Is AI generation the next platform shift?
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Generative artificial intelligence (AI) models are reinventing communication, content creation, and information access. In this roadmap, presented at Bessemer's annual Seed Summit, Partner Talia Goldberg explores the technological advancements driving AI solutions and how these changes are opening up new promising area of investment. Learn more about Generative AI: https://www.bvp.com/atlas/is-ai-gener... https://www.bvp.com/atlas/roadmap-the... https://www.bvp.com/atlas/entering-th... Subscribe for venture insights: https://bessemervp.team/subscribe About Bessemer Venture Partners — We help entrepreneurs lay strong foundations to build and forge long-standing companies. With more than 135 IPOs and 200 portfolio companies in the enterprise, consumer and healthcare spaces, Bessemer supports founders and CEOs from their early days through every stage of growth. Our global portfolio includes Pinterest, Shopify, Twilio, Yelp, LinkedIn, PagerDuty, DocuSign, Wix, Fiverr and Toast, and has more than $20 billion of assets under management. Connect with us — Subscribe to our channel: https://bit.ly/3oVeW4k Visit our website bvp.com: https://bit.ly/3bzFXaE Sign up for our newsletter: https://bit.ly/3SoVY3D Find Bessemer on LinkedIn: https://bit.ly/3zZpGoS Find Bessemer on Twitter: https://bit.ly/3JsVJAF Find Bessemer on Instagram: https://bit.ly/3BH8but
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Is AI generation the next platform shift?
- 1. Living in a large language world Generating value with transformers, NLP, and generative AI Talia Goldberg, Partner
- 2. 2 / Confidential and Not for Distribution AI is the new (old) web3
- 3. 3 / Confidential and Not for Distribution The iPhone moment for AI?
- 4. 4 / Confidential and Not for Distribution Why now? Performance approaching human baseline across text, image, audio, and code Note: All metrics are created by researchers to measure improvements in models. They are updated as model autonomy improves. FID data models from WGAN-GP, BigGAN, AutoGAN, Denoising Diffusion LSGM, StyleGAN assuming a 30 point normalization range (from 2017-2022) with human baseline as 0.0 score. SuperGLUE data from BERT, T5, T5 + Meena, Single Model (Meena Team - Google Brain), Liam Fedus assuming a 20 point normalization range (from 2018-2022) with human baseline as 89.0 score. LJSpeech MOS data from WaveNET, WaveRNN, Transformer TTS, FastSpeech, VITS assuming a 1 point normalization range (2017-2022) with human baseline as 4.5 score. -0.2 0 0.2 0.4 0.6 0.8 1 1.2 2016 2017 2018 2019 2020 2021 2022 2023 Normalized Distance From Human Baseline Near human-level performance across modalities and metrics Human Baseline Image Generation Text Generation Speech Generation
- 5. 5 / Confidential and Not for Distribution Why now? More data, more compute, better models Source: HuggingFace LLMs are doubling in size every few months
- 6. 6 / Confidential and Not for Distribution 2019 2020 ELMo (94m) Open source 2018 2021 BERT (340m) 2019 2020 2018 2021 GPT-3 (175B) Jurassic-1(178B) LaMDA (137B) 2022 2022 MT-NLG(530B) Gopher(280B) Turing-NLG(17B) GPT-2 (1.5B) Megatron-LM(8.3B) TransformerELMo (465m) BigScience BLOOM (176B) OPT-175B(175B) Godel(175B) Private RoBERTa (354m) YaLM (100B) PaLM (540B) Chinchilla (70B) Democratized access via open-source models and enabling infrastructure Power to the people! LFG! Why now? Stable diffusion
- 7. Technology changes happen gradually, then suddenly.
- 8. 8 / Confidential and Not for Distribution Rapid change & uncertainty Source: Google
- 9. 9 / Confidential and Not for Distribution Finding signal amidst the noise AI-native categories Search 2.0: generative, personalized info systems Model agnostic AI enablers, with community participation & networks
- 10. 10 / Confidential and Not for Distribution Where will value accrue? Differentiation Full stack with a data moat e.g., fine tune your own model with proprietary data generated on your own platform, more efficient & flexible Model agnostic enabler, with engaged network or community of participatory users First mover advantage or early AI-native leader Specialist application for a niche but growing market
- 11. 11 / Confidential and Not for Distribution What we are looking for Team Technical founder(s) with commercial mindset and ties to top research institutions Business model SaaS, consumer subscription, or transactional… not just re-selling compute Stage Primarily seed and series A. If in a large, competitive horizontal category, will index towards first movers with momentum & distribution advantages Product AI native experience, or more than a single model, magical ”10x” experience or ROI, not incremental Retention As ”novelty” fades, we look for retention curves that asymptote in <6 months
- 13. 13 / Confidential and Not for Distribution AI-native apps Enable new consumer experiences
- 14. 14 / Confidential and Not for Distribution AI-native apps Natural language interfaces replace GUIs & code
- 15. 15 / Confidential and Not for Distribution AI-native apps Supercharge workflows and increase productivity
- 17. 17 / Confidential and Not for Distribution The iPhone moment for AI?
- 18. 18 / Confidential and Not for Distribution Dall-E prompt: “hand holding an iPhone”
- 19. 19 / Confidential and Not for Distribution The end of search as we know it
- 20. 20 / Confidential and Not for Distribution The end of search as we know it
- 21. 21 / Confidential and Not for Distribution AI search market map
- 22. 22 / Confidential and Not for Distribution AI Advisory Board ► World-class technical & commercial advisors from Google, Jasper, opensource community ► DD support & market insights, portfolio advice, talent ► Collaboration with AI research labs, seed funds, and angels
- 23. 23 / Confidential and Not for Distribution WAGMI How we’d love to work with our seed community… 1 2 3 Collaborate with our AI Advisors Focused on early stage – seed rounds, Series A, & B Anything on roadmap, inclusive of AI-native apps, reimaging search, enablement (not presented today)
- 24. 24 / Confidential and Not for Distribution Let’s work together! AI-native categories Search 2.0: generative, personalized info systems Model agnostic AI enablers, with community participation & networks
Notas do Editor
- https://labs.openai.com/s/KW35ppZRmuafEuhhErs3SG1n
- LLMs underpin multi-modal models spanning image, audio, video, code, and beyond
- LLMs have been increasing an average of 10x per year in size and sophistication. The result: The advent of the transformer, a model architecture, has catalyzed the rapid development of and success of LLMs. The combination of Moore’s law and Denard scaling, combined with improved GPU architectures allows us to train larger models with greater amounts of data.
- If you think its good now, just wait a year…. Research papers doubling
- https://labs.openai.com/s/KW35ppZRmuafEuhhErs3SG1n
- https://labs.openai.com/s/KW35ppZRmuafEuhhErs3SG1n