FT: “An AI assistant [is] a personalised bot that is supposed to help you work, create or communicate better, and interface with the digital world on your behalf. This new class of products has stolen the limelight…among a flurry of new AI developments from Google and its AI division DeepMind, as well as Microsoft-backed OpenAI. The companies simultaneously announced a series of upgraded AI tools that are “multimodal”, which means they can interpret voice, video, images and code in a single interface, and also carry out complex tasks like live translations or planning a family holiday…While smart assistants powered by AI have been in train for nearly a decade, these latest advances allow for smoother and more rapid voice interactions, and superior levels of understanding thanks to the large language models (LLMs) that power new AI models. Now, a fresh scramble is under way among tech groups to bring so-called AI agents out to consumers.”
WSJ: “Job seekers, frustrated with corporate hiring software, are using artificial intelligence to craft cover letters and résumés in seconds, and deploying new automated bots to robo-apply for hundreds of jobs in just a few clicks. In response, companies are deploying more bots of their own to sort through the oceans of applications. “You’re fighting AI with AI,” said Brad Rager, chief executive of Crux, a recruiting firm that matches cybersecurity specialists with employers. The AI arms race is bad for job candidates, he said, who feel defeated when online applications come to nothing, and for employers, who are frustrated when imprecise AI tools highlight weak candidates. “There’s so much promise, but there’s a lot of crap and garbage,” Rager said of the tools used by employers.”
NYTimes: “The root of the problem is the Communist Party’s excessive control of the economy, but that’s not going to change. It is baked into China’s political system and has only worsened during President Xi Jinping’s decade in power. New strategies for fixing the economy always rely on counterproductive mandates set by the government: Create new companies, build more industrial capacity. The strategy that most economists actually recommend to drive growth — freeing up the private sector and empowering Chinese consumers to spend more — would mean overhauling the way the government works, and that is unacceptable. The party had a golden opportunity to change in 1989, when the Tiananmen Square protests revealed that the economic reforms that had begun a decade earlier had given rise to a growing private sector and a desire for new freedoms. But to liberalize government institutions in response would have undermined the party’s power. Instead, China’s leaders chose to shoot the protesters, further tighten party control and get hooked on government investment to fuel the economy.”
Jaspreet Bindra: “There is no Google product which is not going to be baptized with AI. Google Search, with 2 billion plus users and 6 million searches a minute, gets a GenAI makeover. Gmail with 1.8 billion users gets a strong dose of Vitamin AI. YouTube’s 1.8 billion users can have AI-generated text summaries of the nearly 4 billion videos that the site hosts. Another 4 billion Android users get AI on tap. The list goes on. Ironically, however, it seems that Google is following the Microsoft playbook here. Microsoft famously had an EEE strategy of ‘Embrace, Extend and Extinguish’: First it created a product using open standards, then created a proprietary extension which quickly gained dominance through its brute distribution and ownership of the PC market, and it finally used this extension to swamp the market and extinguish its competitor. Latest example: MS 365 has 345 million users, 320 million of them get Teams free; rival Slack languishes at 39 million. So, OpenAI the plucky innovator can launch eye-popping products galore like ChatGPT, Sora and GPT4o, but what it lacks is distribution reach.”
Aaron Levie: “The reason I’m insanely bullish on AI is that since starting Box, we have never seen a bigger shift in how we can work with our enterprise information than today. AI completely revolutionizes how we can work with enterprise information. Since the mainframe era, it’s been relatively trivial to work with our *structured* data in an enterprise. We could query, compute, synthesize, summarize, and analyze anything that could be structured in a database – i.e. the data sitting in our ERP, CRM, and HR systems. But it turns out this is only a small fraction of our corporate information. If you were to “weigh” the amount of data inside of an enterprise (in the form of raw storage), roughly 10% of it would be structured data, and 90% of it would be unstructured data. And our content — things like our documents, contracts, product specs, financial records, marketing assets and videos — makes up the vast majority of this corporate data. Yet for essentially the entire history of computing, we haven’t *really* been able to make sense of this information unless a human is involved. Of course we can store it, send it, share it, and search for it — but deeply understanding what’s inside this information in a way that computers can interact with intelligently has been near-impossible. Well, for the first time ever, generative AI actually lets us talk to our unstructured data. Multimodal models especially allow us to process this content using a computer and essentially perform any task that a human can, but at infinite scale and speed. This is utterly game-changing when working with information in the enterprise. Instantly, our content goes from being digital artifacts that get touched once in a while, to a digital memory that anyone in the enterprise can tap into always. All of a sudden instead of the more information you have making things harder to find and make sense of, the opposite becomes true. And we enter a world where your digital information becomes one of your most valuable resources. When we can turn our content into valuable knowledge, everything about how we work changes. A new employee instantly has access to the same expertise of someone who’s worked at a company for 15 years; when you can understand what’s inside of content — like contracts, invoices, or digital assets— and extract its structured data, you can automate nearly any workflow; and AI can let us classify and protect content with a level of precision that’s never been possible before to prevent threats and risks across the enterprise. This is simply the biggest change we’ve ever seen with how we can work with our data, and this is what we’re building with Box AI. In 1945, Vannevar Bush wrote a seminal article which outlined eerily insightful predictions, including the idea of the “Memex”, a new device “in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.” The vision laid out imagined a future where the more knowledge and information your “computer” had, the smarter and more informed you would become. While many aspects of PCs, mobile devices, and the cloud eventually resembled this early vision, the seamlessness in how we could work with our information never quite played out. Until today.” [This quote is so good that I reproduced it entirely. Memex is one of the things I was fascinated with many years ago.]
