How AI Affects Startup Costs

AI is all the rage but its also ridiculously Expensive to build, maintain and grow: 1. **Data costs**: AI models require vast amounts of high-quality data to learn and improve, which can be time-consuming and costly to collect, clean, and label. 2. **Computing resources**: Training and running AI models require powerful computing resources, such as GPUs or TPUs, which can be expensive to purchase and maintain. 3. **Talent and skill**: Building and maintaining AI systems require specialized skills, such as machine learning engineers, data scientists, and researchers, who can command high salaries. 4. **Software and licensing fees**: Many AI frameworks, libraries, and tools require licensing fees or subscriptions, adding to the overall cost. 5. **R & D**: Continuously improving AI systems requires ongoing research and development, which can be costly and time-consuming. 6. **Infrastructure and storage**: Storing and processing large amounts of data, as well as deploying AI models, require robust infrastructure, which can be expensive to set up and maintain. 7. **Energy consumption**: Training and running AI models can consume significant amounts of energy, leading to higher electricity bills. 8. **Regular updates and maintenance**: AI systems require regular updates and maintenance to stay accurate and secure, which can add to the overall cost. 9. **Ethical and legal considerations**: Ensuring AI systems are ethical, fair, and compliant with regulations can require additional resources and expenses. 10. **Scalability**: As AI systems grow and become more complex, maintaining them can become increasingly expensive.

How AI/ML startups today are different from the startups of yesterday ⤵️ PitchBook just released new data that quantifies the AI boom: 2021–2023: $330B into ~26,000 AI/ML startups 2018–2020: ~$200B into 20,350 AI/ML startups That’s a 66% increase in funding and a 27% increase in the number of AI/ML startups over the past three years. The rise in startups is in part due to the emergence of capital-light businesses that no longer require a large engineering team and resources to scale. AI products like #ChatGPT, #Gemini and #Midjourney create labor efficiency by helping teams complete tasks faster, and have shown to be a structural cost reduction in knowledge work. It’s the latest in a line of paradigm shifts: The internet cut the cost of distribution. ↓ The cloud cut the cost of storage and computing. ↓ Now, AI is cutting the cost of the entire venture creation process, from ideation to production, increasing capital efficiency at the same time. The proliferation of open-source (#MetaLlama) and AI-powered software development (#GitHubCopilot) tools help collapse the distance between language and code to zero. Technology barriers to entry no longer exist as they used to and as a result, #GenerativeAI is ushering in a new era of entrepreneurship – one which is rapidly moving towards a seamless, natural language based “idea-to-product workflow.” It's sparking a new generation of entrepreneurs who are able to harness industry expertise and relationships to solve meaningful problems in their fields. Our investments in Paxton AI, a legal tech start-up using LLMs to tackle regulatory compliance and legal drafting, and Fintary, an AI InsurTech platform automating account reconciliation, are just two examples of Kyber Knight Capital backing founders with deep industry expertise leveraging new technology to capture legacy market opportunities. It’s clear that AI is proving to be a force of economic empowerment across the economy, and minting many of these new breeds of startups. #Funding #VC #Startups #AI #MachineLearning

There has been so much talk about what is going on with startups and venture capital. The reality, is your perspective depends a lot on where you are engaged in the lifecycle of startups...🤔 For early-stage investors (Pre-Seed, Seed and Series-A), the quantity of deals has gone down, but the quality of deals has gone up and the end result is that prices are mostly un-impacted. A couple of reasons this could be: ✅ The end of entrepreneurial tourism - when the entrepreneurship reverts back to being a "hard" job, a lot of people leave the market and you are left with the real fanatics. I can't imagine anyone better to build a company. ✅ Time gap between early-stage companies and liquidity - valuable things take a long time to build, and startups are no different. The companies that are started today won't become liquid for 10 to 15 years so, the state of the market today is pretty irrelevant as it relates to valuations. ✅ Exceptional talent is re-entering the market - for years, the best talent in this country has been locked up in the best companies in this country. That's no longer true! Extremely talented and motivated people are being laid off every month. Many of them are choosing to go and start the next, Uber, Dropbox, Airbnb, or Facebook. ✅ The deflationary impact of AI - most technologies are deflationary, and AI is no different. Artificial intelligence is letting founders do a lot more with a lot less, a lot sooner in the life-cycle of their businesses. That is being reflected in current valuations, and will turbo-charge this companies as they grow. 📋 CONCLUSION You can't time the market any more that you can time the waves, but you can time the seasons. It's definitely spring time for early-stage companies and the tail-end of winter for later stage companies. It is a good time to be investing, time, talent and money if you are an early-stage founders or investor. If you are a later stage founder or investor, it is a good time to hunker-down and wait for the storm to pass. #ycombinator #techstars #venturecapital #ai Lobby Capital David Hornik Eric Carlborg Buddy Arnheim Selina Tobaccowala James Everingham Kevin Johnson Candice Faktor August Capital Georgia Institute of Technology Stanford University Graduate School of Business