The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 35 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Hennessy: A true equivalent to Silicon Valley will undoubtedly emerge in China
“So it is going to happen in China. I have no doubt about it.”
Hennessy: California and San Francisco would economically collapse without the tech sector
“You know, our cities and the state have such dramatic issues, and yet you pull out the high tech sector. I mean, the state and the city of San Francisco will collapse.”
Hennessy: CISC chip architectures lag far behind in energy efficiency
“So you care about energy efficiency even in these large data centers, and when it comes to that measure, the CISC architectures are far behind.”
Hennessy: The drain of AI faculty to industry will harm long-term innovation
“I think we're in a tricky position right now, especially around the machine learning AI area, where there are lots of faculty who are leaving, and that will hurt the industry in the long term, because that means we're eating the sea corn.”
Hennessy: Silicon Valley's dominance has widened despite expectations of rival U.S. hubs
“If you had asked me 1520 years ago, will there be another Silicon Valley in the U.S., I would have said yes, for sure. In fact, just the opposite has happened. The Valley's lead has gotten bigger.”
Hennessy: For-profit higher education fails to deliver real value to students
“The for-profit industry, unfortunately, in the higher education space doesn't deliver a lot of value, so you end up with lots of students who are not able to use their education to get ahead.”
Hennessy: Technology is the sole path to lower education costs
“We've got to figure out how to deliver a high-quality education, not decrease the quality in order to just get the cost down, but hold the quality up while reducing the cost, and the only way I know how to do that is by using technology.”
Hennessy: Early RISC fragmentation allowed Intel to retain microprocessor market dominance
“Rather than the industry converging on one risk architecture, they converged on three or four. That gave Intel a real lead up because they didn't have to beat anyone. They had to kind of beat these little three or four.”
Hennessy: The Apple iPhone was the true tipping point for RISC dominance
“Yeah, the iPhone was really the taking-off point. Some of the earlier Nokia phones began to use the technology, but then when the iPhone came along... Boom.”
Hennessy: Power is the second largest cost in large data centers
“The fascinating thing people don't realize is that after the cost of the physical servers themselves, The second biggest cost in a large data center is power.”
Hennessy: Professors who start companies become better teachers and researchers
“My experience is the faculty members I know at Stanford have gone out and started companies, are better researchers, they're better teachers, they're all around better because they have a wider range of experience.”
Hennessy: Startups relocate to Silicon Valley due to executive talent scarcity elsewhere
“I remember a startup founded at Mark's alma mater, University of Illinois, and great group of people, they could hire great young engineers because it's one of the best engineering schools in the country, but they couldn't get the kind of middle and upper leve…”
Hennessy: Stanford set zero tuition for families earning under $100,000 a year
“We decide we need a very simple message, right? Your family makes less than a 100,000 dollars a year. Your tuition at Stanford is zero.”
Hennessy: Interdisciplinary education cannot substitute for deep core domain knowledge
“I don't believe that multidisciplinary or interdisciplinary things are a substitute for some deep domain knowledge. I'm a firm believer that you start with deep domain knowledge and then you build on top of that.”
Hennessy: Most interesting unsupervised learning applications are in natural sciences, not business
“And the space where that works, sort of unsupervised learning, is such a small part of the giant ML space. It's relatively small. And most of its interesting applications are in the natural science world, not in real world applications.”
Hennessy: Bell Labs and Xerox missed major economic gains from key inventions
“Bell Labs and AT&T were not the major beneficiaries of the discovery of the transistor. Xerox was not the major beneficiary of the discoverer of modern personal computing, right?”
Hennessy: Universities naturally transfer technology to society via student graduation
“That's why universities are the ideal place to do this kind of work, because society benefits. Universities do technology transfer in a very natural way. It's called graduation.”
Hennessy: Hardware breakthroughs take much longer to show impact than software
“Many times when you find a fundamental breakthrough, its importance may take a really long time to emerge, particularly in the hardware sector. It moves so much slower than software.”
