One thing that I think Handel gets wrong, in good company with many other commentators, is the idea that US manufacturing is in precipitous decline. In fact only US manufacturing employment is in decline. It peaked in 1979 at 19.5 million jobs. But since that time output per worker has gone from about $80,000 to $234,000 (in 2000 dollars so this is not just inflation). In fact US manufacturing output is only slightly smaller than the entire 2008 GDP of China and the same as the entire GDP of Germany. Total manufacturing output today is 84% higher than in 1979 when employment peaked. As in agriculture before it, it is primarily technology that is reducing employment. Employment in manufacturing is likely to continue to shrink even as the value of manufacturing output rises.

Handel is impressed with the “savvy” technocrats at the head of China and, for good reason, unimpressed with our political masters in Washington. But the usual problems with a planned economy like China’s (at least in a lot of the biggest sectors) is that without price and consumer signals there is a massive misallocation of capital (as there is in the more planned parts of the US economy: corn ethanol anyone?)

Let’s look at just one area, rail. Handel is very impressed with the Chinese commitment to high-speed rail and thinks that the US should have a similar commitment. The US has a “decrepit railroad system”. But the US is a huge country and rail is just not a good transport solution for passengers. But it for freight. The US shifts a far bigger percentage of its freight by rail than any other country. Even with the disparity in wages, freight rates in the US are half those of China. That doesn’t seem “decrepit” to me. It is an unfortunate fact that you can really only run a rail system for freight or passengers but not both. A 180mph passenger train can’t cope with lots of 60mph freight trains on the same track. In Europe and Japan, most of the freight is on the roads since the rails are largely dedicated to passenger traffic. In fact one of the stated motivations in China for building high-speed rail is to free up the existing tracks for freight.

Further, there are problems in China. Most of the high-speed rail already built is only running with about 25% capacity.  The high-speed rail line from Beijing to Fujian was shut down in April after 2 months from lack of passengers since it was so much cheaper to fly. There is no escaping the fact that high-speed rail is very expensive. There is only one line in the world that covers its costs: Tokyo-Osaka (and maybe Paris-Lyon). All the other highs-speed rail lines lose enormous amounts of money every year. Most don’t even cover their operating costs let alone their capital costs. They are only workable because relatively few people use them but everyone pays for them. There doesn’t seem to be a major strategic gain from shifting people from largely unsubsidized planes to subsidized trains.

China is about to face a huge problem: in order to keep growing fast it needs to switch from being completely export-oriented (because the rest of the world can’t absorb 10% more good from China every year) to stimulating internal demand. This is an incredibly difficult transition—Japan still hasn’t managed it, for example. In order to do that, internal demand needs to rise much faster than the overall economy for many, many years, which either means the overall economy has to grow much more slowly, or that internal demand needs to rise at almost unimaginably high rates (and without creating enormous inflation).

Anyway, in summary, I think this is a very interesting book that has some great analysis of the two countries. As I said, I’m less certain of the solutions proposed because governments tend to be very bad at picking winners, they usually pick losers and then keep doubling down on them. But, in any case, I don’t see any of the solutions that Handel proposes ever coming out of the US political system.

The first book isn’t actually about presentations specifically but is one aspect of a few slides in some presentations. It is Edward Tufte’s book “The Visual Display of Quantitative Information.” He has two more books which are also worth reading but they go over much of the material in this first book in more detail. Anyone who has ever put a graph or a table in a presentation (or in an article or white-paper for that matter) should read this book. It is full of wonderful examples of appalling presentation of data as well as some exemplary ones. Too many books on presentations show you some good ones without being brave enough to call out presentations that don’t work.

The next book is very analytical and contains a lot of data about what works and what does not in presentations. It is Andrew Abela’s “Advanced Presentations by Design.” One key finding is that if you put the points you want to make in bullets in your presentation, then when you present it (so you are speaking as well as showing the slides) it is actually less effective than simply showing the presentation and shutting up, or giving the speech and not showing the slides.

