KYield, Inc.

Interesting article by Steve Lohr central to one of our most important priorities at KYield, Inc. -- intentionally leveraging our AI systems to diversify and strengthen regional economies. However, caution is warranted as it won't happen by accident, nor is it likely to evolve in such a way without intervention from companies like ours due to the conflicts in SV and Big Techs. Indeed, the current trend is just the opposite -- unprecedented concentration of wealth and market power via transferring the knowledge economy to a handful of LLM firms and Big Techs located in 4 or 5 zip codes. "To date, the regions benefiting the most from the rapidly progressing technology have been a handful of metro areas where scientists are building A.I., including Silicon Valley." “This is a powerful technology that will sweep through American offices with potentially very significant geographic implications,” said Mark Muro, a senior fellow at the Brookings Institution, where he studies the regional effects of technology and government policy. “We need to think about what’s coming down the pike.” Strong agreement with this statement from Mark -- a researcher I've followed for many years. We've been thinking about the impacts from AI for nearly 3 decades now -- safety, security, environmental sustainability, sovereignty, and strengthening economic security to name a few, which are frankly afterthoughts in LLM companies. Many issues to consider when thinking about the future of AI and planning for it. One of the biggest questions is whether the LLM firms will even survive given the level of capital burn and legal risk in copyright and data. Another is the outcome of antitrust and regulation. However, an under-appreciated force gaining momentum is sovereignty -- technical and economic. "Karla Valdivieso, co-founder and chief executive, said it was easier to recruit people to a start-up in Chattanooga. She cited an ample pool of educated workers and affordable housing — two of the key characteristics identified in the study for cities picked as potential winners in the rollout of A.I." Some good news to report. We are collaborating with customers on the KOS to address the above issues. Most industries are aligned with a strong diversified economy, much stronger safety & security than currently possible in LLMs, protection of IP, and sovereignty. Stay tuned. 2025 promises to be a transformative year for our efforts towards realizing these goals.

One of the most comprehensive articles to date on the vast waste of electricity in LLMs, and related environmental damage, by Dawn Stover. Among the most tragic aspects of this still relatively new development is the alignment of big government, big tech and big energy against everyone else, hence the green light from the U.S. government (but not yet nuclear regulators...). The nuclear power industry has embraced this trend due to years of slow pace in commercialization of modular reactors--a technology I've long supported, albeit safely and without controlling influence from Big Tech. The USG has embraced this trend (in some cases promoted) due to unhealthy relationships between Big Tech and politicians, technocrats frustrated by slow progress, and some national security hawks who wrongly believe LLMs provide the U.S. an advantage over China (in reality LLMs have rapidly accelerated the China AI threat). Big Tech has embraced nuclear power of course because LLMs are aligned with their need to break through the scale ceiling and expand dominance over more of the economy in an attempt to keep the market cap bubble inflated. Unfortunately for everyone, including the families of all of the above, LLMs are the highest risk AI method possible (cyber and catastrophic--deaths are already occurring), and causes what would have been unthinkable waste in electricity and water just three years ago. And that's before we even mention that all that waste is due to regurgitating stolen IP from everyone else and transferring the knowledge economy to a handful of companies. Meanwhile, safe, responsible, affordable, and accurate AI systems like our KOS are growing organically without a $trillion in subsidies (historic misallocation of capital) badly needed for other purposes across society. Fortunately for us, we are much more aligned with the actual needs of customers. This long article would make an appropriate chapter in a book titled something like: 'How Big Tech and Big Government Lost Their Minds and Credibility Due to Supporting LLM Chatbots'...

Ok, folks -- heads up. Super important topic on enterprise AI. This article reveals a few critical issues we can expand on: 1) Many of the largest companies and those selling AI services like consulting firms have built internal chatbots run on their own data. When done well with precision data management, including access control down to the file level, this pathway can mitigate most of the internal IP risk, but doesn't stop employees from using external/consumer chatbots, and that's a problem. So some have installed surveillance systems (called something else), but employees work round it by using their own personal devices. 2) The problem with this strategy is that to date it's been super expensive to build and maintain, and it requires a massive amount of data to provide much in the way of generalized functionality. Yes, we can each now run our own chatbot of sorts on our own personal data, but even for the most prolific producers of high-quality content, the benefits are limited, and certainly not generalized. 3) At the end of the article, a common flaw is revealed -- untrained staff without prerequisite decades of expert learning assigned the task of developing an AI guide for the org. 4) So where does that leave the super majority of organizations? With the need for refined, secure, and affordable enterprise-wide AI system like our KOS. We thought these issues through many years ago in our R&D. I humbly submit the KOS is much superior to all other known options, including massive spending on custom AI bots. One of the eight functions is knowledge networks that extends DANA (digital assistant) securely to partners, customers or peers -- anyone the organization wants to share knowledge with. Another function is GenAI, the combination of which enables generative functionality without compromising IP security. The other six functions in DANA include data valves to manage the quality and quantity of content consumption (a huge productivity booster), knowledge graphs, prescient search, secure messaging, captured preventions and opportunities, and ultra-personalized learning. All functions in DANA are dynamically tailored to the needs of each individual, providing automated differentiation. Both the enterprise and individual admin have simple-to-use natural language interfaces. An even more powerful option is to further tailor the universal KOS to an industry--specific version, which integrates with financial and operational data for the specific business. We've been working for nearly a year on one for the auto industry, recently announcing a new auto division led by Robert Hegbloom -- (See PR --> https://lnkd.in/eMEFpBpt). Stephanie Stacey https://lnkd.in/ecrQczbU

