Ordinary Chaos

Tag: investment

  • Why Index Ventures is bulking up its investment team in NYC

    Why Index Ventures is bulking up its investment team in NYC

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    While online discourse would make it seem that venture has retreated to the Bay Area, with San Francisco being the most important place to build a startup, Index Ventures is looking to bulk up its New York-based investing team.

    The firm is currently looking to hire another New York-based investor with plans to add three or four new people to the team within the next year, Shardul Shah, a partner at Index Ventures, told TechCrunch. That’s an aggressive addition to the current 10-member team.

    “For a venture fund, that’s hypergrowth,” Shah said, adding that Index is trying to “capitalize on the ecosystem here, and the energy we have as a team.”

    Shah said there is a lot to like about the New York ecosystem that is different from San Francisco’s. While the Bay Area may have better density when it comes to engineering talent and venture capital, Shah said that New York has it beat in one key area: density of customers. This is especially true for companies building in the health or financial fields, he said. While a plethora of investors is helpful for early-stage startups, a deep pool of customers is what really helps companies grow sustainably. The city’s diversity of industry is another plus, too, Shah said.

    He added that it’s also a natural place for firms to maintain a presence if they have portfolio companies or colleagues in both San Francisco and Europe. He added that European companies expanding to the U.S. generally set up shop in New York first, which is another interesting stream of potential deal flow.

    It probably doesn’t hurt that Index has already garnered a successful portfolio in New York. The firm was early an investor in some of the city’s largest startup winners, including Datadog, which went public with a $7.8 billion valuation in 2019, and Cockroach Labs, which was valued at nearly $5 billion in its most recent funding round in 2021.

    Index was founded in 1996 in Geneva and has expanded into a new geography about every 10 years, Shah said. The firm opened its New York office in 2022 amid a wave of Bay Area investors expanding east. Lightspeed Venture Partners opened a New York office that year as well. Sequoia opened one in 2023.

    And naturally, this wave is mingling with a number of New York’s prominent, homegrown VC firms like $80 billion in assets under management Goliath Insight Partners and storied firm Union Square Ventures.

    New York consistently maintains its spot as the second largest venture ecosystem in the U.S. Startups in New York raised $12.6 billion in the first half of 2024, according to PitchBook data. While significantly less than the $40.4 billion invested in California startups in the first half of this year, it’s nothing to sniff at.

    According to CB Insights’ unicorn tracker, New York is also home to 122 unicorns compared to San Francisco’s 182. There are, of course, dozens more Bay Area unicorns when adding in those in the greater area (Palo Alto, Redwood City, etc.). But New York has far more of them than any other locale besides Silicon Valley.

    Still, New York’s ecosystem does have a weakness: large exits. Datadog is arguably the most prominent startup exit from the ecosystem and that happened five years ago.

    Index is ready to fund more growth.

    “It sounds like people are going back like 20 years, like when they said Europe is a museum,” Shah said about the current rhetoric. “To say that [venture capital] only happens on the West Coast, it’s not accurate. It’s not even close.”

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  • As war rages in Ukraine, investment in European defense and dual-use tech skyrockets

    As war rages in Ukraine, investment in European defense and dual-use tech skyrockets

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    A billion dollars of venture capital will be invested into European defense technology in 2024, a first for the continent, and a five-fold increase since 2018. The investment comes as a result of both increasing geopolitical unrest and the brutal invasion of Ukraine by Russia. 

    The data, contained in a new Dealroom report, shows that VC investment into defense-related tech is outpacing any other type of investment across the broad spread of NATO member states and its allies by 25%, totaling $3 billion since 2018. 

    The bulk of the investment in the space since then has been captured by startups in Germany, the U.K., and France, which collectively accounted for 87%, or $2.2 billion. German defense tech companies have, alone, raised more in the past six years than those in the Nordics, Netherlands, Switzerland, and the U.K. combined. The news may come as a surprise to some observers, given the caution the German government has exhibited over shipping weapons to Ukraine. 

    Much of that investment was into companies based out of Munich, which topped the list of European cities in the report. But most of that could be attributed to the $487 million raised by ‘battlefield AI’ startup Helsing in 2024.

