Topics discussed include Apple Vision Pro breakdown, Meta vs Snap performance, SaaS recovery, AI investing approaches, and the rolling real estate crisis.
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Quick takeaways
Investors in AI are split into three camps: waiting to see who will win, betting on open source solutions, and actively investing.
The SaaS industry is experiencing accelerated growth again, with companies beating conservative forecasts and investing in productivity.
Investors in AI focus on foundational models, infrastructure, and applications, with open source options expected to commoditize economic value.
Investors have differing opinions on investing in AI hardware, with closed models on open data expected to diminish in value.
YouTube's extensive data repository gives Google a significant advantage, ensuring higher quality models in the AI landscape.
Deep dives
The rise of open source AI models
There are three camps emerging in AI investment: those waiting to see who will win, those betting on open source solutions, and those actively investing. OpenAI's chat GPT is still perceived as the best model, giving them a consumer lead. Developers are building custom GPTs on the open AI platform to reach a large audience of consumers. While open source models are catching up, the value of closed models will diminish. However, open AI may maintain a lead due to its large consumer base.
The rebound of the SaaS industry
After several quarters of deceleration, the SaaS industry is seeing accelerated growth again. Companies are beating their conservative forecasts, indicating a recovery. Lowered expectations and cost-cutting during the recession have created a new baseline for growth. Companies are now investing in SaaS to improve productivity. Price negotiations are more favorable for buyers, leading to increased adoption. The SaaS industry is becoming more competitive, with a focus on speed, simplicity, and developer tools.
The future of AI investing
Investors have three main areas of focus in AI: foundational models, infrastructure, and applications. Open source models are expected to commoditize economic value, while still providing utility. The challenge for investors is to determine which areas will provide long-term value. OpenAI may maintain a lead in models due to its vast consumer base and developer-friendly platform. The picks and shovels providers, as well as companies with proprietary data, stand to benefit in the long run. Speed and responsiveness in AI infrastructure are critical for success.
Investing in AI hardware
Investors are divided on investing in AI hardware. Some believe that open source platforms will be successful, while others see potential in proprietary solutions. The value of closed models trained on open data is expected to diminish, benefiting open source options. However, the ease of using proprietary hardware and developer-friendly platforms may give certain companies an advantage. Speed and usability are crucial considerations for successful AI infrastructure offerings.
OpenAI's Three Business Areas
OpenAI is involved in three main business areas. Firstly, they are working towards achieving the same quality code point as other AI companies in the industry. Secondly, OpenAI has developed a consumer-facing app called Chat GPT, which they believe will have broad appeal and high engagement. Lastly, OpenAI is also selling enterprise services to large fortune 500 companies. While they have made progress in selling to the fortune 500, they still need to focus on delivering functional, production-grade code with fast service-level agreements (SLAs) that meet the industry's performance expectations.
The Importance of Data Advantage in AI
Data advantage is a crucial factor in driving value creation in the AI field. YouTube, with its extensive data repository consisting of video, audio, text, and images, holds a significant advantage over other platforms. The sheer scale and diversity of data on YouTube, which dwarfs that of other sources like Common Crawl, give Google a formidable moat. Data advantages ensure models achieve higher quality, and YouTube's data repository continues to grow, solidifying its position as the most valuable asset in the AI landscape. The importance of data advantage is evident in the diminishing returns observed when utilizing shared datasets for training models.
Challenges in Commercial Real Estate
Commercial real estate faces significant challenges, particularly in the office market. There is a lack of demand for office spaces, resulting in a surplus of supply. The office market, along with retail, are the sectors most heavily affected, with an estimated $1.2 trillion loss in office value and potential write-downs in office debt. These implications could negatively impact equity holders, primarily private equity funds and institutions, with pension and retirement funds being the end beneficiaries. Potential write-downs in office debt could also affect regional banks, leading to solvency concerns and potential government intervention to support retirees and pensioners.
Supply and Demand Dynamics in Real Estate
Real estate experiences contrasting supply and demand dynamics between commercial and residential sectors. While there is a lack of demand for office spaces, the demand for residential homes is high. However, residential real estate also faces financing challenges, with increased costs and negative leverage. The ability to reset prices annually in the residential market allows for quicker adaptation to changing supply and demand dynamics. Nonetheless, the commercial real estate market experiences a structural behavior change due to the COVID-19 pandemic, resulting in delayed vacancy issues and an impending vacancy cliff. Additionally, the dependence on debt refinancing and the potential negative impact on equity holders and banks exacerbate the challenges in the commercial real estate sector.
Potential Government Intervention in Real Estate Crisis
The commercial real estate crisis, especially in the office market, may require government intervention to avoid extensive losses for equity holders, banks, and pension funds. With potential write-downs in office debt and significant declines in equity values, retirement funds may suffer, necessitating structured solutions to support retirees and pensioners. Governments may step in to prevent systemic issues by providing assistance and stabilizing the real estate market. The crisis also highlights the interplay between demand, financing, and solvency concerns in the commercial real estate sector.
The Importance of Data Advantage in AI
Data advantage is a crucial factor in driving value creation in the AI field. YouTube, with its extensive data repository consisting of video, audio, text, and images, holds a significant advantage over other platforms. The sheer scale and diversity of data on YouTube, which dwarfs that of other sources like Common Crawl, give Google a formidable moat. Data advantages ensure models achieve higher quality, and YouTube's data repository continues to grow, solidifying its position as the most valuable asset in the AI landscape. The importance of data advantage is evident in the diminishing returns observed when utilizing shared datasets for training models.