"It's time to bet against American exceptionalism". This column requires a rebuke, but not for obvious reasons. There is no question about the Big Tech bubble, or excessive national debt and continuing high deficit spending -- strong agreement on those issues. The problem is confusing "American exceptionalism" with Big Techs, or more broadly with excessive marketing power and consolidation, when the truth is that American exceptionalism has historically been driven by a combination of our entrepreneurial culture, rich diversity, and dynamic markets. Big Techs threaten American exceptionalism, they don't represent it! It does not require exceptional ability to milk monopolies for decades or bully competitors with market abuses. What's been exceptional about the American economy in my lifetime has been the ability of inventors, entrepreneurs, creative teams, communities, customers and partners collaborating together to maintain dynamic markets and competition despite excessive market power. American exceptionalism is (mostly) our proven collective ability to execute creative destruction. I would submit that while that ability has been damaged by the abuses of Big Techs and failure of the USG to enforce antitrust for decades, creative destruction is actually occurring. It's never obvious to passive public investors until after the fact. It's only obvious to those of us in the trenches. Bottom line: those who want to participate in American exceptionalism and benefit from it need to stop looking to mature oligopolies that represent the past, and help build the future (a better version).

A thoughtful discussion with Shannon Vallor, written by Philip Ball, on the current manifestation and hype-storm of AI, and the extreme elements in SV that have evolved into what is undeniably a religious cult in LLM firms and portions of Big Techs. I'm in substantial agreement with Shannon on most points. The importance of AI for good can't be dismissed, for example, and no question about the recent trend towards authoritarianism -- even before LLMs, though misused AI is a near perfect tool for them (e.g., the CCP). However, it's important to understand and highlight the difference between Hinton's fear of rogue super intelligence that decides to destroy humanity, which I've always said was far too premature and distracting, and the greatest risks consumer LLM chatbots have already created. LLMs are indeed stochastic parrots, but they are more than that. Due to the vast, unprecedented data scale scraped on every topic, including scientific journals, run on the most powerful supercomputers, combined with inherent security flaws that invite jailbreaks by evildoers, as I posted earlier today -- LLMs are among the top two or three cat risks today. When combined with nuclear bombs, bioweapons/pandemic risk, and efforts to undermine civilization -- particularly by authoritarian regimes, I think consumer LLMs made available to the public represent the greatest risk to our species today. Moreover, these types of risks evolve quickly in chain reactions, undoubtedly to include 'sleeper cells' in state-backed groups. Melanie Mitchell and David Krakauer are probably correct in suggesting that it may represent a new type of intelligence. At very least it's the largest and most dynamic representation of collective intelligence to have ever been unleashed, for better and worse, with all the limitations and risks inherent with the technology, the way it's been recklessly applied, and the powerful perverse incentives driving it.

Good work by EY via the WSJ CIOJ. Their AI 'Pulse Survey' asks 500 US senior business leaders "about their company’s AI investment level". One of the best I've read relative to what we are seeing in the trenches. We might conduct surveys in the future as our reach is among the greatest in the industry over a longer period. “Generative AI’s ‘terrible twos’ have been both volatile and shown incredible promise,” said Whitt Butler, EY Americas Consulting Vice Chair. “Leaders are banking on AI as the future but our research uncovered challenges like data infrastructure, which are holding back adoption. Leaders must put emerging and evolving risks like data and change management at the top of their AI transformation agenda to maintain momentum and realize adoption.” “Data infrastructure and management are table stakes for maximizing the potential of AI, but too many organizations are falling behind,” said Dan Diasio, EY Global Artificial Intelligence Consulting Leader. "Companies urgently need to build knowledge assets, capturing their unique expertise and processes, which will prove especially important as agentic AI models come online and revolutionize how we work.” I strongly agree with both of these statements. What many still don't understand is that in order to achieve competitiveness in EAI and remain competitive, it requires a highly refined EAI OS like our KOS. Those who are still struggling with legacy systems requiring large numbers of data wranglers taping the enterprise together can't possibly compete with native EAI systems with end-to-end data management, strong embedded governance and security, and eight functions like our KOS. Of course not all other things are ever equal, but if they were even closely competitive otherwise, it would be impossible to compete against a refined EAI OS. One of the nice side-effects is that adopting the KOS eliminates AI fatigue in the digital workplace -- admin is all conducted with a simple-to-use natural language interface, at the enterprise level and individual level with DANA.

