| | | | | | Is AI a bubble about to burst or a tree ready to branch out? Or would you say it’s just a bubblegum losing its flavour? Today, let’s explore the future of AI and whether the current surge in generative AI is sustainable or a sign of something much larger. Your AI Human, Amit Raja Naik, is here to decipher it for you. | | | | | | | | | | | | However, some believe otherwise. | | | | | “As long as Sam Altman is the CEO, OpenAI will never go bankrupt. He will continue to drop mind-blowing demos and feature previews, and raise billions. I am not being sarcastic; it's the truth,” posted an AI influencer, Ashutosh Shrivastava, on X. Further, he added that with products like Sora, the voice engine, GPT-4’s voice feature, and now SearchGPT, anyone who thinks OpenAI will go bankrupt is simply underestimating Altman. As OpenAI prepares to seek more funding in the future, it's essential for Altman to create more bubbles of hype. Without this, the industry risks underestimating the full impact of generative AI. Chinese investor and serial entrepreneur Kai-Fu Lee is bullish about OpenAI becoming a trillion-dollar company in the next two to three years. “OpenAI will likely be a trillion-dollar company in the not-too-distant future,” he recently said. On the contrary, analysts and investors from major financial institutions like Goldman Sachs, Sequoia Capital, Moody’s, and Barclays have released reports expressing concerns about the profitability of the substantial investments in generative AI. Sequoia Capital partner David Cahn’s recent blog, ‘600 Billion Question’, points out the gap between AI infrastructure spending and revenue. He suggests the industry needs to generate around $600 billion annually to cover investment costs and achieve profitability. | | | | | | | Early signs of an AI bubble? | | | | | On Tuesday, Microsoft shares fell 7% as the tech giant reported lower-than-expected revenue. Revenue from its Intelligent Cloud unit, which includes the Azure cloud-computing platform, rose 19% to $28.5 billion in the fourth quarter, missing analysts’ estimates of $28.68 billion. Despite that, the company announced plans to spend more money this fiscal year to enhance its AI infrastructure, even as growth in its cloud business has slowed down, suggesting that the AI payoff will take longer than expected. Microsoft CFO Amy Hood explained that the spending is essential to meet the demand for AI services, adding that the company is investing in assets that “will be monetised over 15 years and beyond”. CEO Satya Nadella also said that Azure AI now boasts over 60,000 customers, marking a nearly 60% increase year-on-year, with the average spending per customer also on the rise. Last week, Google’s cloud revenue exceeded $10 billion, surpassing estimates for Q2 2024. The company is, however, facing increasing AI infrastructure costs. Google CEO Sundar Pichai insists, “The risk of under-investing far outweighs the risk of over-investing for us.” He warned, “Not investing to stay ahead in AI carries much more significant risks.” “If you take a look at our AI infrastructure and generative AI solutions for cloud across everything we do, be it compute on the AI side, the products we have through Vertex AI, Gemini for Workspace and Gemini for Google Cloud, etc, we definitely are seeing traction,” Pichai said, elaborating that the company now boasts over two million developers playing around with Gemini on Vertex and AI Studio. "AI is exceptionally expensive, and to justify those costs, the technology must be able to solve complex problems, which it isn’t designed to do,” said Jim Covello, Goldman Sachs’ head of global equity research. | | | | | | | Dario Amodei, the CEO of Anthropic, has predicted that training a single AI model, such as GPT-6, could cost $100 billion by 2027. In contrast, a trend is emerging towards developing small language models, more cost-efficient language models that are easier to run without requiring extensive infrastructure. OpenAI’s former co-founder, Andrej Karpathy recently said that the cost of building an LLM has reduced drastically over the past five years due to improvements in compute hardware (H100 GPUs), software (CUDA, cuBLAS, cuDNN, FlashAttention) and data quality (e.g., the FineWeb-Edu dataset). Abacus.AI chief Bindu Reddy has predicted that in the next five years, smaller models will become more efficient, LLMs will continue to become cheaper to train, and LLM inference will become widespread. “We should expect to see several Sonnet 3.5 class models that are 100x smaller and cheaper in the next one to two years,” she said. | | | | | | | | | Generative AI isn’t represented by a single bubble like the dot-com era but is manifested in multiple, industry-specific bubbles. For example, generative AI tools for video creation, such as Sora and Runway, demand much more computational power compared to customer care chatbots. Despite these variations, generative AI is undeniably a technology with lasting impact and is here to stay. “I think people are using the word “bubble” too lightly and without much thought, as they have become accustomed to how impressive ChatGPT or similar tools are and are no longer impressed. They are totally ignoring trillion-dollar companies emerging with countless new opportunities. Not everything that grows is a bubble, and we should stop calling AI a bubble or a trend. It is a new way of doing things, like the internet or smartphones,” posted a user on Reddit. “AI is more like… a tree. It took a long time to germinate, sprouted in 2016, became something worth planting in 2022, and is now digging its roots firmly in… Just like a tree, AI’s impact and value will keep growing and evolving. It's not a bubble; it’s more like an ecosystem,” said another user on Reddit. The bubblegum effect: The issue today is that investors are using OpenAI and NVIDIA as benchmarks for the AI industry, which may not be sustainable in the long term. While NVIDIA has had significant success with its H100s and B200s, it cannot afford to become complacent. The company must continually innovate to reduce training costs and maintain its edge. This concern is evident in NVIDIA chief Jensen Huang’s anxiety about the company's future: “I am paranoid about going out of business. Every day I wake up in a sweat, thinking about how things could go wrong,” said Huang. He further explained that in the hardware industry, planning two years in advance is essential due to the time required for chip fabrication. “You need to have the architecture ready. A mistake in one generation of architecture could set you back by two years compared to your competitor,” he said. NVIDIA's success should not be taken for granted, even with the upcoming release of its latest GPU, Blackwell. Alternatives to NVIDIA are increasingly available, particularly for inference tasks, including Google TPUs and Groq. Recently, Groq demonstrated impressive inference speed with Llama 3.1, and Apple selected Google TPUs over NVIDIA GPUs for its model training needs.