Next, two books that really are about putting together presentations. Garr Reynolds’s has a book called “PresentationZen” and Nancy Duarte has one called “slide:ology.” These two books somewhat cover the same material with slightly different perspectives. In fact the blurb on the back of each book is written by the other author. You probably don’t need both of them but you’ll need to look at them both to decide which one you feel most comfortable with. Both books carry on from the analysis I mentioned above, emphasizing that a presentation should be designed to reinforce visually what you are saying, not repeat it textually. A presentation is not a crux for the presenter, not a sort of teleprompter for not having rehearsed enough.

Finally there is Jerry Weissman’s “Presenting to Win.” This is complementary to the other books in that it focuses much less on the visual aspect of a presentation and much more on how to make a presentation tell a story. His track record and focus is putting together presentations for IPO roadshows, which are probably a type of presentation that has more money riding on it than anything else. But most of what he says is appropriate for other types of presentations.

Between these books you get instruction on how to create a compelling narrative in a presentation, how to maximize the visual impact of your presentation, how to display quantitative information compellingly, and more analysis that you probably care to read about what works and what doesn’t in presentations.

Two other resources that I think are good for presentations: any Steve Jobs keynote speech (look at the iPhone announcement if you only look at one) and many of the speakers at TED, which has a 20 minute time-limit and so forces speakers to maximize their impact and focus on the most important messages.

For example, assume you are a manager deciding how big a factory (or fab) to build. Your marketing manager tells you he is certain that you’ll sell between 80,000 and 120,000 per year. But you insist on a number and get given the average of 100,000 and you build a factory with a capacity for 100,000. Let’s assume that the marketing manager nailed the numbers precisely (don’t we always?). On average how much money will you make? Well, the number will be somewhere between 80,000 and 120,000. If the number is less than 100,000 you make less money than you expected. If the demand is greater than 100,000 you don’t make more money because your capacity is maxed out. So, on average, you make less money than you expected even though your factory has average capacity.

There are other fascinating things. You may have heard of Simpson’s paradox. One of the most famous examples of this was a 1986 kidney stone study where treatment A was more effective than treatment B. But if you looked at only small kidney stones, then treatment B was better than treatment A. And if you looked at only large kidney stones, then again treatment B was better than treatment A. But when the two were combined, A was better than B. WTF?

Another example: in each of 1995, 1996 and 1997 David Justice had a higher baseball batting average Derek Jeter. But taking the three years together, Derek Jeter had a higher average than Justice. WTF?

A lot of what you learned in school about statistics (means, variance, correlation etc) is really not very relevant now that we can run large numbers of investigations as to what is really going on in seconds. Means and standard deviations were an attempt to get at something important before this capability existed, what Sam Savage calls “steam era” statistics. Now we can use computation to make sure we don’t fall into traps.

There’s also lots of stuff about options and how to price them depends on thinking (or computing) this sort of thing properly. If a stock is $20 today and on average will be $20 in 12 months time, how much should you pay for an option to buy it for $21 in a year. If you’d succeeded in answering this a few decades ago you’d have won the Nobel prize. You may have heard about Black-Scholes option pricing, which does the math to work this out. Even though at the average stock price ($20) an option to purchase at $21 is worth nothing (because you’d simply not exercise your option) it clearly is worth something since there is some chance that the stock will end up above $21 and you can make money exercising your option and selling it at the market price.

I haven’t finished the book yet but I can see already that some of the ideas are important in thinking about business plans and formalizes some of the sensitivity analysis that it is always good to do (how much more money do we need to raise if the first orders come 6 months later than expected? if the product costs 30% more to develop?).

Consider a drunk walking down the middle of a highway. His average position is in the center of the road on the yellow line. But on average where is he. Dead.

And don’t forget, almost everyone has more than the average number of legs.

