03 Ago The Race to Capture Value: Cloud Lessons for the AI Era Andreessen Horowitz

Let’s Get Personal: Why AI Will Unlock a Massive Market for Online Consumer Services Andreessen Horowitz

‘As a company deeply immersed in the 3D domain, we would joke that zebras were the universe’s unique interpretation of a horse. The substitution of ‘i’ for ‘e’ was originally a designer’s typo from our early days. However, we found it charming and chose to embrace it,’ the startup’s head of growth Kostiantyn Tymoschuk recalls.

The ultimate value-add of AI software goes beyond the exact dollar figures captured in software budgets to all the economic activity it will support. A 3D model only looks as realistic as Yakov Livshits the texture or materials that are applied to the mesh. Deciding which mossy, weathered stone texture to apply to a medieval castle model can completely change the look and feel of a scene.

The funding brings the company’s total investment so far to $2.5 million

But those use cases are almost certainly going to evolve into even more powerful applications as stage two, or wave two, as venture capital firm Andreessen Horowitz (a16z) calls it. We’re still in what Khetan called a “pull world,” in which we’re asking AI for responses. Synthesis or synth AI is when the AI automatically looks at the data and tells us what it sees, and can be set up at any cadence we want. AI has been integrated into our lives for a long time, but generative AI just hits different.

• This is precisely what is already happening in AI – it’s why you can use state of the art generative AI not just at low cost but even for free today in the form of Microsoft Bing and Google Bard – and it is what will continue to happen.
• Millions of writers today use ChatGPT to provide inspiration for their own stories.
• The result is that the same banks that were “too big to fail” in 2008 are much, much larger now.
• Games have the highest entrance barrier of all types of entertainment since it takes a lot of time and money to create a significant amount of interactive content.

While new software products can help merchants calculate the amount of taxes they owe in a given geography, they do not actually help with the remittance of said payments—which can be a massive undertaking to set up in-house. For example, if you are worried about AI generating fake people and fake videos, the answer is to build new systems where people can verify themselves and real content via cryptographic signatures. Digital creation and alteration of both real and fake content was already here before AI; the answer is not to ban word processors and Photoshop – or AI – but to use technology to build a system that actually solves the problem. Popular racing game Forza developed a Drivatar system that uses machine learning to build an AI driver for each human player mimicking their driving behavior. The Drivatars are uploaded to the cloud and can be called upon to race other players when their human partners are offline, even earning credits for victories.

Foundational learning: neural networks, backpropagation, and embeddings

NVIDIA’s DLSS technology can already generate new higher-resolution game frames on the fly using consumer-grade GPUs. One day, you may be able to click “interact” on a Netflix movie, then step into the world with every scene generated on the fly and uniquely personalized for the player. With their natural language capabilities, the ways we interact with agents have also expanded.

The use of OpenAI for natural language text generation enables the AI characters to engage in realistic and coherent conversations. Meanwhile, the Convex back-end serverless framework provides the necessary infrastructure to manage the shared global state and a journal of all events, ensuring the continuity and evolution of character interactions and narratives. Generative AI and foundation models are poised to have a transformative and overwhelming impact on various industries, driving significant economic growth.

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Yakov Livshits
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.

Founded in 2021, Zibra AI aims to use the funding to expand its team and fuel the growth of its innovative generative AI technology. The company’s flagship product, Zibra Effects, is a Unity Verified Solution that can be utilized across PC and Mac, iOS and Android, consoles and VR/AR, with use cases in industries like gaming, metaverse and professional training simulations. By automating the creation of high quality 3D assets and visual effects, the technology helps streamline production for developers. Though it initially used its AI technology to build video games, it realized that allowing users to generate all types of short videos, including 3D, presented a much bigger opportunity. Large language models (LLMs), with their ability to proficiently collect and distill large amounts of data, could change this as they can augment or fully replace the process of a human combing through large amounts of data. This causes the prices for existing goods and services to fall, and for wages to rise.

Ready Player Me enables users to generate a 3D avatar of themselves with a selfie and import their avatar into over 9k games/apps. AI character platforms Character.ai, InWorld, and Convai enable the creation of custom NPCs with their own backstory, personality, and behavior controls. Want to create a Hogwarts simulation where you are roommates with Harry Potter?

It’s Time to Build For America: Announcing Our $500M+ Commitment to Companies Building in American Dynamism Andreessen Horowitz

In fact, the terminology of AI risk recently changed from “AI safety” – the term used by people who are worried that AI would literally kill us – to “AI alignment” – the term used by people who are worried about societal “harms”. The original AI safety people are frustrated by this shift, although they don’t know how to put it back in the box – they now advocate that the actual AI risk topic be renamed “AI notkilleveryoneism”, which has not yet been widely adopted but is at least clear. The second widely mooted AI risk is that AI will ruin our society, by generating outputs that will be so “harmful”, to use the nomenclature of this kind of doomer, as to cause profound damage to humanity, even if we’re not literally killed.

Here, we share our early framework for assessing defensibility in growth-stage AI companies, when competitive advantages transition from early theories to the factors that govern long-term market winners and losers. Startup applications stealing more market share from incumbents than infrastructure companies is consistent with the common intuition that applications tend to be less defensible products. Maturity in developer tooling and core infrastructure off-the-shelf makes it easier to build new versions of familiar applications that steal customers from the prior generation. It’s much harder to convince customers to rip-and-replace core infrastructure, which creates a longer transition cycle from old to new infrastructure and buys incumbents more time to adapt. Trained on trillions of tokens of data with clusters of thousands of GPUs, LLMs demonstrate remarkable natural language understanding and have transformed fields like copy and code, propelling us into the new and exciting generative era of AI.

Japanese crypto exchange JPEX to pause interest rewards as partners freeze funds

Thus, AI-powered solutions often still use humans in the loop to ensure accuracy, a situation that can be difficult to scale and often becomes a burdensome cost that weighs on gross margins. This post explores the economics of traditional AI and why it’s typically been difficult to reach escape velocity for startups using AI as a core differentiator (something we’ve written about in the past). It then covers why generative AI applications and large foundation-model companies look very different, and what that may mean for our industry. Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields.

Although existing digital services often aren’t direct substitutes for in-person services, they offer similar experiences. Though generative AI will unlock opportunities across consumer services, it will have an outsized impact in industries that haven’t been penetrated by e-commerce. To date, e-commerce has penetrated industries with transactions that require limited third-party expertise, like paying utilities or booking flights. We see outsized opportunities in high-touch industries where personalization matters the most to consumers, represented by the top-right quadrant below. We’ve all marveled at what generative AI can produce, but there are still a lot of questions about what it all means. In time, we’re likely to see more professional-grade generative AI products emerge.

Alongside ChatGPT, this category includes Google’s Bard and Quora’s Poe, all ranked in the top 5. ChatGPT represents 60% of monthly traffic to the entire top 50 list, with an estimated 1.6 billion monthly visits and 200 million monthly users (as of June 2023). Download the report to understand the generative AI landscape across funding trends, top-valued startups, most active VCs, and more. The a16z article is around 3,000 words and, given the depth of the analysis, will probably take close to 15 minutes to read in its entirety.