Nice work on the impending limits of data scale, which have long-been exceeded if limiting to licensed and legal data training, as should have been the case for commercialization. While synthetic data can help with some types of applications, it won't help much for the needs in healthcare, business and government--they need precision accuracy, security and efficiency. There is nothing whatsoever efficient about training on the world's public data other than for theft and related transference of the knowledge economy to a handful of companies. Note near the end of the article, Nicola concludes with the need to do 'more with less'. What's so strange about the LLM hype-storm is that it's so obviously driven by the strategic desires of Big Techs, which is so blatantly conflicted with the needs of customers and society, as well as science. This obsession with scale was mostly about getting big bucks out of Big Tech to initially chase dreams of AGI while being obscenely overpaid for doing so, and has expanded in an attempt to overpay an army in the AI arms race. Good science, on the other hand, is about doing things as elegantly as possible -- as efficiently, securely and aligned with the needs of society. Anyone who has studied the works of great scientists should be fully aware that it isn't about selling far more stuff than needed in the most wasteful manner possible, and creating unprecedented risks for society in the process. That's much more similar to organized crime than science, regardless of denial or degree of justification for the 'greater good' lost in the fog of 'the end justifies the means'. Nonsense. It's not that the goals of consumer LLMs are too grande -- it's that they are much too petty and limited. Intuition based on evidence suggests that super intelligence won't occur unless and until we gain efficiencies that are superior to the human brain. The level of computing and power consumption currently needed to approach a single human brain is insane, and that's due to the obsession with scale, which is being driven by the needs of Big Tech. Our Synthetic Genius Machine R&D is just the opposite -- towards massive compression and super-efficiency to eventually achieve super intelligence...in a super safe and secure manner.

A must read for a glimpse at safety testing in LLMs (gift link), which is a self-regulated red teaming process for the most dangerous and valuable technology in history, in firms under immense pressure to release unsafe systems, raise capital, etc. Stuart Russell is correct in his quote: “I actually think we don’t have a method of safely and effectively testing these kinds of systems”. An unbiased professor who has studied related issues for decades vs conflicted grad students earning instant wealth. Let me describe what's actually occurring in blunt fashion: 1) Anthropic (featured in article) is among the most responsible of the LLM firms--often rated highest by independent testers, but the technology is inherently flawed from a safety perspective (porous text instructions). 2) Expert hackers have proven the ability to jailbreak all LLMs in about an hour (work around all safety precautions). 3) Red teaming is commonly used by the DoD and others for the purpose of learning, but every general/admiral is fully aware "no plan survives first contact with the enemy" (due to experience, training and wisdom), whereas LLM firms are mostly run by grad students with zero real-world experience. Moreover, the JCS doesn't make the decision to go to war, and certainly not defense contractors who would earn billions, which is the equivalent of what's occurring with LLM firms & Big Techs. 4) Any other industry with a similar risk profile would be shut down immediately by regulators. Just one of infinite numbers of examples is LLM bots helping to plan assassinations. It was an early scenario tested that proved so effective it caused fear in red teams. I haven't seen any evidence yet, but the assassination of Brian Thompson, CEO of the American UnitedHealthcare, could have easily been planned by an LLM bot by trivial avoidance of safeguards. The information was likely available to LLM bots. LLM bots are very good at connecting dots, even if incorrect at times, due to training on vast amounts of data on the most powerful supercomputers ever built, which were then unleashed to the public without access controls or any other EFFECTIVE safety measures. Yes, the same information is available in web search, but in many real-world scenarios it would require several lifetimes for humans to find data that LLM bots have already scraped and can summarize in 2-5 seconds. A time lag is critical for law enforcement and intelligence agencies to prevent cats. As someone who has studied both AI systems and catastrophic risks for decades, including all major human-caused cats, I can confirm the conclusion of Stuart Russell quoted above. There is no way to make LLMs safe other than to control access like we do with any other advanced tech with cat risk -- like nuclear weapons, bioweapons, etc. Bottom line (again): Any system that can accelerate discoveries can also accelerate catastrophes in the wrong hands.