Hennessy: IBM and DEC missed microprocessors by building increasingly complex machines
“They were building machines which were getting increasingly complicated rather than simpler. And they missed the whole importance of the microprocessor and VLSI and how we completely changed the industry.”
Hennessy: Global RISC processor chip volume exceeds 50 billion units
“Oh, more than that. Probably fifty billion.”
Hennessy: Foundational tech startups must target underdogs seeking a competitive advantage
“Companies are always a little reluctant to take a risk on a startup, particularly with something like a new architecture, which really is a long commitment. So what you had to do was find companies who felt like they needed a leg up. Over the other players in …”
Hennessy: Leaders facing a crisis should execute tough cuts quickly
“If you have a crisis and you've got to take a tough step, do it quickly, get it over with, and move through. Reset the clock so you can Then charge ahead.”
Hennessy: Top decisions in complex organizations are always gray and ambiguous
“In a complex organization, all decisions are gray when they get to the top. And so you've got to get comfortable making decisions, making calls in that situation.”
Hennessy: Commercialization flexibility gives universities a major recruiting edge for faculty
“So you're a young person, you've got multiple faculty offers, you might be interested someday in taking your technology out. Where's the place to come? Well, it's pretty obvious where the place to come is, and that's a big benefit to the university in terms of…”
Hennessy: Stanford required full financial aid recipients to work campus jobs
“Even though your tuition is zero, you have to work for the university 10 hours a week during the year and 20 hours a week during the summer and contribute that to your education.”
Hennessy: Post-bachelor's education will shift toward skill-based certification models
“We're moving very quickly towards a certification type model where you take a course or a sequence of courses, right? So you go and take the sequence of courses on cryptography and blockchain, and you become an expert on that, and by demonstrating that you've …”
Hennessy: Algorithmic thinking is a fundamental meta-discipline everyone must learn
“It is unique, and it is this meta-discipline, I mean, I think, and it's become the new meta-discipline that everybody needs to learn. Because algorithmic thinking is such a fundamental thing about how the world operates these days.”
Hennessy: Scientific innovation requires a new breed of interdisciplinary machine learning experts
“And this is a big gap right now because the senior people in the field, it's highly unlikely that most of Most of them are going to take a year or two out and go back and learn a bunch of things about computer science and statistics and machine learning ideas.…”
Hennessy: Machine learning is the ultimate garbage-in, garbage-out technology
“ML is the ultimate garbage in, garbage out technology, because if the data isn't good and properly validated and the learning process isn't You're going to get assumptions and outputs that are ridiculous.”
Hennessy: Startups must aggressively eliminate any projects that aren't potential home runs
“And that was good advice about how to think about a research career, but it doesn't work in a company. You've got to get rid of those things that are not the home runs.”
Hennessy: Computer science now attracts top academic talent over biological sciences
“I thought 10 years ago that computer science was going to become second to the biological sciences in terms of getting the best students, and that Everybody, the really best students were going to go do the biologicals, biotech, things like this. Well, that's …”
Hennessy: Mobile and IoT growth made RISC processor architecture crucial
“And in this case, with the explosion of the mobile world and Internet of Things, Efficient process architectures became really crucial, and that really changed the world, and that's why our work has had such great impact over time.”
Hennessy: Early RISC commercial adoption was driven by gaming and networking
“Yes, that was one of the earliest breakthroughs for the RISC people in the embedded space were games, high-end network switches, places where there was really high-end color printers, where there was really a fair amount of performance demand, but also conside…”
Hennessy: Stanford lost roughly 28% of its endowment during the 2008 crisis
“About 28% of the endowment vaporized in a six-month period.”
Hennessy: Machine learning is driving scientific breakthroughs across biology, chemistry, and astrophysics
“You just see breakthroughs in biology and chemistry, in astrophysics. Coming out of various forms of machine learning. So all of a sudden it becomes this tool that is applicable to a whole range of things and is changing those fields.”