If you missed Microsoft’s this week, you might want to carve out an hour to read and watch the video. Be sure to watch it end-to-end with relatively focused attention because what you’re about to see is similar to the moment when the first web browser, ’s 1993 (later ) was unveiled.
The fuse for the trillion-dollar economy was lit last year with fairly useful GPT examples. ChatGPT4 is simply an affirmation that this new economy is both significant and near. Microsoft simply validated - at an enterprise level - just how powerful LLMs can change everything.
Today, Microsoft is to LLMs what Netscape was to the world wide web.
While I have no strong affinity for Microsoft or their products since personally abandoning Windows in 2003, the vision they paint (The Future of Work with AI) is quite remarkable. It demonstrates that the rumours were fact; Microsoft has been quietly working with OpenAI and GPT models since early 2020 to be able to exhibit this much forethought and vision of future work with paired-AI copilots.
In 60 minutes, Microsoft wholly dismembered Google Workspaces, which now looks as if it were built by a team of five-year-olds who missed the Mensa cut. Heads will likely roll at Google, and they’ll catch up over time. Still, in the meantime, the tens of millions of businesses and hundreds of millions of users who jettisoned Microsoft Office to feast on a low-cost collaborative alternative to the office suite will be grazing in a pasture that is arid, brown, and unable to nourish the appetite we crave for advanced productivity in the AI economy.
But more important, micro pilots as well. The nature of AI and the implementation approaches vary. They can start small and grow in complexity. I’ve already created some interesting experiments in various products from note-taking apps like . The diverse possibilities to enhance work performance are almost infinite. At we use GPT with to perform summations and entity extraction for CyberLandr support.
Copilots will soon exist everywhere, and if you aren’t using them or building them, you’ll miss the opportunity to participate on the ground floor of an emerging trillion-dollar economy. Worse, you’ll watch from the sidelines as your competitors make more deals, provide more advanced solutions, and outsell you on every level.
Google Workspaces and LLMs
I’ve been a huge fan of Workspaces since 2010. I have a historical client base of businesses that have created advanced automation solutions using Firebase, Google Apps Script, and other Google Cloud features and SDKs.
Google struggles to get ahead of the AI movement surrounding OpenAI and its broad array of GPT APIs. But, until it can offer something tangible from its own stable of AI research experiments, GPT will be an attractive and highly useful platform for building many of the examples Microsoft demonstrated this week.
Google’s rich development environment has allowed me to build impressive GPT features intersecting all Workspace document types.
Given a slide deck → write talking points for the presentation. Given AI-generated talking points, create and insert an appropriate image for each slide. Given a large collection of documents in Google Drive, build a search bot capable of locating and summarizing documents based on a simple natural language query. With a corpus of Gmail messages, categorize and report metrics about the conversations. With a new spreadsheet, use the column headings to generate 100 sample rows of data into the sheet.
The possibilities for integrating GPT features into Google Workspace workflows and documents are limited only to your imagination.
Acquire, Enhance, Do Something with It
GPT and LLMs are ideal for collecting information, enhancing it, and then using it. Ideally, the time required to perform these processes are compressed to create hyper-value for workforces. If instrumented well, the benefits impact two key dimensions of work.
Compress the time needed to gather and enhance information
Not only does AI help us do more in less time; it helps us create vastly more information about our work.
There’s a slice of automation and hyper-productive work that intersects with browser plugins as well as OS-level apps. (for example) dancing around a very powerful idea - the ability to design no/low-code recipes (playbooks) that act as copilots. It still lacks a number of features to reach the status of seamless copilot integration and workflow benefits, but it is well-poised to generate paired-AI assistants to workers. and even have equal appeal as copilot building blocks.
The Reach of Copilots
Some consultants eat their own dog food; they run their entire business on the same solution patterns they advocate for their clients. Most, however, don’t.
With AI and LLMs, it will be a very different climate; purveyors of AI solutions must use these future work patterns or risk disruption. Imagine a web design firm competing for business in 1998 without a modern-looking website. Imagine if Expedia executives used travel agents while promoting the disruption of the travel industry. Imagine if Apple execs stayed with Blackberry post-2007.
The depth and impact of AI are tantamount to deep-rooted disruptors we have witnessed in our lifetimes. This is not a superficial advancement that provides a new sheen to dull application surfaces.
I’m old; my career should have ended a decade ago. Delightfully though, integration and automation demands have kept me somewhat relevant. Oddly, my near-fifty-year experience appears to be the prologue to a new, emerging, trillion-dollar adventure.
Like Microsoft, I quietly started learning everything I could about GPT, LLMs, embedding architectures, vector databases, and search index architectures four years ago. I sense I’ll be working with copilots to build copilots until at least 2036.
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