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Amodei also sees use cases ranging from “legal use cases, medical use cases, storing financial information and managing financial bets, [to] where you need to preserve the company brand. You don’t want the tech you incorporate to be unpredictable or hard to predict or characterize.” With better steering, LLMs will also be able to do more complex tasks with less prompt engineering, as they will be able to better understand overall intent. We start with a gentle introduction to transformer and latent diffusion models, which are fueling the current AI wave. Next, we go deep on technical learning resources; practical guides to building with large language models (LLMs); and analysis of the AI market. Finally, we include a reference list of landmark research results, starting with “Attention is All You Need”—the 2017 paper by Google that introduced the world to transformer models and ushered in the age of generative AI.
So far 2D image generators like Stable Diffusion, or MidJourney have captured the majority of the popular excitement over generative AI due to the eye-catching nature of the images they can generate. But already there are generative AI models Yakov Livshits for virtually all assets involved in games, from 3D models, to character animations, to dialog and music. The second half of this blog post includes a market map highlighting some of the companies focusing on each type of content.
But when the economic benefits are as compelling as they are with generative AI, there is ample velocity to build a company around more traditional defensive moats such as scale, the network, the long tail of enterprise distribution, brand, etc. In fact, we’re already seeing seemingly defensible business models arise in the generative AI space around two-sided marketplaces between model creators and model users, and communities around creative content. Many of the use cases for generative AI are not within domains that have a formal notion of correctness.
Critically, growth must be profitable — in the sense that users and customers, once they sign up, generate profits (high gross margins) and stick around for a long time (high retention). In the absence of strong technical differentiation, B2B and B2C apps drive long-term customer value through network effects, holding onto data, or building increasingly complex workflows. An extreme example would be the creation of an entire video game from a single prompt. Today, companies create models for every aspect of a complex video game—3D models, voice, textures, music, images, characters, stories, etc.—and creating a AAA video game today can take hundreds of millions of dollars. The cost of inference for an AI model to generate all the assets needed in a game is a few cents or tens of cents.
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In adherence to the Trust Project guidelines, BeInCrypto is committed to unbiased, transparent reporting. However, readers are advised to verify facts independently and consult with a professional before making any decisions based on this content. Gartner predicts that by 2026, 50% of all sales and marketing providers will incorporate assistants, and 60% of design process by new websites will be by generative AI. And by 2025, 75% percent of digital marketing communications will have avatars. “As crazy as it sounds to say the potential size of this market is somewhere between all software and human endeavors, it’s not actually crazy when you understand how this technology works and what it’s going to be capable of,” says Roetzer. And existing platforms like HubSpot are now building generative AI into their products.
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- And for competitive games, beating up on computer bots never gets old – from frying aliens in Space Invaders to the comp stomp in Starcraft, eventually turned into its own game mode Co-op Commanders.
- “During a gold rush, sell shovels.” The mega cloud providers and GPU companies have a very high probability of minting huge sums of money over the next couple of decades irrespective of which Gen AI app or LLM builder wins.
- They can guide the model toward the answer they’re seeking, rather than requiring the company to shoulder a pool of humans to ensure immediate correctness.
To set up their version of AI Town, developers must clone the project’s repository, install packages, and add API keys for services such as OpenAI and Pinecone. Convex handles the world’s initialization, character AI, and gameplay logic, while the Next.js-powered website manages user account management through Clerk. Each AI character in the town is given a starting prompt that shapes its persona and serves as the basis for its interactions and behavior. As the AI characters engage with each other, they retain the memory of their conversations, allowing for continuity in their interactions.
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
For example, here is a nice tutorial from Albert Bozesan on using Stable Diffusion to create in-game 2D assets. It’s possible that large studios will seek competitive advantage by building proprietary models built on internal content they have clear right & title to. Microsoft, for example, is especially well positioned here with 23 first party studios today, and another 7 after its acquisition of Activision closes. What all of these generative AI models have in common is that they are trained using massive datasets of content, often created by scraping the Internet itself.
AI copilots as a game mechanic could even create entirely new gameplay modes. Picture Fortnite but every player has a Master Builder wand that can instantly build sniper towers or flaming boulders via prompts. In this game mode, victory would likely be determined more by wand work (prompting) than the ability to aim a gun. While we previously Yakov Livshits covered the use of generative agents in simulation games, there’s another emergent use-case where the AI serves as gaming copilot – coaching us on our play and in some cases even playing alongside of us. NovelAI has developed its own LLM Clio, which it utilizes to tell stories in a sandbox mode and help solve writer’s block for human writers.
This is because, at the level of the individual worker, the marketplace sets compensation as a function of the marginal productivity of the worker. A worker in a technology-infused business will be more productive than a worker in a traditional business. The employer will either pay that worker more money as he is now more productive, or another employer will, purely out of self interest. The result is that technology introduced into an industry generally not only increases the number of jobs in the industry but also raises wages.
We don’t even need new laws – I’m not aware of a single actual bad use for AI that’s been proposed that’s not already illegal. The core mistake the automation-kills-jobs doomers keep making is called the Lump Of Labor Fallacy. This fallacy is the incorrect notion that there is a fixed amount of labor to be done in the economy at any given time, and either machines do it or people do it – and if machines do it, there will be no work for people to do. Its proponents claim the wisdom to engineer AI-generated speech and thought that are good for society, and to ban AI-generated speech and thoughts that are bad for society. This cult is why there are a set of AI risk doomers who sound so extreme – it’s not that they actually have secret knowledge that make their extremism logical, it’s that they’ve whipped themselves into a frenzy and really are…extremely extreme.
Consumers win in the long run
And, at this early stage in generative AI, technologists and product pickers will likely have the biggest impact on which companies emerge as winners. Think of these as vertical-specific call centers where a representative needs to distill what a customer, agent, or broker actually needs during a conversational dialogue, and either respond with the answer or enter the appropriate information into a system. Allowing LLMs to manage some of these conversations could dramatically improve efficiency and profitability. In the process, we should drive AI into our economy and society as fast and hard as we possibly can, in order to maximize its gains for economic productivity and human potential.