Sarah Wynn-Williams – Careless people
To make the central thesis compelling, it would have been better to split the book in two: one on Facebook’s policy choices and one on how it treats its employees.
“It is essential to understand the underpinnings of the technology so you have an intuition for how the industry is going to change” – Jensen Huang
A juicy founder story if there ever was one.
Make sure to start with many ideas, and to test them early.
Jeremy Utley and Perry Klebahn – Ideaflow
A rather traditional book on the innovation process, but with the merit of a Stanford stamp of approval.
You should relentlessly apply the Pareto principle to how you spend your time!
Dan Sulivan and Benjamin Hardy – 10x is easier than 2x
Motivational books are typically easy to misinterpret.
The only way to control the power of tech power houses and defend the rights of ordinary citizens is stricter regulation
Marietje Schaake – The tech coup
Although the book makes valuable points, the full focus on legislation risks overlooking the importance of thriving innovation for defending long term competitiveness in geopolitical context.
It is great if you can set your own success criteria, achieve them, become financially independent, and have fun in the process
Steve Wozniak and Gina Smith – iWoz
It is amazing how passionate this guy can be about a universal remote control.
AI will not take our jobs, but creep into our workflows and contribute as intern, coach, and idiot savant
Ethan Mollick – Cointelligence
Not wrong, but misses true depth and is overly-reliant on the author’s conversations with Chat-GPT.
Use ‘validated learning’ to launch your ‘MVP’, and ‘pivot’ when your ‘value and growth hypotheses’ are falsified
A staple of startup literature, advocating a deceivingly simple concept which is hard to get right (as is proven by the examples of startups that have failed since publication).
Re-read 2024: Even though some examples are by now pretty stale, there are still many relevant insights in there.
There are no less than 12 primary imperatives that explain how the evolution of technology creates economical, cultural, and social value
A brave attempt to put up a framework for assessing technological innovations, that is rich of ideas, which are in many cases [in 2023] still relevant (e.g. Cognifying in the light of GenAI), but sometimes feel out-dated (e.g. Sharing is a post-truth world).
AI commitments and gaps
U.S. president Bidenś recently announced AI commitments agreed by US government with Amazon, Anthropic, Google, Inflection, Meta, Microsoft, and OpenAI.
Fun fact: Meta unleashed its Llama2 (even though there are questions on its openness) just before committing to protecting proprietary and unreleased model weights.
In any case; the USA has a totally different approach from the EU with their AI Act. These commitments provide a great opportunity to do an early check on how self-regulation in AI could shape-up.
There are three observations that stand out.
Vagueness
It has already been observed that most of said ‘commitments’ made by big tech are vague, generally non-committal, or confirmation of current practices.
Considering the success of the EU in getting big tech to change (e.g. GDPR, USB-C) I am convinced that in tech, strong legislation does not stifle creativity and innovation; but fuels it.
Data void
There are also notable omissions. The one that sticks out for me is the lack of commitment with respect to training data. And that at a moment that legal cases over data theft and copyright infringement are popping up in various places. In that context, Getty Images hopes that training on licensed content will become a thing.
Admittedly, discussions on data ownership are super interesting. But full clarity on the data going into foundational models (and the policies around it) would also sharpen the extent to which data biases may put model fairness and ethics at risk.
Content validation
By far the most interesting commitment is around identification of AI-generated content:
“The companies commit to developing robust technical mechanisms to ensure that users know when content is AI generated, such as a watermarking system“
Considering the expected amount of generated content, I expect not watermarking of AI-generated content (the vast majority of future data volumes) will be problematic.
And it also addresses the problem from the wrong side. In the future, the question will not be “What is fake?”, but rather “What is real?”
This points in the direction of watermarking of human-produced content to be the way forward. Think of an NFT for every photo you make with your smartphone of digital camera. I didn´t hear Apple, Samsung, or Nikon about this yet. But I wouldn´t be surprised if we see announcements in the near future.
There are a huge number of ways in which Artificial General Intelligence (AGI) can take over the world, rendering humanity essentially useless
Nov. 2017: Interesting exploration of the implications of AGI, faulted by the typical preference of Analytical Philosophy for construction of intricate, highly theoretical scenario’s, under-emphasizing basic challenges (in the case of AGI: lack of robustness / antifragility).
Jun. 2023: The writer has leveraged the recent rise of LLMs like ChatGPT to further fuel fear about an AGI break-out – even though other AI-related risks require more imminent attention.
The careful study of ancient wrecks reveals much about how – through the ages and across civilizations – engineers have solved for the same challenges in different ways
Richard Steffy – Wooden Ship Building and the Interpretation of Shipwrecks
It would be worthwhile research topic to map the development of ship building to the principles of disruptive innovation as laid out by Clayton Christensen.
Ongoing advances in technology make that ethical norms develop incredibly fast
Filled with highly interesting statistics about the evolution of public perception on ethical issues.
The philosophy of the Silicon Valley elite is just a bunch of ill understood one-liners from preferably obscure thinkers
Adrian Daub – What tech calls thinking
Entertaining and polemic book, although many of the author’s points hardly need to be argued.
Marginal communities provide natural experiments that convincingly illustrate basic economical mechanisms
Richard Davies – Extreme Economies
Well chosen examples (prisons, refugee camps, declining cities, etc.) illustrate why economics is a social science
Natural philosophy transformed into science thanks to the commitment and sacrifice of some thrill-seeking geeks
Richard Holmes – The age of wonder
Conveys lively how science was considered an undertaking for daring adventurers.
The Silicon Valley philosophy of innovation and disruption undervalues the importance of maintenance and durability
Lee Vinsel, Andrew Russel – The innovation delusion
Funny enough, the polemic narrative applies all the trick of typical innovation literature to promote a maintenance mindset.
To become successful as a startup founder: copy everything you can and only invent what you must
JimMcKelvey – The innovation stack
The book is exactly what it tries to avoid: being just another entertaining founder story (in this case about Square).
The US is losing out in AI, due to a lack of long-term vision and direction
The book’s set-up with multiple scenarios for the future works surprisingly well and is especiall concerning for European readers: Europe is almost completely irrelevant in all of Webb’s scenarios.
To unlock creativity, make sure you get the culture right
The best quote is not from the author: “Quality is the best business plan” (John Lasseter, director of Toy Story).