Most recently, AI hardware company Etched.ai, unveiled its chip purpose-built just to run Transformer models. Etched claims that Sohu can process over 500,000 tokens per second with Llama 70B. One 8xSohu server replaces 160 H100s. According to the company, “Sohu is more than ten times faster and cheaper than NVIDIA’s next-generation Blackwell (B200) GPUs.” On the other hand, Meta recently released Llama 3.1, which is currently competing with GPT-40. Meta chief Mark Zuckerberg is confident that Llama 3.1 will have a similar impact on the AI ecosystem as Linux had on the operating system world. Moreover, Meta also recently launched AI Studio, which allows creators to build and share customisable AI agents. Meanwhile, the Linux founder recently dismissed the AI hype, and cautioned to wait for a decade for the ‘hype’ to end. However, he also acknowledged its potential, advocating a cautious approach to its long-term impact on jobs. “I hate the AI hype and, at the same time, I think AI is very interesting," said Linux creator Linus Torvalds, urging patience and caution amid claims of AI replacing jobs within the next decade. Enjoy the full story here. | | | | | | | Why is Zuck Selfish with Open Source? | | | | | | | With the release of Llama 3.1, Zuckerberg has established himself as the king of open-source AI. Contrary to popular belief, Zuckerberg has admitted that the pursuance of an open-source strategy is driven by somewhat selfish reasons related to the tech ecosystem, as he wants to influence how the models are developed and integrated into the social fabric. “I mean, this might sound selfish, but after building this company for a while, one of my goals for the next 10-15 years is to ensure we can build the fundamental technology for our social experiences. There have been too many times when I’ve tried to build something, only to be told by the platform provider that it couldn’t be done,” said Zuckerberg at SIGGRAPH 2024, citing the success of PyTorch. Read the full story here. | | | | | | | Google announced the launch of a 2-billion parameter model in the Gemma 2 family, offering best-in-class performance across diverse hardware. It also announced ShieldGemma safety classifiers to filter harmful content, and Gemma Scope tools for researchers to examine decision-making processes. DeepLearning.AI founder and chief Andrew Ng recently launched a new short course Embedding Models: From Architecture to Implementation, covering word, sentence, and cross-encoder models, BERT training, and building dual encoder models for semantic search, taught by Vectara's Ofer Mendelevitch. AI security company Protect AI has announced the acquisition of SydeLabs, a company specialising in automated attack simulation (red teaming) for GenAI systems. Bengaluru and San Francisco-based AI startup Unscript recently transformed a single photo into a full-fledged video, generating head and eye movements, facial expressions, voice modulations, and body language, achieving studio-quality results in under 2 minutes, significantly reducing manual shooting efforts. | | | | | AI Forum, AIM & NVIDIA Present: Masterclass on Optimizing RAG Models for Enterprise-Grade Accuracy | | | | | | | AI Forum, in partnership with AIM & NVIDIA, is set to host an insightful workshop on August 9, focusing on the optimisation of retrieval-augmented generation (RAG) models to achieve enterprise-grade accuracy. This virtual workshop, scheduled from 3:00 to 4:30 pm IST, aims to provide participants with advanced techniques and best practices for enhancing the performance of RAG models. | | | | | DECODE: Operational Excellence—Improving Customer Experience with GenAI | | | | | | | Join us for an insightful webinar on August 10th at 3 PM IST, featuring industry leaders Kiran Kuppili and Bala Natarajan, as they discuss the transformative impact of generative AI on operational excellence and customer experience. | | | | | | | | | Cypher 2024 marks a significant expansion as it celebrates its 8th edition by branching out to the USA in addition to its already established presence in India. Browse through the links below to learn more about the different editions of Cypher 2024. These links will guide you to comprehensive event information, including agendas, speakers, registration details, and more. | | | | | Enjoying Sector 6 (formerly AIM Daily XO)? Share it with colleagues or friends – they can sign up here. We love hearing from our readers! Have thoughts on our new format? Questions, comments, or ideas are always welcome. If there’s a specific topic in AI or analytics that you're curious about, tell us! Reach out to us at info@analyticsindiamag.com. Stay tuned for more insights in our next edition!
Curated with ♥️ in Namma Bengaluru | | | | | | | | |
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