Some Red State/Blue State Indicia
- Thread starter nsacpi
- Start date
Stop posting about this - Zito said this was overblown and the figure would come in very low of real fraud.
sturg33
I
it just seems to me that leftists should be MORE outraged than the right. These are THEIR programs that they champion and tell us are necessary to help the most needy people, and the programs are being robbed to enrich politicians and their foreign friends.
Outcomes are irrelevant to the left. Its about process and intention.
Can't wait to see whats happening in CA/NY/IL
Its too good
Tapate50
Well-known member
Instead they have been quiet and reinforced they would vote for him again
Paper morals
nsacpi
Expects Yuge Games
Anthropic, the developer of the chatbot Claude, plans to raise $10 billion at a valuation of $350 billion before the new investment, according to people familiar with the matter, nearly doubling its valuation from four months ago.
GIC, Singapore’s sovereign-wealth fund, and Coatue Management plan to lead the new financing, the people said. The funding round, the third megadeal in the past year, follows a $13 billion investment in September that valued the company at $183 billion.
The round is expected to close in the coming weeks. The total amount of the deal could change.
GIC, Singapore’s sovereign-wealth fund, and Coatue Management plan to lead the new financing, the people said. The funding round, the third megadeal in the past year, follows a $13 billion investment in September that valued the company at $183 billion.
The round is expected to close in the coming weeks. The total amount of the deal could change.
nsacpi
Expects Yuge Games
In the wee hours of an August morning, an artificial-intelligence project manager at Google loaded the newest creation from its DeepMind lab onto a platform that ranks AI models.
Google had fallen behind in the AI race, while its rival OpenAI had attracted hundreds of millions of users to its ChatGPT chatbot. Google researchers were hoping that a new feature—a lightning-fast image generator—would give the search titan an edge in a weak spot for ChatGPT.
Naina Raisinghani, known inside Google for working late into the night, needed a name for the new tool to complete the upload. It was 2:30 a.m., though, and nobody was around. So she just made one up, a mashup of two nicknames friends had given her: Nano Banana.
Within days, Nano Banana had the top spot in performance rankings on the platform LM Arena, was trending on X and had far exceeded Google’s usage expectations. By September, Google’s Gemini AI app had become the most downloaded app in Apple’s app store.
Two months later, Google launched its most powerful Gemini model yet, which surged past competitors to become the most capable AI chatbot. With that, the Alphabet-owned company had leapfrogged OpenAI to the front of the AI pack.
Google’s deep roots in science and research, willingness to pour billions of dollars into developing custom hardware, and leadership changes in recent years that cleared the way for faster experimentation are now paying off. It also has managed to protect its all-important search business—at least for now—from the surging popularity of chatbots, which are changing how consumers use the internet.
Google’s AI work has begun generating substantial revenue through search ads, paid versions of Gemini for consumers and business and sales of new computer chips developed in-house. The November release of Google’s latest Gemini model outperformed ChatGPT on a variety of measures, sending Alphabet’s stock soaring and triggering a Code Red inside OpenAI. That company has since narrowed the race with the launch of a more powerful version of ChatGPT, which still has far more users than Google’s Gemini.
Google Chief Executive Sundar Pichai talked up the magnitude of the company’s AI push on the day the new Gemini model launched. “Love to see that we’re launching at the scale of Google,” he told employees in an internal memo.
When Pichai rose to the top job at Google in 2015, AI was a technology of keen interest to computer science researchers and almost no one else. The following year, he declared that the company known to consumers for its search engine, maps and productivity tools was going all in on AI.
In a memo posted to the company’s blog, Pichai wrote that the previous decade had been all about a smartphone-oriented world. “But in the next 10 years,” he predicted, “we will shift to a world that is AI-first, a world where computing becomes universally available.”
Google already had laid the foundation with an AI research division called Google Brain, which was co-founded in 2011 by Jeff Dean, a computer scientist who helped develop the neural-network technology that underpins today’s large language models. A few years later, Google acquired DeepMind, the London-based AI research lab co-founded by Demis Hassabis, a chess prodigy who would later share a Nobel Prize for work on an AI system that aids biomolecular research.
In a move that drew less attention at the time, Google also started designing its own AI chips, believing it would need vastly more computing power to support applications such as voice recognition. Those chips, called tensor-processing units, or TPUs, were designed to draw less power than the central-processing units in computers or the graphics-processing units in videogame cards. They would prove a game changer, for Google and the industry.
The November launch of Gemini 3 triggered another bottleneck in computing capacity. It is a problem that Google has been anticipating for more than a decade, and its solution—the AI computer chips it developed—is looking like a competitive edge. Its latest chip, called Ironwood, has helped significantly reduce the cost of running its AI models.
The news in late November that Google was in talks to sell Meta billions of dollars worth of the chips for its own AI efforts was enough to sink shares of Nvidia, the world’s leading chip maker, by 7% that day.
In an internal memo to employees this December, Pichai sounded a triumphant note. “We’re ending 2025 in a great position,” he wrote. “Thinking back to where we were as a company even just a year ago, it’s incredible to see the progress.”
The good news is that the technological spillovers generated by companies like Google and Nvidia are enormous and will help to power growth in both red and blue states in the years ahead.
