more complex than the mechanical operation of a picture-making tool, invoking the active synthesis of disparate aesthetic elements. Like Fox Talbot and his contemporaries in the nineteenth century, “synthographers” maintain that AI art simply automates the most time-consuming parts of drawing and painting, freeing up human cognition for higher-order creativity.

Separating human from camera was a necessary part of preserving the myth of the camera as an impartial form of vision. To incorporate photography into an economic landscape of creativity, however, human agency needed to ascribe to all parts of the process.

Consciously or not, proponents of AI-generated images stamp the tool with rhetoric that mirrors the democratic aspirations of the twenty-first century.

Stability AI took a similar tack, billing itself as “AI by the people, for the people,” despite turning Stable Diffusion, their text-to-image model, into a profitable asset. That the program is easy to use is another selling point. Would-be digital artists no longer need to use expensive specialized software to produce visually interesting material.

Meanwhile, communities of digital artists and their supporters claim that the reason AI-generated images are compelling at all is because they were trained with data sets that contained copyrighted material. They reject the claim that AI-generated art produces anything original and suggest it instead be thought of as a form of “twenty-first century collage.”

Erasing human influence from the photographic process was good for underscoring arguments about objectivity, but it complicated commercial viability. Ownership would need to be determined if photographs were to circulate as a new form of property. Was the true author of a photograph the camera or its human operator?

By reframing photographs as les dessins photographiques — or photographic drawings, the plaintiffs successfully established that the development of photographs in a darkroom was part of an operator’s creative process. In addition to setting up a shot, the photographer needed to coax the image from the camera’s film in a process resembling the creative output of drawing. The camera was a pencil capable of drawing with light and photosensitive surfaces, but held and directed by a human author.

Establishing photography’s dual function as both artwork and document may not have been philosophically straightforward, but it staved off a surge of harder questions.

Human intervention in the photographic process still appeared to happen only on the ends — in setup and then development — instead of continuously throughout the image-making process.

Google is trying to kill the 10 blue links. Twitter is being abandoned to bots and blue ticks. There’s the junkification of Amazon and the enshittification of TikTok. Layoffs are gutting online media. A job posting looking for an “AI editor” expects “output of 200 to 250 articles per week.” ChatGPT is being used to generate whole spam sites. Etsy is flooded with “AI-generated junk.” Chatbots cite one another in a misinformation ouroboros. LinkedIn is using AI to stimulate tired users. Snapchat and Instagram hope bots will talk to you when your friends don’t. Redditors are staging blackouts. Stack Overflow mods are on strike. The Internet Archive is fighting off data scrapers, and “AI is tearing Wikipedia apart.”

it’s people who ultimately create the underlying data — whether that’s journalists picking up the phone and checking facts or Reddit users who have had exactly that battery issue with the new DeWalt cordless ratchet and are happy to tell you how they fixed it. By contrast, the information produced by AI language models and chatbots is often incorrect. The tricky thing is that when it’s wrong, it’s wrong in ways that are difficult to spot.

The resulting write-up is basic and predictable. (You can read it here.) It lists five companies, including Columbia, Salomon, and Merrell, along with bullet points that supposedly outline the pros and cons of their products. “Columbia is a well-known and reputable brand for outdoor gear and footwear,” we’re told. “Their waterproof shoes come in various styles” and “their prices are competitive in the market.” You might look at this and think it’s so trite as to be basically useless (and you’d be right), but the information is also subtly wrong.

It’s fluent but not grounded in real-world experience, and so it takes time and expertise to unpick.

While open access is a cornerstone of Wikipedia’s design principles, some worry the unrestricted scraping of internet data allows AI companies like OpenAI to exploit the open web to create closed commercial datasets for their models. This is especially a problem if the Wikipedia content itself is AI-generated, creating a feedback loop of potentially biased information, if left unchecked.

This is reflected in their name: a “language model” implies that they are tools for working with language. That’s what they’ve been trained to do, and it’s language manipulation where they truly excel. Want them to work with specific facts? Paste those into the language model as part of your original prompt! There are so many applications of language models that fit into this calculator for words category: Summarization. Give them an essay and ask for a summary. Question answering: given these paragraphs of text, answer this specific question about the information they represent. Fact extraction: ask for bullet points showing the facts presented by an article. Rewrites: reword things to be more “punchy” or “professional” or “sassy” or “sardonic”—part of the fun here is using increasingly varied adjectives and seeing what happens. They’re very good with language after all! Suggesting titles—actually a form of summarization. World’s most effective thesaurus. “I need a word that hints at X”, “I’m very Y about this situation, what could I use for Y?”—that kind of thing. Fun, creative, wild stuff. Rewrite this in the voice of a 17th century pirate. What would a sentient cheesecake think of this? How would Alexander Hamilton rebut this argument? Turn this into a rap battle. Illustrate this business advice with an anecdote about sea otters running a kayak rental shop. Write the script for kickstarter fundraising video about this idea.