    Bristol and the U.K.’s ‘Silicon South West’ — best known for its defense and space industries — garnered the next largest amounts for defense investment, followed by Paris.

    Indeed, the U.K., which has a large defense industry, is home to six of the top 10 European cities for defense tech investment in the report — London (4th), Reading (5th), Oxford (6th), Leeds (8th) and Cambridge (9th). 

    The report also details how VC investment in defense tech across NATO countries rose four-fold in the last 6 years, reaching almost $5.9 billion, taking the total raised by defense startups in NATO countries and its allies to $18 billion.

    Furthermore, the report counted 370 VC-backed defense tech startups in NATO countries, which have a combined enterprise value of $161 billion. And defense tech comprises 1.8% of European VC funding, a number that has tripled since 2022.

    Despite Europe’s growth, the U.S. remains the dominant force in the defense tech sector, with American defense tech firms attracting 83% of VC investment. 

    And even though more than half of VC funding for European defense tech startups came from investors on the continent, this year, there was a marked acceleration in funding from U.S. investors, who provided 66% of the capital for European defense tech companies.

    The State of Defence Investment 2024: Resilience Builders in Nato & Europe, published at the Resilience Conference, also outlined how dual-use technologies, which can be applied for both civil and military purposes, have seen a marked rise in interest from investors. 

    Jeannette zu Fürstenberg, managing director and head of Europe at General Catalyst, said in a statement: “By leveraging the power of AI, we can not only enhance our defense capabilities but also develop dual-use technologies with broader applications for critical national infrastructure. As investors, we are driven by the mission to protect democracies and build resilient infrastructure.”

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  • Generative AI coding startup Magic lands $320M investment from Eric Schmidt, Atlassian and others

    Generative AI coding startup Magic lands $320M investment from Eric Schmidt, Atlassian and others

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    Magic, an AI startup creating models to generate code and automate a range of software development tasks, has raised a large tranche of cash from investors, including ex-Google CEO Eric Schmidt.

    In a blog post on Thursday, Magic said that it closed a $320 million fundraising round with contributions from Schmidt, as well as Alphabet’s CapitalG, Atlassian, Elad Gil, Jane Street, Nat Friedman and Daniel Gross, Sequoia and others. The funding brings the company’s total raised to nearly half a billion dollars ($465 million), catapulting it into a cohort of better-funded AI coding startups whose members include Codeium, Cognition, Poolside, Anysphere and Augment. (Interestingly, Schmidt is backing Augment, too.)

    In July, Reuters reported that Magic was seeking to raise over $200 million at a $1.5 billion valuation. Evidently, the round came in above expectations, although the startup’s current valuation couldn’t be ascertained; Magic was valued at $500 million in February.

    Magic also on Thursday announced a partnership with Google Cloud to build two “supercomputers” on Google Cloud Platform. The Magic-G4 will be made up of Nvidia H100 GPUs, and the Magic G5 will use Nvidia’s next-gen Blackwell chips scheduled to come online next year. (GPUs, thanks to their ability to run many computations in parallel, are commonly used to train and serve generative AI models.)

    Magic says it aims to scale the latter cluster to “tens of thousands” of GPUs over time, and that together, the clusters will be able to achieve 160 exaflops, where one exaflop is equal to one quintillion computer operations per second.

    “We are excited to partner with Google and Nvidia to build our next-gen AI supercomputer on Google Cloud,” Magic co-founder and CEO Eric Steinberger said in a statement. “Nvidia’s [Blackwell] system will greatly improve inference and training efficiency for our models, and Google Cloud offers us the fastest timeline to scale, and a rich ecosystem of cloud services.”

    Steinberger and Sebastian De Ro co-founded Magic in 2022. In a previous interview, Steinberger told TechCrunch that he was inspired by the potential of AI at a young age; in high school, he and his friends wired up the school’s computers for machine-learning algorithm training.

    That experience planted the seeds for Steinberger’s computer science Bachelor’s program at Cambridge (he dropped out after a year) and, later, his job at Meta as an AI researcher. De Ro hailed from German business process management firm FireStart, where he worked his way up to the role of CTO. Steinberger and De Ro met at the environmental volunteer organization Steinberger co-created, ClimateScience.org.

