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Strategic Computing's Machine Acquisition Program
This piece is an accompaniment to today’s MOSIS piece. So please read the MOSIS piece before starting this one.
Pattern Language Tags:
Promoting a coordination/service mechanism to reduce material costs and increase research feedback cycles
In the early 1980s, as DARPA’s Strategic Computing Initiative was getting underway, lack of cheap computing power was one of the primary concerns of DARPA performers. The work of MOSIS in reducing the cost of VLSI chips for chip research was one major “infrastructure” investment by DARPA that helped overcome a cost issue in an area of strategic importance for the research community. Another group of performers plagued by a similar problem was DARPA’s corp of software/AI researchers and their need to acquire high cost machines to continue with their current research agendas. PM Ronald Ohlander — who managed many AI projects for IPTO as the Strategic Computing program began — undertook the SC Machine Acquisition Program to help DARPA researchers overcome this cost problem. The program — carried out in a similar period to MOSIS and riding a similar technological wave — would have much more mixed results than MOSIS.
With the launch of SC in 1983, Ronald Ohlander launched the Machine Acquisition Program under the SC umbrella. The program was meant to help boost the productivity of SC’s AI research community. The Machine Acquisition Program would use SC resources to buy those in the AI and software research community LISP machines — the high level programming language popular for AI research at the time — to meet their needs. The program sought to do so with significant cost savings by negotiating bulk discounts with manufacturers. One major drawback was that these specialized LISP machines could only be networked to other LISP machines at the time and could not be time shared, but the AI research community was confident that these machines would be a massive factor in enabling cutting-edge AI research in the coming years.
So, Ohlander went forward with the program. ISI — the org that had won the MOSIS contract — also won the right to run this much lower touch contract. Approximately six months after ISI first proposed to serve as the machine acquisition center for SC, it had won a sole-source justification to service the early part of the contract and made its first purchase — in March 1984. With ISI’s first purchase of 30 Symbolics 3600 LISP computers — which cost a total of $2.8 million total at the time (~$8 million today) — the program was off and running.
ISI charged no overhead rate to DARPA for the contract and only asked to be compensated for the cost of the machines purchased and the time and resources ISI invested into the contract. The primary service provided by ISI — besides buying machines for researchers on SC’s behalf — was working with the staff at computer manufacturers to negotiate bulk discounts for the researchers. Given how limited the market for LISP machines was — which is primarily what the program would specialize in buying — the demand for LISP machines by DARPA researchers represented a substantial portion of the market. So, ISI’s job was primarily to find a way to negotiate deals so DARPA researchers’ collective demand could translate to lower prices.
Beyond this function, ISI carried out a handful of services to enhance the user experience of the researchers and limit the headaches that are sure to arise when acquiring new machines in a young manufacturing area. ISI would operate the machines at its office for about thirty to sixty days to ensure there were no problems. And, when there were problems, they would coordinate with the manufacturer to either repair the machine or have a replacement sent.
Ohlander and DARPA required that Common LISP be offered on every machine purchased by the program and pushed the research community and industry to settle on the specification of a common LISP language. Ohlander had taken the temperature of the AI research community and they felt that LISP was clearly the best language for their work now and would continue to be moving forward. So, Ohlander and DARPA did their part to ensure that these researchers had more LISP machines sooner rather than later, that they could be acquired as cheaply as possible, and that the community settled on common LISP standards so they could seamlessly build on each other’s work and communicate findings.
By the end of 1985, when the program was in full swing, the Machine Acquisition Program had spent over $8 million on LISP machines for the SC research community. These machines would have retailed for around $50,000 to $100,000 but were acquired for about a 35%-45% discount by the ISI team. Additionally, ISI often secured massive discounts for additional services such as follow-up maintenance from manufacturers. On one occasion, ISI even secured a maintenance fee of $76,176 from Symbolics for services with a book value of $495,000. DARPA received all these cost savings for only the service cost paid to ISI — which was equivalent to about 2% of the cost of what they spent on these massively discounted machines.