A flaw in this analogy: calculators are repeatable Andy Baio pointed out a flaw in this particular analogy: calculators always give you the same answer for a given input. Language models don’t—if you run the same prompt through a LLM several times you’ll get a slightly different reply every time.

Software is misunderstood. It can feel like a discrete thing, something with which we interact. But, really, it is the intrusion into our world of something very alien. It is the strange interaction of electricity, semiconductors, and instructions, all of which somehow magically control objects that range from screens to robots to phones, to medical devices, laptops, and a bewildering multitude of other things. It is almost infinitely malleable, able to slide and twist and contort itself such that, in its pliability, it pries open doorways as yet unseen.

the clearing price for software production will change. But not just because it becomes cheaper to produce software. In the limit, we think about this moment as being analogous to how previous waves of technological change took the price of underlying technologies—from CPUs, to storage and bandwidth—to a reasonable approximation of zero, unleashing a flood of speciation and innovation. In software evolutionary terms, we just went from human cycle times to that of the drosophila: everything evolves and mutates faster.

A software industry where anyone can write software, can do it for pennies, and can do it as easily as speaking or writing text, is a transformative moment. It is an exaggeration, but only a modest one, to say that it is a kind of Gutenberg moment, one where previous barriers to creation—scholarly, creative, economic, etc—are going to fall away, as people are freed to do things only limited by their imagination, or, more practically, by the old costs of producing software.

We have almost certainly been producing far less software than we need. The size of this technical debt is not knowable, but it cannot be small, so subsequent growth may be geometric. This would mean that as the cost of software drops to an approximate zero, the creation of software predictably explodes in ways that have barely been previously imagined.

Entrepreneur and publisher Tim O’Reilly has a nice phrase that is applicable at this point. He argues investors and entrepreneurs should “create more value than you capture.” The technology industry started out that way, but in recent years it has too often gone for the quick win, usually by running gambits from the financial services playbook. We think that for the first time in decades, the technology industry could return to its roots, and, by unleashing a wave of software production, truly create more value than its captures.

Software production has been too complex and expensive for too long, which has caused us to underproduce software for decades, resulting in immense, society-wide technical debt.

technology has a habit of confounding economics. When it comes to technology, how do we know those supply and demand lines are right? The answer is that we don’t. And that’s where interesting things start happening. Sometimes, for example, an increased supply of something leads to more demand, shifting the curves around. This has happened many times in technology, as various core components of technology tumbled down curves of decreasing cost for increasing power (or storage, or bandwidth, etc.).

Suddenly AI has become cheap, to the point where people are “wasting” it via “do my essay” prompts to chatbots, getting help with microservice code, and so on. You could argue that the price/performance of intelligence itself is now tumbling down a curve, much like as has happened with prior generations of technology.

it’s worth reminding oneself that waves of AI enthusiasm have hit the beach of awareness once every decade or two, only to recede again as the hyperbole outpaces what can actually be done.

This analogy to lossy compression is not just a way to understand ChatGPT’s facility at repackaging information found on the Web by using different words. It’s also a way to understand the “hallucinations,” or nonsensical answers to factual questions, to which large language models such as ChatGPT are all too prone

When an image program is displaying a photo and has to reconstruct a pixel that was lost during the compression process, it looks at the nearby pixels and calculates the average. This is what ChatGPT does when it’s prompted to describe, say, losing a sock in the dryer using the style of the Declaration of Independence: it is taking two points in “lexical space” and generating the text that would occupy the location between them

they’ve discovered a “blur” tool for paragraphs instead of photos, and are having a blast playing with it.

A close examination of GPT-3’s incorrect answers suggests that it doesn’t carry the “1” when performing arithmetic. The Web certainly contains explanations of carrying the “1,” but GPT-3 isn’t able to incorporate those explanations. GPT-3’s statistical analysis of examples of arithmetic enables it to produce a superficial approximation of the real thing, but no more than that.

In human students, rote memorization isn’t an indicator of genuine learning, so ChatGPT’s inability to produce exact quotes from Web pages is precisely what makes us think that it has learned something. When we’re dealing with sequences of words, lossy compression looks smarter than lossless compression

Generally speaking, though, I’d say that anything that’s good for content mills is not good for people searching for information. The rise of this type of repackaging is what makes it harder for us to find what we’re looking for online right now; the more that text generated by large language models gets published on the Web, the more the Web becomes a blurrier version of itself.

Can large language models help humans with the creation of original writing? To answer that, we need to be specific about what we mean by that question. There is a genre of art known as Xerox art, or photocopy art, in which artists use the distinctive properties of photocopiers as creative tools. Something along those lines is surely possible with the photocopier that is ChatGPT, so, in that sense, the answer is yes

If students never have to write essays that we have all read before, they will never gain the skills needed to write something that we have never read.

