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"#silicon valley"
The Tech Baron Seeking to Purge San Francisco of “Blues”
The Tech Baron Seeking to Purge San Francisco of “Blues”
Balaji Srinivasan is a prominent tech figure who is promoting an authoritarian "Network State" movement that seeks to establish tech-controlled cities and territories outside of democratic governance. He envisions a "Gray" tech-aligned tribe that would take over San Francisco, excluding and oppressing the "Blue" liberal voters through measures like segregated neighborhoods, propaganda films, and an alliance with the police. These ideas are being promoted by Garry Tan, the CEO of Y Combinator, who is attempting a political takeover of San Francisco and has attacked local journalists critical of his efforts. The mainstream media has largely failed to cover the extremist and authoritarian nature of the "Network State" movement, instead portraying Tan's efforts as representing "moderate" or "common sense" politics.
·newrepublic.com·
The Tech Baron Seeking to Purge San Francisco of “Blues”
How Big Tech and Silicon Valley are Transforming the Military-Industrial Complex | Costs of War
How Big Tech and Silicon Valley are Transforming the Military-Industrial Complex | Costs of War
Given the often-classified nature of large defense and intelligence contracts, a lack of transparency makes it difficult to discern the true amount of U.S. spending diverted to Big Tech. Yet, research reveals that the amount is substantial, and growing. According to the nonprofit research organization Tech Inquiry, three of the world’s biggest tech corporations were awarded approximately $28 billion from 2018 to 2022, including Microsoft ($13.5 billion), Amazon ($10.2 billion), and Alphabet
From 2021 through 2023, venture capital firms reportedly pumped nearly $100 billion into defense tech startup companies — an amount 40 percent higher than the previous seven years combined.
·watson.brown.edu·
How Big Tech and Silicon Valley are Transforming the Military-Industrial Complex | Costs of War
AI startups require new strategies
AI startups require new strategies

comment from Habitue on Hacker News: > These are some good points, but it doesn't seem to mention a big way in which startups disrupt incumbents, which is that they frame the problem a different way, and they don't need to protect existing revenue streams.

The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents.
There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.
This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required
Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.
Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).
While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.
A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).
AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different
Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.
the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”
·longform.asmartbear.com·
AI startups require new strategies
Magic Mushrooms. LSD. Ketamine. The Drugs That Power Silicon Valley.
Magic Mushrooms. LSD. Ketamine. The Drugs That Power Silicon Valley.
Users rely on drug dealers for ecstasy and most other psychedelics, or in elite cases, they employ chemists. One prolific drug dealer in San Francisco who serves a slice of the tech world is known as “Costco” because users can buy bulk at a discount, according to people familiar with the business. “Cuddle puddles,” which feature groups of people embracing and showing platonic affection, have become standard fare.
·wsj.com·
Magic Mushrooms. LSD. Ketamine. The Drugs That Power Silicon Valley.
Society's Technical Debt and Software's Gutenberg Moment
Society's Technical Debt and Software's Gutenberg Moment
Past innovations have made costly things become cheap enough to proliferate widely across society. He suggests LLMs will make software development vastly more accessible and productive, alleviating the "technical debt" caused by underproduction of software over decades.
Software is misunderstood. It can feel like a discrete thing, something with which we interact. But, really, it is the intrusion into our world of something very alien. It is the strange interaction of electricity, semiconductors, and instructions, all of which somehow magically control objects that range from screens to robots to phones, to medical devices, laptops, and a bewildering multitude of other things. It is almost infinitely malleable, able to slide and twist and contort itself such that, in its pliability, it pries open doorways as yet unseen.
the clearing price for software production will change. But not just because it becomes cheaper to produce software. In the limit, we think about this moment as being analogous to how previous waves of technological change took the price of underlying technologies—from CPUs, to storage and bandwidth—to a reasonable approximation of zero, unleashing a flood of speciation and innovation. In software evolutionary terms, we just went from human cycle times to that of the drosophila: everything evolves and mutates faster.
A software industry where anyone can write software, can do it for pennies, and can do it as easily as speaking or writing text, is a transformative moment. It is an exaggeration, but only a modest one, to say that it is a kind of Gutenberg moment, one where previous barriers to creation—scholarly, creative, economic, etc—are going to fall away, as people are freed to do things only limited by their imagination, or, more practically, by the old costs of producing software.
We have almost certainly been producing far less software than we need. The size of this technical debt is not knowable, but it cannot be small, so subsequent growth may be geometric. This would mean that as the cost of software drops to an approximate zero, the creation of software predictably explodes in ways that have barely been previously imagined.
Entrepreneur and publisher Tim O’Reilly has a nice phrase that is applicable at this point. He argues investors and entrepreneurs should “create more value than you capture.” The technology industry started out that way, but in recent years it has too often gone for the quick win, usually by running gambits from the financial services playbook. We think that for the first time in decades, the technology industry could return to its roots, and, by unleashing a wave of software production, truly create more value than its captures.
Software production has been too complex and expensive for too long, which has caused us to underproduce software for decades, resulting in immense, society-wide technical debt.
technology has a habit of confounding economics. When it comes to technology, how do we know those supply and demand lines are right? The answer is that we don’t. And that’s where interesting things start happening. Sometimes, for example, an increased supply of something leads to more demand, shifting the curves around. This has happened many times in technology, as various core components of technology tumbled down curves of decreasing cost for increasing power (or storage, or bandwidth, etc.).
Suddenly AI has become cheap, to the point where people are “wasting” it via “do my essay” prompts to chatbots, getting help with microservice code, and so on. You could argue that the price/performance of intelligence itself is now tumbling down a curve, much like as has happened with prior generations of technology.
it’s worth reminding oneself that waves of AI enthusiasm have hit the beach of awareness once every decade or two, only to recede again as the hyperbole outpaces what can actually be done.
·skventures.substack.com·
Society's Technical Debt and Software's Gutenberg Moment