HackerEarth’s Post

TOP 3 AI Startups to Watch Today 🧠🔍 You know as well as I do that there are so many news stories right now and it’s so easy to get lost in the sheer volume. Today, I’ve selected a few AI startups that are worth knowing about and have been making headlines over the past month. ⛏️ KoBold Metals: startup that uses AI to locate critical mineral deposits KoBold Metals has just made headlines by raising an impressive $491 million in its latest funding round—an infusion that values the company at a staggering $2 billion. What sets KoBold apart? They’re on a mission to change the mining industry by using AI to sift through massive datasets, to pinpoint valuable deposits of copper, lithium, nickel, and cobalt. Earlier this year, the company hit a significant milestone with the discovery of what could be one of the largest high-grade copper deposits in history, nestled in Zambia. 🗂 Poolside: AI Assistant from GitHub's former CTO Though the company is still in the shadows, yet to publicly release its product, it has already attracted a remarkable $626 million in venture capital. Poolside, founded by Jason Warner, has ambitious plans to develop an AI assistant specifically for programmers. Poolside’s $3 billion valuation, marks it as one of the fastest-growing startups in the burgeoning AI landscape. This is not the first assistant we’ve seen on the market, but it definitely earns our trust. 🌐 World Labs: AI models with spatial intelligence capabilities The "godmother of AI" has raised $230 million to help the company develop AI models with spatial intelligence capabilities. World Labs plans to build large world models (LWMs) by 2025 that use data such as images to generate 3D worlds. These will let users interact with and control them. Which of these startups caught your attention? Share your thoughts in the comments! 💬 #aistartup #startup #futuretech #aitech #innovation #startupreview #top #aientrepreneur

There is A LOT to be said about King Sam Altman Midas' mad quest for the Holy AGI Grail, but for this post I'll stick to one tiny detail that involves YOU: If you think that expanding the chip manufacturing industry to these gargantuan dimensions is a good idea, you also have to be pro significant gargantuan expansion of mining. Yes, mining. You know, digging holes in the ground to collect precious metals. Because chips are made with very tangible precious metals, not fairy dust. Which, by the way, also means that if you invest in this mad quest, you can also throw your corporate ESG reporting through the window. But hey, who cares if we can get AGI. Then we can all go on vacation to a barren, poisoned landfill built on top of massive electronic waste and mining sludge. *** I am an AI and business strategist, professional keynote speaker, and board director. I am also a K-1 fighter, metal fan, and eternal rebel. Everything I publish is written by my human self, with a touch of AI-enabled research and illustrations. Follow me or connect for thought-provoking and sometimes humoristic perspectives on artificial intelligence and human stupidity.   #artificialintelligence #generativeAI #responsibleai #ethicalAI #sustainableAI #genAI https://lnkd.in/dfGCHFpy

This was a very interesting thought, and certainly brings to light some observations I’ve made over my time involved with serious science. I feel that there is a great deal of emphasis on commercialisation and application. This is of course fantastic and absolutely the right thing to be doing if we want to continue to positively impact as many people as possible. But I also feel that the rate and aggression at which commercialisation is pursued, is sometimes a little too fast. In doing so, I feel that the “boring” gritty details (things like mechanisms of action, understandings of secondary interactions, even wider applications) get overlooked. I think if we continue along this path, we risk creating an unsustainable scientifc ecosystem, whereby things are short lived, confined to singular applications, and discarded before their time. I believe that it will be the “little things” that will actually be of most benefit and add to the superiority narratives that enterprise thrives on. Sometimes what could, at first glance, seem like a step backwards (or at least not an immediate giant leap forward) is actually a series of well paced steps in the right direction over a large period of time. And that is what gets you further!

[theory on the uptick interest in deeptech/hardware from VCs *with a concern/observation] - opportunity set outside of it isn't compelling/attractive (much of the low hanging fruit in software has been picked) - abundance of elite talent entering the space (similarly with crypto a few years ago (and still is the case if you like deep enough) and AI the last couple of years) - markets are massive and growing (huge % of GDP, The DoD awards multiple 8-9 figure contracts daily) - Elon, Palmer, Karp, Thiel, Lonsdale - strong returns from recent exits in deeptech (10x genomics, archer avitation, auris) and large valuations for private deeptechs (SpaceX, Anduril) - and these exits are happening quicker than traditional counterparts - global political unrest (China/Iran/Russina, Israel v. Hamas) However, I wonder how much of the recent uptick is driven by the need for VCs to deploy their immense amounts of capital quickly (bc of investment periods) and deeptech offers a place to invest a lot of money quickly b/c of it's capital intensive nature.* Samo Burja argued "The reason that so much capital flowed into AI wasn't just because AI was wonderful and exciting, it's because there was nothing else around." Is the same playing out in deeptech, but not because of lack of good alternatives, which may be a part of it, but because there's a lot of dry powder and managers have investing periods meaning they need to deploy their capital within a given period of time and deeptech can consume a lot of it, quickly, with good risk-adjusted returns. *There are respectable people and both sides of that statement. Bessemer wrote, "Deep tech businesses are capital-intensive...so the opportunity cost of capital is relatively high when deciding whether to invest in deep tech." Leo Polovets, whom I respect deeply, thinks it's not as clearcut. "My analysis found that companies across traditional and deep tech sectors are similarly capital intensive. The median company has raised 11-15% of its exit size in prior funding, which runs counter to the common assumption that to get a $1b exit in deep tech, you need $500m+ in funding."