The book looks at semiconductor as an economic issue rather than from a technological point of view (although this is not ignored) which fits in with my view of the world. Semiconductor process transitions are driven by economics (cheaper transistors) more than technology (better transistors) especially now where leakage and other considerations make it unclear whether you are getting better transistors or only more of them.

The book examines how the semiconductor industry has lurched between major crises that has driven both its success and its restructuring over time. It starts back in the 1980s when the US, having essentially invented the integrated circuit, started to lose the quality war to Japan. It examines 8 crises in total.

First, losing the memory quality war to Japan that eventually drove most US memory supplies (Intel, for example, remember they were a memory supplier) out of the market. Most readers probably don’t remember when HP announced how much better the quality of Japanese memories was compared to American, and how it shook the industry to the core (they had lots of data).

The second crisis was the rising cost of fabrication. The result of this in the US (but not elsewhere) was the creation of fabless semiconductor companies that used TSMC, UMC and Chartered to manufacture. Also the creation of clubs of companies sharing the cost of process development.

The third crisis was the rising cost of design. This meant that low volume products just were not economically viable. Chips used to be consumed by big corporations largely insensitive to price to consumers who were hyper-sensitive to price. This was the fourth crisis. Somewhere in here the FPGA started to play a role.

The fifth crisis was the limits to Moore’s law, in particular limitations in lithography (Moore’s law is more about lithography than any other aspect of semiconductor manufacture). This has been an ongoing issue forever, of course, but has started to become the fundamental limitation on progress.

As the number of people involved in a design, and their cost, increased out of control there was a rush to find new talent in India and China. Partially for cost reasons but also because there were too few designers available without looking globally.

But fabs got more and more expensive, and price pressure on end-products got more intense leading to the current situation where most companies cannot afford to build a fab nor develop a leading edge process to run within it. There are just 4 or 5 groupings now that can do this (Intel, Samsung/IBM/ST, TSMC, Japan, UMC/TI) and there is likely to be further consolidation. Even with tapping into low cost Asian labor , semiconductors are not getting the share they feel they should of the electronics value chain.

The 8th challenge is the new level of global competition. Japan is clearly, for example, losing out as a “Galapagos market” with lots of internal competition but, as I’ve said  before, turning their back on the world, just like how the Galapagos produced giant tortoises. But also there is governmental competition with states attempting to join the industry keeping global competition feverish.

The book has a great graphic that summarizes the change in the basis of competition over time. If you read from left to right you see the problems come up chronologically. The vertical scale splits them into technological problems, economic problems, and competitive/globalization issues. This single graphic pulls together all of the issues facing the semiconductor industry, and how it got here, in a single simple chart.

As I said earlier, if you are involved in the challenges of the semiconductor industry, this is a book you should read (and, in case anyone is suspicious, I’m have no relation with the publisher other than receiving a free copy).

The heart of the idea of the book is that you don’t know what the customer wants. So in addition to developing a product (preferably the minimum shippable product, since how do you know the customer even wants that?) you need to develop customers. You have a product development process. You need a customer development process. And hiring a VP of sales and a VP of business development and waiting around for engineering to ship doesn’t count.

A secondary idea is that the customer development process is very different if you are creating a brand new market, entering an existing market or re-segmenting an existing market (producing a product that only serves part of the market, usually but not always either creaming off the high-end or disrupting the low-end).

It is hard to summarize an entire book in one blog post and I don’t intend to try. You’ll have to invest in the book yourself and I guarantee that you will find plenty of thoughtful ideas that are immediately applicable to almost any product launch, whether in a startup or a large company.

If you only take one idea away from the book it would be this: get out of the building. Startups don’t fail for lack of technology, they fail for lack of customers. Heed Steve’s words: “In a startup, no facts exist inside the building, only opinions.” You have to go and talk to potential customers and even talking won’t be enough. You’ll have to ship them early product, burn them when it doesn’t do what they needed, and correct your course. If you scale the company before you have the product right, you’ll run out of money (and in the current climate you’re not getting any more).

