The Next Token of Progress: 4 Unlocks on the Generative AI Horizon Andreessen Horowitz
It’s easy to see why—it has one of the most beautiful, fully realized virtual worlds of any game on the market. Many generalist content creation tools like Jasper, Copy, and Writer have gained meaningful traction among SMBs. But we’ve also started to see verticalized tools that are tailor-made for the workflow of specific types of businesses. Products like Harvey and Spellbook, for example, help legal teams automate tasks like intake, research, and document drafting. In real estate, Interior AI enables agents to virtually stage their properties, while Zuma helps property managers convert leads to booked tours.
Generative AI refers to a subset of artificial intelligence that focuses on creating new data or content based on existing data. This is achieved through the use of machine learning algorithms, specifically deep learning techniques, that can generate outputs such as text, images, audio, or video. Generative AI models learn patterns and structures from the input data and can then generate new, original content that resembles the training data. While current machine learning technology allows for improved decisioning on simple products like auto and home insurance, more complex underwriting processes like commercial and life insurance remain challenging.
Meta and Deepmind alumni raise €105m seed round to build OpenAI rival Mistral
Indeed, according to Irreverent Labs’ co-founder and CEO, Rahul Sood, the company elected to work more closely with Samsung as a strategic investor partly to access Samsung Next portfolio companies that may want to use its API. Further, the outfit will be working with Samsung’s device units in order to develop a larger distribution strategy, Sood explained. Our catalog contains everything you need to build and scale a high-performing agile development team. Watching the generative AI space shape up over the past several months has reaffirmed my belief that, as product cycles mature, different types of builders have leverage at different moments in the cycle.
What we now call generative AI wouldn’t exist without the brilliant research and engineering work done at places like Google, OpenAI, and Stability. Through novel model architectures and heroic efforts to scale training pipelines, we all benefit from the mind-blowing capabilities of current large language models (LLMs) and image-generation models. Just as the time to 1 million Yakov Livshits users has been truncated, so has the time it takes for many AI companies to hit $10-million-plus of run-rate revenue, often a fundraising hallmark for achieving product-market fit. For example, it may take an investment of $20 million to build a robot that can pick cherries with 80% accuracy, but the required investment could balloon to $200 million if you need 90% accuracy.
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Luma uses neural radiance fields (NeRFs) to allow consumers to construct photorealistic 3D assets from 2D images captured on an iPhone. Kaedim uses a blend of AI and human in the loop quality control to create production-ready 3D meshes that are already being used by over 225 game developers today. And CSM recently released a proprietary model that can generate 3D models from both video and images. The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates.
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.
Signs in the world could reflect the player achieving a certain title or status (“wanted for murder!”). NPCs could be set up as LLM-powered agents with distinct personalities that adapt to your behavior – dialogue could change based upon a player’s past actions with the agent, for example. We’ve seen this concept executed successfully in a AAA game already – Monolith’s Shadow of Mordor has a nemesis system that dynamically creates interesting backstories for villains based on a player’s actions. If you share our belief in the team’s mission to chart the future of game development, we encourage you to check out the company’s career page. We are now experiencing a new transformative shift, one that could be the most impactful yet.
TABLE OF CONTENTSWhat Is To Be Done?
The most popular games cost millions—sometimes even hundreds of millions—to produce. Aside from the storyline of the game, developers need to generate thousands of media assets, from the graphics themselves to 3D models to soundtracks. Nearly everything in generative AI passes through a cloud-hosted GPU (or TPU) at some point. Whether for model providers / research labs running training workloads, hosting companies running inference/fine-tuning, or application companies doing some combination of both — FLOPS are the lifeblood of generative AI. For the first time in a very long time, progress on the most disruptive computing technology is massively compute bound.
So far, we’ve had a hard time finding structural defensibility anywhere in the stack, outside of traditional moats for incumbents. Over the last year, we’ve met with dozens of startup founders and operators in large companies who deal directly with generative AI. We’ve observed that Yakov Livshits infrastructure vendors are likely the biggest winners in this market so far, capturing the majority of dollars flowing through the stack. Application companies are growing topline revenues very quickly but often struggle with retention, product differentiation, and gross margins.
And when the job involves one of the more fundamental capabilities of carbon life, such as perception, humans are often cheaper. Or, at least, it’s far cheaper to get reasonable accuracy with a relatively small investment by using people. This is particularly true for startups, which typically don’t have a large, sophisticated AI infrastructure to build from. It can create new and innovative game content, improve game mechanics, and boost player engagement. With its ability to generate vast amounts of data, generative AI is helping game developers create more immersive and realistic gaming experiences.