Google had fallen behind in the AI race, while its rival OpenAI had attracted hundreds of millions of users to its ChatGPT chatbot. Google researchers were hoping that a new feature—a lightning-fast image generator—would give the search titan an edge in a weak spot for ChatGPT.
Naina Raisinghani, known inside Google for working late into the night, needed a name for the new tool to complete the upload. It was 2:30 a.m., though, and nobody was around. So she just made one up, a mashup of two nicknames friends had given her: Nano Banana.
Within days, Nano Banana had the top spot in performance rankings on the platform LM Arena, was trending on X and had far exceeded Google’s usage expectations. By September, Google’s Gemini AI app had become the most downloaded app in Apple’s app store.
Two months later, Google launched its most powerful Gemini model yet, which surged past competitors to become the most capable AI chatbot. With that, the Alphabet-owned company had leapfrogged OpenAI to the front of the AI pack.
Google’s deep roots in science and research, willingness to pour billions of dollars into developing custom hardware, and leadership changes in recent years that cleared the way for faster experimentation are now paying off. It also has managed to protect its all-important search business—at least for now—from the surging popularity of chatbots, which are changing how consumers use the internet.
Google’s AI work has begun generating substantial revenue through search ads, paid versions of Gemini for consumers and business and sales of new computer chips developed in-house. The November release of Google’s latest Gemini model outperformed ChatGPT on a variety of measures, sending Alphabet’s stock soaring and triggering a Code Red inside OpenAI. That company has since narrowed the race with the launch of a more powerful version of ChatGPT, which still has far more users than Google’s Gemini.
Google Chief Executive Sundar Pichai talked up the magnitude of the company’s AI push on the day the new Gemini model launched. “Love to see that we’re launching at the scale of Google,” he told employees in an internal memo.
When Pichai rose to the top job at Google in 2015, AI was a technology of keen interest to computer science researchers and almost no one else. The following year, he declared that the company known to consumers for its search engine, maps and productivity tools was going all in on AI.
In a memo posted to the company’s blog, Pichai wrote that the previous decade had been all about a smartphone-oriented world. “But in the next 10 years,” he predicted, “we will shift to a world that is AI-first, a world where computing becomes universally available.”
Google already had laid the foundation with an AI research division called Google Brain, which was co-founded in 2011 by Jeff Dean, a computer scientist who helped develop the neural-network technology that underpins today’s large language models. A few years later, Google acquired DeepMind, the London-based AI research lab co-founded by Demis Hassabis, a chess prodigy who would later share a Nobel Prize for work on an AI system that aids biomolecular research.
In a move that drew less attention at the time, Google also started designing its own AI chips, believing it would need vastly more computing power to support applications such as voice recognition. Those chips, called tensor-processing units, or TPUs, were designed to draw less power than the central-processing units in computers or the graphics-processing units in videogame cards. They would prove a game changer, for Google and the industry.
The November launch of Gemini 3 triggered another bottleneck in computing capacity. It is a problem that Google has been anticipating for more than a decade, and its solution—the AI computer chips it developed—is looking like a competitive edge. Its latest chip, called Ironwood, has helped significantly reduce the cost of running its AI models.
The news in late November that Google was in talks to sell Meta billions of dollars worth of the chips for its own AI efforts was enough to sink shares of Nvidia, the world’s leading chip maker, by 7% that day.
In an internal memo to employees this December, Pichai sounded a triumphant note. “We’re ending 2025 in a great position,” he wrote. “Thinking back to where we were as a company even just a year ago, it’s incredible to see the progress.”
The good news is that the technological spillovers generated by companies like Google and Nvidia are enormous and will help to power growth in both red and blue states in the years ahead.
sturg33
I
Gonna be hard for their founder to pay that new wealth tax California is pushing!
nsacpi
Expects Yuge Games
I don't think the wealth tax proposition will pass. Being the state that helps create so many billionaires is a problem every other state would kill for.
sturg33
I
that it is being discussed as a serious idea is a total indictment of the sanity of this state
nsacpi
Expects Yuge Games
It is bad policy and would be a dumb move by the voters. Even so, I would not advocate for disenfranchisement just because someone voted in a way I considered dumb.
Tapate50
Well-known member
What does it do to create them that is unique ?
Why are they still upside down for their budget with so much wealth ?
nsacpi
Expects Yuge Games
The higher education system in California sets it apart. Especially Stanford and the schools in the Cal system. It has by far the best state university system. If you were to compile a top 10 or top 20 of state universities they would include the following Cal schools: Berkeley, UCLA, San Diego, Davis, Irvine, Santa Barbara. The Cal State system is quite strong too.
The schools are strong in computer science, the natural sciences and engineering. They are also culturally unique in terms of entrepreneurial culture. Stanford led the way in terms of pioneering that culture. But the Cal schools have adopted that culture as well. There are other great universities that generate a lot of innovation. MIT for example. But even a school like MIT doesn't fully share in that entrepreneurial culture.
None of this happens by magic. It takes a commitment by the state.
In a low key way, California's state government is quite forward-looking as well. For example, it has a well-elaborated plan for being at the forefront of the next big thang: quantum computing. It developed this plan (which involves various public-private partnerships) before leaders in other states even had an inkling about what quantum computing was.
I haven't even mentioned Caltech and UCSF in this discussion. But ANY other state, red or blue, would die to have an institution of that quality and especially one that is as integrated as those two are into the surrounding entrepreneurial ecosystem.
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