The idea of listening to customers is not to find out everything that they want and build a laundry list. It is to attempt to narrow the product down to the minimum shippable product, one that at least a few customers can get value from even if it doesn’t do everything they want. Saint-Exupery’s quote that “A designer knows he has achieved perfection not when there is nothing left to add, but when there is nothing left to take away,” summarizes the goal for the earliest stage of customer development. Then start iterating as your understanding increases.

If you read newspaper articles on startups, you often get the idea that a couple of guys in a garage really understood something deep and took down some huge corporation that was too dumb to notice. The reality is that almost all successful startups end up doing something different from what they first intended when they were founded, sometimes dramatically so. Look at Paypal (originally doing beamed payments from Palm Pilots) or more recently Twitter (part of a podcasting company). Or even Google (originally just doing search without a clue about how to monetize it). In EDA the changes are less dramatic but very few business plans survive after contact with the market.

There are a number of reasons that EDA startups are not getting funded by venture capitalists any more. The most obvious is that large EDA companies have stopped buying them at high valuations. This is for a mixture of reasons but one is that standalone tools are harder to ramp up profitably without tightly integrating them into the main body of pre-existing tools. For example, standalone statistical static timing is interesting, but much more important is integrating statistical static timing into the synthesis, place and route flow. Don’t just find the errors after the fact, stop them occurring in the first place.

But a second reason that EDA startups are unattractive is that they don’t require enough money. Venture funds are growing larger and it is a fact of life that being on the board of a company looking after a $3M investment is about the same amount of work as looking after an investment of $30M. If a fund is large, it can’t afford to dole it out $3M at a time; that requires too many investments. Instead, fewer but larger investments are required. This means that the size of investment is too large for an EDA company. Too large in two ways: too large since EDA tools don’t require that much capital to develop, and too large since the exit price required to make the investment successful is higher than is likely to happen.

I’ve been somewhat involved with several startups recently who are looking about how to raise a little money. Relatively small amounts are needed and venture capitalists are simply not the place to go looking. Individual investors (angels) and corporate investors (customers) are much more likely. New technology continues to be needed and this type of investor can live with the likely return on a successful company.

The new rules are raise only a tiny amount of money, run the company on a shoestring, validate the technology with some initial sales and exit earlier rather than later. If you wait, you will need more money to build a big channel, and any acquirer will have to tear it down anyway. EDA startups spend more money building sales channels than technology, and one thing Cadence and Synopsys don’t need more of is channel.

One thing that the book points out is something I’d not really thought about. The sales cycle for an EDA tool is about 9 months. What do you think the sales cycle for an EDA company is? More, a year or two from first contact to closed deal. If you are going for an early exit, the sales cycle for the company is about the same as the time you need to develop the product, so you need to start selling the company before you found it!

T.J. Rodgers, the CEO of Cypress Semiconductor, is also a critic of government intervention in the economy, especially that of Silicon Valley. Whatever you think of the strategic decisions that he made at Cypress, most of which look pretty good in hindsight, he is a great writer. I wish I could write as clearly and interestingly as he does.

If you haven’t read it then I highly recommend reading every word of his 1993 testimony to Congress in the Clinton era, “Free Market or Government Subsidies?” It is especially worth reading in the light of the current extensive intervention in the economy by the government in all sorts of arbitrary ways. Luckily they aren’t intervening  much yet in technology. Of course, on one level, it would be nice to get some stimulus money, but without the interference that comes with it.

One area that technology that does have a large government dimension, at the very least in competing for the same VC money, is the environment. I regard most of the current venture-capital investments in “green” technologies largely as bets on governments subsidizing them whether they are economic or not. For instance, did you know that Germany, that famously sunny country with its enormous deserts, is the biggest installer of photovoltaic solar power, accounting for almost half the world market? And with the senate dominated by states that have few people but lots of agriculture, there are no prizes for which country has the most subsidies for turning expensive food into ethanol, a nasty fuel that corrodes pipes, attracts water and produces lots of aldehyde pollution at the tailpipe. Not to mention uses roughly as much energy to produce as it generates when used.