There is always a joy to come back to a course as an alumni and share about topics close to one's heart and also a topic of interest among new participants. Yesterday, I was invited by Prof Edward Tay back to SMU Academy (Adv Certificate in Venture Capital) to share about how Generative AI Tutor can help participants to learn about investing quickly, perform due diligence list, generate term sheet, draft agreement, prepare for startup mentoring, or even learn from famous investors. An investor will never be replaced by AI, he will be overtaken by another investor who know How to leverage on AI to be faster, better and more accurate. Wei Sun Chua, Hira Nadeem, Desmond Ling, Winston Foo, Carolyn Tan, Kai YANG, Pei San Lok, Sher Khan, Dawood Suleman, Kaylene Wong, Doreen Chai (蔡尚君), Pearla Sim Aaron Choo, FRM #angelinvesting #venturecapital #aitutor

I am truly and genuinely happy for all my friends and colleagues who are seeing absolutely stunningly huge investments being made in their various quantum computing and AI start-ups. But I heard a talk today by a guy in the Netherlands who has a proposal for testing some of Penrose's theories and has had trouble finding suitable funding and it made me think about the enormous gulf that now exists in funding circles. Take a certain company that recently received venture funding of $300 million. Now compare that to those of us who need only a few hundred thousand dollars (or even less in many cases – you'd be surprised how much a theorist can do for as little as $10,000) and consider how difficult it has become to actually obtain research grants these days. If a full third of that $300 million – so $100 million – was, instead, used to support small-to-medium sized grants, imagine how many scientists could actually get funded. And the company would *still get* $200 million of venture capital. Concentrating an increasingly large amount of money in the hands of an increasingly small amount of people is a terrible way to innovate and a surefire way to kill basic science.

Silicon Valley Thoughts At Mento vc, we review about 100 projects a week, and 85-90 of them contain an AI thesis. This is no surprise: Silicon Valley and San Francisco have long been the AI capital of the planet. What surprises me is that the talk here is no longer about AI, but about AGI (Artificial General Intelligence). This is a form of AI that is capable of understanding, learning, and applying knowledge in any area of human activity, just like humans do. AGI will come, and it's only a matter of time. The questions that currently concern IT people in the Valley are: - What will we, as humanity, do when AGI arrives? - How quickly will AGI be able to spread? - What regulations will be needed, and are they needed? And investors are in an even greater duality. On the one hand, they invest a lot of money in AI projects, on the other hand, they understand that AGI will come and kill most of them. For investors, this means writing off investments. Over the past six months, at least, we have added a point to our project review: what will happen to the startup if AGI appears? In general, when you spend a long time in the Valley, you may get the feeling that the whole world is already thinking about AGI, and that new discoveries are about to happen, but Portugal is very sobering for me: there everyone enjoys life - the sun, delicious food and a leisurely look at the ocean, where every second person is on a surf... We live in interesting times, friends!

LLaMa 3 is a key part of Meta's AI strategy and represents tremendous progress in just a few months time. Who do you think will win the AI foundation model arms race? To me, OpenAI is trying to be 'foundation-for-all' akin to the IBM of old. Anthropic is making waves but with an emphasis toward control and safety. Google is pushing Gemini as a means of competing with Azure and AWS, and Meta is pushing open source AI as a way to be like the Android of the market. Feels like we are going to have a pyramid of foundation models with multiple large multi-purpose models from multiple large players at the bottom and more specialized models from various providers moving up as the pyramid narrows.

A recent article "Is the AI bubble about to burst" by David Swan (The Age) included an insight from our co-founder Rhys Davis. "The average value multiplier for AI companies sat at around 25-times, in comparison to the estimated 40-times of Canva and mining companies trading at 70-times... to say AI is ‘overhyped’ ignores the fact that tech and non-tech businesses are exceeding those valuations". Read the full article here: https://lnkd.in/gmF9Fnad #AI #technology #generativeAI #startups