However, the program quickly encountered a change in the technical landscape that caused some headaches. Ohlander had set up the program to give the researchers as much choice as possible. DARPA was not going to tell the researchers which computers were best suited to pursue their research goals. So, ISI planned to buy whichever machines DARPA’s performers asked them to buy. The program was set up with the four main 1983 manufacturers of LISP machines in mind — Symbolics, TI, LISP Machines Inc, and Xerox. The LISP machines from these vendors were supposed to represent the near future of the field — so the researchers had said. But as early as 1986, the research community had almost entirely shifted its preferences, now preferring the program to acquire them the new general-purpose workstations such as Sun’s Sparc, which retailed for three or four times less than the LISP machines. These machines, made possible by substantial commercial advances in computer architecture and manufacture, were powerful enough to run LISP and more versatile than the more expensive, specialized LISP machines.
The program’s activities contracted substantially during the budget crisis in 1987 and 1988. When the program revived, preferences had once again begun to shift. Performers were beginning to become interested in the new lines of parallel processing machines — which SC had spent significant resources helping develop.
Lessons Learned (and Caveats)
Between 1984 and 1992 the Machine Acquisition Program spent over $15 million on equipment for its research contractors. From a cost-savings perspective, the program was absolutely a success. And from a short-term point of view, all parties had their needs satisfied. Researchers got the machines they wanted when they wanted them. ISI even did a lot of installation and debugging for them. Manufacturers in the relatively small market for LISP machines received a much appreciated demand boost. And DARPA was able to speed up the velocity in which research was done in its AI community, receive its products and services at a substantial discount, and get the research community to agree on common LISP standards to ensure its money was spent on researchers who could iterate on each others’ work as quickly as possible.
But does all of that overshadow the fact that the majority of the money the program spent was on machines that were essentially obsolete within a couple years of their initial purchase? To use an objective term like “mistake” with the benefit of hindsight is probably unfair. The research frontier is constantly shifting and the role of people like DARPA PMs is to shape that frontier, adapt rapidly when things change, and also make peace with the fact that money will sometimes be spent on dead ends. Whether or not the decision to fund the program was considered a mistake internally would depend on whether Ohlander and DARPA management had adequately considered this possible outcome and accepted the risks, or if they were somewhat blindsided by the outcome.
Looking at the landscape as they would have seen it, in 1983, is a useful exercise for PMs. In 1983, those who worked on SC programs generally understood that most of the AI/software applications coming out of the work SC was funding would build on top of the computer hardware and architecture developments DARPA was funding in the early 1980s. While projecting technological developments can be messy, it seemed quite clear that orders of magnitude improvements in computing were coming and would impact the researchers who made use of computing equipment in five years or less. In a very active way, SC was explicitly aiming to help achieve the orders of magnitude improvements in computation that they knew to be possible as rapidly as possible. In fact, as I explore in my piece on SC’s three main applications (coming out this Tuesday), some of the programs were counting on one or two orders of magnitude improvements coming by the end of the decade. The rapidly evolving hardware landscape was an area that many SC PMs kept even more abreast of than those in the AI research community. Yet, in spite of the rapid changes occurring in the field, the Machine Acquisition Program invested a yearly amount similar to what SC was spending on MOSIS to buy machines that were only useful to the AI research community as long as the current state-of-the-art only improved along a very foreseeable path — foreseeable to the AI researchers in particular. If Ohlander and DARPA knew this might happen, but felt $15 million was a small cost to pay for getting the AI researchers moving on their projects as fast as possible, it would be hard to call the work this program did a mistake. But if DARPA simply placed too much faith in the foresight of the AI research community when it came to near-term hardware developments, then this is surely a lesson to learn from.
Taking a step back and comparing the similar MOSIS program to Machine Acquisition, it does seem that the MOSIS budget had less downside risk in spite of also being a DARPA program dedicated to investing in subsidized computing hardware for researchers. The MOSIS budget was spent on infrastructure that was less likely to be rendered obsolete by technological advances elsewhere in the SC portfolio or industry. In addition, MOSIS also put itself in the (less risky) position of broker, rather than the (more risky) position of large equipment purchaser. The Machine Acquisition Program was on the hook for a lot of obsolescent, big-ticket hardware that cost $50,000 to $100,000 per unit. MOSIS, on the other hand, was simply paying others for runs on their big-ticket hardware. Given this difference in capital ownership, the MOSIS program was better structured to adapt to unforeseen developments such as those that befell the Machine Acquisition Program. The ISI team, surely, would have been forced to scramble in response to new research developments showing that VLSI standards or λ proportions were no longer useful and the future looked different than they expected. But it does not seem that they would have been in as much trouble as the Machine Acquisition Program if they guessed wrong.