T.J. Rodgers’s plea to Congress in 1993 for a balanced budget (given that the budget was eventually in surplus on Clinton’s watch) looks absurd today, given the way we are racking up an unpayable tab. But his testimony from 15 years ago stands up really well today. This slide from Intel shows just how deep a hole we are digging (and don’t forget Medicare dwarfs all this).

Even more recommended is T.J. Rodgers’s piece for the Cato Institute, “Why Silicon Valley Should Not Normalize Relations with Washington,” that does a great job of contrasting what he, T.J. Rodgers, worries about on a daily basis, with what Dwayne Andreas, then-CEO of Archer Daniels Midland worries about. T.J. Rodgers worries about semiconductor technology, borrowing money, how much to invest in solar, which products to design. Andreas worries about…well, making sure that Congress passes the right laws to ensure that he can farm the subsidies. ADM is the largest beneficiary, even before the last changes of the law, of the policy of insisting that a certain amount of ethanol gets added to gasoline.

Since I’m having a rant, I’ll finish with a blog comment by someone called Bob on a response to a piece on…whatever. Anyway, it’s here if you want to read it in the original Latin. But I think it is a great expression, even if a little exaggerated (the "whole" point, hmm) of the difference between how Silicon Valley works and how Washington works, complete with Silicon Valley imagery in the last few words. I think T.J. Rogers would be proud:

“I reject the premise that capitalism is currently failing to ‘deliver the goods.’ The whole point of capitalism is to destroy companies like GM and Chrysler. The whole point of capitalism is to destroy unions like the UAW that favor older and retired workers at the expense of younger workers and workers yet to come. The whole point of capitalism is to destroy the "smart guys" who create defective financial products. The whole point of capitalism is to punish us for electing a government that enmeshes itself with the jokers above. The ‘current unpleasantness’ is a feature, not a bug.”

And, less this be interpreted as some sort of complaint about the current administration, remember that lots of this stuff, the first financial bailouts, Fannie Mae, GM bailout number 1 etc all happened on the Bush watch. It’s more of a plague on both your houses. Besides, so far most of that creative destruction has yet to occur so the shakeout is just being delayed.

The key insight of the book is that the mainstream buys for different reasons than early adopters. As a result, it is much harder than you would expect to turn success with early adopters into success with the mainstream. Getting from this early success to the nirvana of mainstream adoption is crossing the chasm, the chasm being the fact that you can burn all of your money trying to get across unsuccessfully if you ignore what is necessary for success.

The big idea in Crossing the Chasm actually comes from an earlier book by Bill Davidow published way back in 1986 Marketing High-technology (still in print), which first introduced the idea of the “whole product.” However, Geoffrey Moore did a much better job of explaining it and the chasm metaphor was a much more viral image.

Early adopters will do their own work to make up for deficiencies in your product, especially tailoring it to work in their environments, adding missing scripts or libraries and generally working out how to get the most value out of your product. Mainstream customers will not do that. You need to deliver them everything that you need, the whole product. You may not need to deliver all this yourself, but you need to create an ecosystem so that everything is available.

A good example is the early days of Synopsys. You can sell a synthesis product like Design Compiler (DC) to a few early adopters on the basis that they will do their own work to take existing simulation cell libraries and manually create the .lib libraries necessary for synthesis. However, the mainstream will not. The mainstream wants the whole product, one that they can use from day one. This means not just DC but also .lib libraries for whichever library they happen to want to use for fabrication. So in the early days Synopsys had a huge group of engineers creating these libraries for the ASIC vendors. I think Bob Dahlberg, who ran the group, told me that it peaked at 200 people. Within a year the ASIC vendors realized that they needed to do this job themselves since they didn’t want Synopsys’s library group to be on their critical path to revenue from a new process node.

This shows another point, that once you start to achieve success in the mainstream, you become part of someone else’s whole product and they need to support you to be successful themselves.