Sometimes it’s only in the process of writing that you discover your original ideas.

Some might say that the output of large language models doesn’t look all that different from a human writer’s first draft, but, again, I think this is a superficial resemblance. Your first draft isn’t an unoriginal idea expressed clearly; it’s an original idea expressed poorly, and it is accompanied by your amorphous dissatisfaction, your awareness of the distance between what it says and what you want it to say. That’s what directs you during rewriting, and that’s one of the things lacking when you start with text generated by an A.I.

here are five large language model applications that I find intriguing: Intelligent automation starting with browsers but this feels like a step towards phenotropics Text generation when this unlocks new UIs like Word turning into Photoshop or something Human-machine interfaces because you can parse intent instead of nouns When meaning can be interfaced with programmatically and at ludicrous scale Anything that exploits the inhuman breadth of knowledge embedded in the model, because new knowledge is often the collision of previously separated old knowledge, and this has not been possible before.

We fully acknowledge that it’s hard to be confident in any predictions at the pace the field is moving. Right now, though, it seems we’re much more likely to see applications full of creative images created strictly by programmers than applications with human-designed art built strictly by creators.

Every aspect of interacting with the various institutions that monitored and managed my kids—ACS, the foster care agency, Medicaid clinics—produced new data streams. Diagnoses, whether an appointment was rescheduled, notes on the kids’ appearance and behavior, and my perceived compliance with the clinician’s directives were gathered and circulated through a series of state and municipal data warehouses. And this data was being used as input by machine learning models automating service allocation or claiming to predict the likelihood of child abuse.

The dominant narrative about child welfare is that it is a benevolent system that cares for the most vulnerable. The way data is correlated and named reflects this assumption. But this process of meaning making is highly subjective and contingent. Similar to the term “artificial intelligence,” the altruistic veneer of “child welfare system” is highly effective marketing rather than a description of a concrete set of functions with a mission gone awry.

Child welfare is actually family policing. What AFST presents as the objective determinations of a de-biased system operating above the lowly prejudices of human caseworkers are just technical translations of long-standing convictions about Black pathology. Further, the process of data extraction and analysis produce truths that justify the broader child welfare apparatus of which it is a part.

As the scholar Dorothy Roberts explains in her 2022 book Torn Apart, an astonishing 53 percent of all Black families in the United States have been investigated by family policing agencies.

The kids were contractually the property of New York State and I was just an instrument through which they could supervise their property. In fact, foster parents are the only category of parents legally obligated to open the door to a police officer or a child protective services agent without a warrant. When a foster parent “opens their home” to go through the set of legal processes to become certified to take a foster child, their entire household is subject to policing and surveillance.

Not a single one was surprised about the false allegations. What they were uniformly shocked about was that the kids hadn’t been snatched up. While what happened to us might seem shocking to middle-class readers, for family policing it is the weather. (Black theorist Christina Sharpe describes antiblackness as climate.)

In reality, artificial intelligence encompasses a range of techniques that largely remain useful for a range of uncinematic back-office logistics like processing data from users to better target them with ads, content and product recommendations.

the technologies would at times cause harm, as their humanlike capabilities mean they have the same potential for failure as humans. Among the examples cited: a mistranslation by Facebook’s AI system that rendered “good morning” in Arabic as “hurt them” in English and “attack them” in Hebrew, leading Israeli police to arrest the Palestinian man who posted the greeting, before realizing their error.

Recent local, federal and international regulations and regulatory proposals have sought to address the potential of AI systems to discriminate, manipulate or otherwise cause harm in ways that assume a system is highly competent. They have largely left out the possibility of harm from such AI systems’ simply not working, which is more likely, she says.

In other words, when Kylie Jenner posts a petition demanding that Meta “Make Instagram Instagram again”, the honest answer is that changing Instagram is the most Instagram-like behavior possible.

The first trend is the shift towards ever more immersive mediums. Facebook, for example, started with text but exploded with the addition of photos. Instagram started with photos and expanded into video. Gaming was the first to make this progression, and is well into the 3D era. The next step is full immersion — virtual reality — and while the format has yet to penetrate the mainstream this progression in mediums is perhaps the most obvious reason to be bullish about the possibility.

The second trend is the increase in artificial intelligence. I’m using the term colloquially to refer to the overall trend of computers getting smarter and more useful, even if those smarts are a function of simple algorithms, machine learning, or, perhaps someday, something approaching general intelligence.

The third trend is the change in interaction models from user-directed to computer-controlled. The first version of Facebook relied on users clicking on links to visit different profiles; the News Feed changed the interaction model to scrolling. Stories reduced that to tapping, and Reels/TikTok is about swiping. YouTube has gone further than anyone here: Autoplay simply plays the next video without any interaction required at all.