Endings and Beginnings (Part 2 – Gaia Platform)

Shortly after I retired from Amazon my wife and I decided we would need to temporarily relocate to New York to help out with family. Just after we arrived in NY my semi-retirement plan would take a u-turn. Literally. Back to Seattle.

One day I checked my voicemail and on it was the message “Hi Hal, it’s David Vaskevitch. I have an idea I wanted to talk to you about.” David is a friend, was my hiring manager at Microsoft back in the 90s, and is Microsoft’s former CTO. David is perhaps best known for his long-term vision, and had a hand in creating many of the products (and non-product functions like channel marketing and Microsoft Consulting) that Microsoft is best known for.  For example, it was David’s vision for Microsoft to enter the enterprise server market and turn SQL Server (which at the time was a departmental-level targeted port of Sybase) into a Microsoft developed Enterprise-grade database. David put the strategy in place, hired the team leaders, negotiated the deal to split from Sybase, set many of the key requirements, and contributed to the overall design of what became SQL Server 7.0.  Mehtap Ozkan is our third founder. Mehtap brings product and technical judgement, and lots of startup experience, to our team. Soon David, Mehtap, and I were in conversations that evolved into a concrete product concept and fundraising began.

In January of 2019 the first tranche of funding arrived and Gaia Platform LLC was off and hiring. In Bellevue WA, 2550 miles from where I was temporarily living. That was some commute, but fortunately by August I was back in Colorado and commuting to Bellevue on a regular basis. Another failure at retirement for me.

So what was interesting enough to draw me out of retirement this time? One was addressing a problem that I’d been interested in since the 70s, the other was addressing  another platform shift that is underway. I can’t resist a good platform shift, so let’s talk about that first.

In computing we’ve had a number of platform generations over the years, and I’ve managed to participate in all of them to date. Mainframes, Minicomputers, PCs/PC Servers, Mobile, and Cloud are the main ones. I don’t think IoT is, by itself, a platform shift and I see “The Edge” as part of the maturation of the Cloud platform. So what is the next platform? Autonomous Machines. We have some examples today, take iRobot’s Roomba robot vacuums. iRobot has sold over 25 million robots to date, most of which are Roombas. So only a small fraction of the existing ~1.2 Billion households in the world have Roombas. And projections are another 2 Billion homes need to be built by the end of this century to accommodate population growth. Then what about robot mops, robot lawnmowers, autochefs, etc.? We haven’t even gotten to the potentially far larger industrial side of things and we are looking at an eventual market for tens of billions of autonomous machines.

So am I talking about robotics as a platform shift? Not exactly, while many robots will be autonomous machines not all autonomous machines are robots. What is a drone air taxi? Or a medical diagnostic machine you deploy to a natural disaster site? Or an autonomous tractor plowing fields? Overall platform shifts have resulted in a roughly order of magnitude larger number of systems. PCs are order 1 Billion, mobile is order 10 Billion, and we expect there will be 100 Billion Autonomous Machines over some reasonable period of time.

Each new hardware platform shift has also resulted in a software platform shift, though initial systems tried to reuse the prior generation’s technology. Anyone else remember IBM sticking a 370 co-processor in the IBM PC XT (the IBM PC XT/370) so you could run mainframe software on it? Or the DEC Professional series of PDP-11 based PCs? Sticking a PDP-11 processor in a PC form-factor and running a variant of  (the very successful on minicomputers) RSX-11M OS and software stack on it does not a successful PC make. The winner in each generation of platform has been a new software stack designed to support the new use cases.

The need for a new software stack brings us to the other problem, programming is too hard. There are two dimensions to this, making it easier for the professional programmer and making it easy for the non-programmer to create and maintain applications. In the 70s, 80s, and 90s application backlog was top of mind in the IT world. In the 70s even something as simple as changing a report would get thrown on the stack of requests with a year or more waiting period until the limited set of programmers could get around to it. This is why end user report writing tools like RAMIS, FOCUS, Easytrieve, etc. were among the most popular of mainframe 3rd party software. Datatrieve was huge in the minicomputer era. Crystal Reports was an early winner in the PC business software world. Many of these evolved into more end-user query tools. Other “4GL” tools were more oriented towards full application generation, like DEC RALLY, while other end-user data access tools like DEC Teamdata turned out to be the forerunner of today’s end-user BI tools.

From about the mid-80s to the mid-90s we were in a golden age of addressing the need for creating applications without programmers, or to make it easier for application programmers (as opposed to system programmers or today’s software engineers) to build applications. These efforts peaked with Microsoft Access for non-programmers and Microsoft Visual Basic (VB) for application programmers. Before VB the development of GUI application user interfaces was the realm of a narrow set of systems programmers, VB made it possible for all levels of developer to create state of the art end-user experiences for the GUI environment. There were other efforts of course, Sybase PowerBuilder had a tremendous following as well. And then progress seemed to stop. While the later 90s saw lots of new tools emerge to make website creation easier, Microsoft Frontpage for example, that area too devolved into one of extreme complexity (e.g., the current popularity of the “Full Stack Developer”) unless your needs could be met by prepackaged solutions. Today there is somewhat of a renaissance in Low-Code/No-Code solutions, but not at the level of generality that Microsoft Access and Visual Basic provided.

Going back to the professional programmer for a moment, that world just keeps getting more complex the more tools and systems we introduce. Yet we still haven’t addressed key problems that have been with us since (at least) the 80s, such as the impedance mismatch between databases and programming languages. At some point we pretty much declared Object-Relational Mapping Layers (ORM) the solution and moved on. But this is a terrible solution, perhaps best described by Ted Neward as “The Vietnam of Computer Science“.  We think we can do better, not by creating another/different layer but by creating a new database engine that combines the relational and graph databases along with a programming model that is more native for professional programmers. It also sets the foundation for us to provide a Declarative Programming environment that will support increased productivity for traditional programmers, low-code development, and no-code development.

Of course one of the greatest advances in years is that machine learning has now become a useful, nee essential, tool in application building. It is also a form of declarative programming, and we see it as part of our overall platform. But we aren’t going to build new ML/DL technology, we are going to integrate existing technology it into our overall platform. We think about a Perceive-Understand-Act application flow, and ML/DL is the technology of choice for the Perception part. We are building technology for Understand (i.e., database) and Act (i.e., declarative programming) and to integrate these three areas (and other technologies such as traditional programming languages or the Robot Operating System) together as a platform for Autonomous Machines.

It’s a tall order for a startup to be addressing this broad of a space, and platforms are not considered the easiest startup plays, but I like hard problems. Of course we can’t deliver everything all at once, and one of the challenges is figuring out how to put product into the hands of customers early without compromising our ability to make key architectural advances that are critical to delivering on the overall vision. We are working our way through that too. As with almost anything in tech the biggest challenge has been hiring the right set of people to pull this off. Our hiring continues, so if any of this interests you please feel free to reach out.

 

 

 

 

 

 

 

This entry was posted in Autonomous Machines, Computer and Internet, Database, Programming. Bookmark the permalink.

2 Responses to Endings and Beginnings (Part 2 – Gaia Platform)

  1. B Silverberg says:

    Best of luck on your new endeavor with DavidV, Hal!

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