The whole product becomes a barrier to entry too. Once Synopsys had all the ASIC vendors on-board, they were not likely to want to create libraries for other synthesis tools. So Mentor’s Autologic, Compass’s ASIC Synthesizer, Trimeter, SILC and all the other struggled not just because Synopsys could invest more in developing synthesis but also because nobody else could get the whole product together easily.

And here’s someone who gets it, although isn’t sure what to do about it. Chris Wilson, the CTO of NuSym, complains that there is now so much infrastructure required in a simulator (3 versions of the API, several testbench languages, Verilog, VHDL, SystemVerilog, C) that it takes all their effort just to do that and very little is used to deliver the core differentiated technology. Of course it would be convenient for him if someone else provided all that so that they can focus on their core technology, but nobody does. Synopsys didn’t want to develop ASIC libraries either. But he knows he won’t be successful without full compatibility.

Coincidentally, both Bill Davidow and Geoffrey Moore both ended up in the same venture capital firm, Mohr-Davidow Ventures (MDV). When we finally got MDV over the finishing line to invest in VaST while I was there, we ended up being invited to a half-day meeting with Geoffrey Moore. This lead to a dysfunctional conversation since I knew that the Mohr in the name of the company was spelled differently so I figured that somebody was confused about who we were meeting. But I was wrong: Geoffrey Moore was (and is) a partner of MDV. At that point VaST was having some early success in a handful of companies, mostly in Japan, and so we spent a very interesting afternoon brainstorming how we could create an ecosystem of models which we all knew was the main barrier to getting across the chasm.

Although published in 1991 it is still in print. It covers how accounting used to be useful to managers, starting with New England mill owners in the 19^th century. However, as the accounting rules and processes were hijacked by financial accounting they have become steadily more and more useless for managing the business. Nobody wants to keep multiple sets of books so managers try and manage using accounts put together for financial accounting reasons on a timescale driven by financial accounting deadlines.

The situation is even more disconnected in the case of a software or design company. Much of the real value of the company is bound up in partially or completed software products (or designs). The rules for capitalizing development are so strict that it must only be done when the product is pretty much released. Almost all the development is written off as an expense as if it were part of the utility bill, as opposed to an investment building up value in the company. From a point of view of keeping the tax paid by the company low this may be desirable; from the point of view of the balance sheet giving a useful assessment of the company not so much. Design Compiler is clearly a major asset of Synopsys but you won’t find it on the balance sheet anywhere, either as an estimate of its value as a forward looking business or even as a rollup of the cost of development over the years.

Other intangible assets, such as an effective high-skilled development team, appear nowhere. If a key employee leaves the value of the company may well have changed in a meaningful way but this is nowhere reflected. It is completely unclear how one would actually account for this in any sensible way, of course, but it sort of happens anyway. Look at the change in market cap of Apple when Steve Jobs is thought to be sick or not, which is actually the value of the asset of having Jobs as CEO that in principle should be on the balance sheet somewhere.

Software companies seem to have very lax financial controls in my experience. I worked for over ten years at VLSI Technology, a semiconductor company. That is a business in which a lot of money flows around but the margins are thin. Fabs cost (today) billions of dollars so getting the accounting right is important. The financial controls and forecasting in a semiconductor company are generally very good. When we spun Compass out we were still consolidated into VLSI’s books and we kept the finance we were used to. Every manager did an expense forecast for 6 months ahead, and monthly we looked at variances to that and were expected to explain them. Startups are small enough that their financial controls, at least for cash, are usually pretty good. But when I got to Cadence I was surprised that even as the manager of the custom IC business unit (then a $250M/year business) I wasn’t expected to forecast my expenses, it was hard to even find out what they were, and as a result they were pretty much whatever they turned out to be. The concept of over-spending didn’t exist. I assume that has changed somewhat now that the financial outlook is less rosy, but that sort of thing is part of the DNA of a company and is actually quite hard to change.