    Magic develops AI-driven tools (not yet for sale) designed to help software engineers write, review, debug and plan code changes. The tools operate like an automated pair programmer, attempting to understand and continuously learn more about the context of various coding projects.

    Lots of platforms do the same, including the elephant in the room GitHub Copilot. But one of Magic’s innovations lies in its models’ ultra-long context windows. It calls the models’ architecture “Long-term Memory Network,” or “LTM” for short.

    A model’s context, or context window, refers to input data (e.g. code) that the model considers before generating output (e.g. additional code). A simple question — “Who won the 2020 U.S. presidential election?” — can serve as context, as can a movie script, show or audio clip.

    As context windows grow, so does the size of the documents — or codebases, as the case may be — being fit into them. Long context can prevent models from “forgetting” the content of recent docs and data, and from veering off topic and extrapolating wrongly.

    Magic claims its latest model, LTM-2-mini, has a 100 million-token context window. (Tokens are subdivided bits of raw data, like the syllables “fan,” “tas” and “tic” in the word “fantastic.”) One hundred million tokens is equivalent to around 10 million lines of code, or 750 novels. And it’s by far the largest context window of any commercial model; the next-largest are Google’s Gemini flagship models at 2 million tokens.

    Magic says that thanks to its long context, LTM-2-mini was able to implement a password strength meter for an open source project and create a calculator using a custom UI framework pretty much autonomously.

    The company’s now in the process of training a larger version of that model.

    Magic has a small team — around two dozen people — and no revenue to speak of. But it’s going after a market that could be worth $27.17 billion by 2032, according to an estimate by Polaris Research, and investors perceive that to be a worthwhile (and possibly quite lucrative) endeavor.

    Despite the security, copyright and reliability concerns around AI-powered assistive coding tools, developers have shown enthusiasm for them, with the vast majority of respondents in GitHub’s latest poll saying that they’ve adopted AI tools in some form. Microsoft reported in April that Copilot had over 1.3 million paying users and more than 50,000 business customers.

    And Magic’s ambitions are grander than automating routine software development tasks. On the company’s website, it speaks of a path to AGI — AI that can solve problems more reliably than humans can alone.

    Toward such AI, San Francisco-based Magic recently hired Ben Chess, a former lead on OpenAI’s supercomputing team, and plans to expand its cybersecurity, engineering, research and system engineering teams.

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  • UK’s Wayve secures strategic investment from Uber to further develop self-driving tech

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    Uber is making a strategic investment into Wayve as an extension of the U.K.-born startup’s previously announced $1.05 billion Series C round. The partnership will also see the two companies work with automakers to integrate Wayve’s AI into consumer vehicles that will one day operate on the ride-hail giant’s platform.

    The tie-up comes a week after Uber announced Cruise’s robotaxis would join the Uber app in 2025. It’s the latest in a series of autonomous driving technology partnerships that Uber has secured over the past couple of years. 

    Details about Uber’s partnership with Wayve are scant, but the startup has made a splash since it launched in Cambridge in 2017. Over the past two years, Wayve has raised over $1.3 billion from backers including SoftBank Group, Nvidia and Microsoft. 

    The startup is developing a self-learning, rather than a rule-based system, for autonomous driving – similar to Tesla’s AI. Also like Tesla, Wayve doesn’t rely on lidar sensors. It uses cameras and radar to help its AI perceive the world around it. Unlike Tesla, Wayve builds its AI so that other automakers can equip consumer vehicles with its Level 2+ advanced driver assistance system, as well as Level 3 and Level 4 automated driving capabilities. 

    The SAE defines Level 3 and 4 self-driving systems as ones that can operate autonomously under certain conditions. The driver still needs to be ready to take over a Level 3 system, but not with a Level 4 system. Wayve is currently still testing its L2+ technology on Jaguar I-Paces and Ford E-Transits with safety drivers behind the wheel, and has not begun testing L3 and L4, according to a Wayve spokesperson. 

    Wayve did not provide more details as to the nature of its deal with Uber. In a statement, the company said the partnership “envisions future Wayve-powered self-driving vehicles being made available on Uber’s network.” 