MOSIS had its risks, but it did not share with the Machine Acquisition Program the additional risk of government-purchased big-ticket research equipment sitting in the corner of a lab and serving minimal purpose to the research movement. For MOSIS, if certain specs proved obsolete, certain manufacturers might be similarly hit hard, but MOSIS could simply switch runs to those who were better suited. Of course, MOSIS could only make this switch once it re-wrote some of its software and re-designed some of its services. And while that is inconvenient, this sort of reworking, such as re-writing software or adjusting the Mead-Conway standards, was already built into the organization’s processes. Minor changes of this sort were routine. It is likely that MOSIS could, within reason, likely do whatever internal research work was required to adapt their service to the new world and continue on — shaken but in decent standing.
In summary, the Machine Acquisition Program, in comparison to MOSIS, both incurred more of a financial burden in terms of expensive machine ownership while also being more liable to having those machines rendered obsolete by other investments in SC’s portfolio. The program could still be considered a worthwhile investment if program managers believed one of the following:
It was important to get the AI research community working on DARPA applications projects sooner rather than later. This was likely at least partially a factor since DARPA Director Robert Cooper is said to have insisted that SC begin work on applications programs concurrently with the programs developing the technology base.
There was a chance that very useful near-term applications and learnings would arise from the AI researcher community’s current lines of work with LISP machines — even if they did go obsolete quite quickly.
The PMs considered the money spent and the known risks incurred a small price to pay to seed early work in an interesting research area.
Assessing the project’s execution, it is unclear if the researchers — who were allowed to purchase whatever equipment they wanted — were effectively incentivized and informed enough on the state of hardware research to properly make purchases with DARPA’s money. From one POV, the researchers are the experts and should have final say on what equipment is best to carry out their own research. But, on the other hand, all of the financial downside — and incentives for making prudent decisions like waiting three or so years before spinning up a program like this in the AI community — lied with DARPA. Some could accuse those in charge of the program of not properly weighing SC’s own internal knowledge of the developing technological frontier in hardware research when purchasing millions in soon-to-be-obsolete machines. In retrospect, Ohlander and DARPA could have weighed their own knowledge of the evolving microelectronics landscape more heavily than they did when making decisions on this program.
But it is probably not worth dwelling on what could have been. PM work is often about making “big if true” bets and being willing to incur the losses as long as the wins are big wins. But it is still helpful to know about and learn from DARPA bets that did not pan out. And Machine Acquisition is, to most, a bet that did not pan out. To be sure, the Machine Acquisition Program should probably be remembered for the money it saved as well as the money it had a part in spending fruitlessly.
The Machine Acquisition Program had similar goals and (somewhat) similar strategies to wildly successful DARPA projects like MOSIS (with its goal of reducing chip costs with shared fabrication runs), the ARPAnet (with its early goal of using the network to share research machines and reduce costs), and DARPA’s computer time sharing program (which enabled more researchers to use a single machine). In the end, the Machine Acquisition Program’s outcomes were simply a victim of the risks that Ohlander and SC leadership (hopefully knowingly) took when they acquired infrastructure for researchers in a field whose equipment was rapidly evolving.
While the rising tide of computer chip development did raise all boats in the computer research field, the tide did not have the same positive effect on all existing projects within the SC portfolio. The Machine Acquisition Program provides a useful case study for those making investments that build on top of a rapidly evolving field of technology.
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Particularly useful if the reader wants to understand how Ohlander attempted to subtly nudge researchers away from inordinately selecting Symbolics machines. Symbolics, in the beginning, was the only vendor that had a track record but also was the only vendor that did not offer ISI a steep discount.
Also contains further details about the program’s early history and various operational details.
ISI’s Annual Reports
Section 16 of the 1985 Annual Report outlines the early goals and work of the program. Additionally, this section briefly outlines some areas of military application ISI believed the research area could eventually contribute to.
Summer 1983 - Summer 1984 Annual Technical Report contains brief descriptions of ISI’s first year of acquiring machines and operating the program.