    Neither Wayve nor Uber shared a timeline for Wayve-powered vehicles joining Uber’s app; whether those vehicles would be fully self-driving or just equipped with advanced driving assistance technology; or the size of Uber’s investment. 

    In a statement, Alex Kendall, Wayve’s CEO and co-founder, did say the partnership would help to “massively ramp up our AI’s fleet learning, ensuring our AV technology is safe and ready for global deployment across Uber’s network.” 

    Kendall also noted that together, Wayve and Uber would “work with automotive OEMs to bring autonomous driving technologies to consumers sooner.” 

    Uber’s CEO Dara Khosrowshahi said in a statement that Wayve’s approach to AI “holds a ton of promise” as the company works towards “a world where modern vehicles are shared, electric and autonomous.”

    “We’re thrilled to bring Wayve on as a partner to work alongside automakers as we continue to build out Uber as the best network for self-driving vehicles,” said Khosrowshahi.

    In recent weeks, Uber has been positioning itself as the ideal partner for self-driving startups looking to commercialize. Waymo’s robotaxis joined the Uber platform in Phoenix last year. Uber has also partnered with autonomous sidewalk delivery robot companies – like Serve Robotics, Cartken and Coco – and autonomous freight startups – like Waabi and Aurora – to bring self-driving capabilities to Uber Eats and Uber Freight.

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  • Klaris Clear Ice Maker Review: A Worthy Investment to Up Your Home Bartending Game

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    The machine comes with the mold for four cubes; you can also separately buy a Collins mold that makes three prisms of ice for a taller glass. The molds are made of a thick silicone—much thicker than most standard ice molds. Once you fill the mold up with water, you just place it in the compartment inside, close the lid, push the knob, and let it do its thing. You don’t need to use filtered water. The faucet will work for your water source just fine because all of the impurities in the water, like minerals and gas, are going to get cleared out. I tested cycles with both filtered and tap water and the end result was the same.

    Each cycle takes between eight and 12 hours to complete. That’s kind of a big window. It partially depends on how cold the water you fill your mold with is, and the temperature of the surrounding environment. It would be ideal for the Klaris to have a more concise timeframe for completing the cycle. There is a timer that shows how much time has elapsed but not how much time is remaining.

    In my apartment with an air temperature hovering around 70 degrees Fahrenheit, the Collins rocks took around 10 hours, and the standard cubes took around eight. I’ve heard anecdotes that during the winter in cold regions, it can take as little as five hours.

    Since it takes so long, I forgot to harvest my ice right away a few times; the feature
    that allows you to keep it cold for up to five additional hours is helpful here. The one time I
    completely forgot to check on it even after the delay, everything was back to being liquid water,
    so I just started it over. You obviously need to plan a bit if you have a specific event you want to
    use the ice for. The delay functions overall are a great touch and help you time out your ice
    harvest, since sometimes it will finish overnight.

    Making Things Clear

    But since the machine plugs into the wall, I’m not sure why there can’t be a feature just to keep it cold until you open it back up. How does it work? The water is frozen layer by layer from the bottom up, which is known as directional freezing. (You don’t get this with a mold in your standard freezer because the cold air comes from all directions.) Simultaneously, an impeller-type fixture on the inside of the cover spins the water, which circulates the impurities up and out of the cube. This motion provides the necessary constant agitation, ridding the chance for the impurities to settle to the bottom.

    The impeller needs to be submerged in the water, so you need to fill up the water in the mold to the fill line, which is higher than where the ice will ultimately reach. When the cycle is complete and you open the cover, it almost looks as if nothing happened because there’s a layer of liquid water above the clear ice. This water contains the impurities. You then pull the mold out and dump that water into the sink. You turn the mold upside down and twist and push a little and the rocks pop out. You need to pull them apart from each other with the plastic dividers that are in the mold. These can be a little flimsy and you don’t get extra, so I try carefully not to break them.

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  • Vacant Office Buildings in the United States: An Opportunity for Public Investment?

    Vacant Office Buildings in the United States: An Opportunity for Public Investment?

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    Vacant Office Buildings in the United States: An Opportunity for Public Investment?