TerraFirma, a construction robotics company founded by former SpaceX engineers, has raised $115 million in funding. The round includes a $100 million Series A led by Kleiner Perkins, with participation from Bain Capital Ventures and others. The company develops semi-autonomous construction equipment, including excavators and dozers, that can be operated remotely. Skilled operators control multiple machines simultaneously from screens, potentially making each operator up to 300% more effective. Founded in 2024 by Noah Schochet and Noah McGuinness, TerraFirma is currently working on commercial projects including site preparation for a Starbucks in North Austin and infrastructure projects for the US government. The company aims to address labour shortages in construction whilst building technology applicable for future lunar and Martian construction projects.

AI startups dominate venture capital flow: 300+ deals in July as investors back artificial intelligence. Nearly 28% of all VC deals in July involved AI, as mega-funds close and early-stage investors double down Three hundred artificial intelligence startups closed funding rounds over the past month. That's more than one deal per hour, across every major sector from healthcare to defense tech to robotics. The capital flowing into these companies represents a fundamental reshaping of where venture capital is going - and how fast it wants to get there. The data tells a striking story: in a market where venture capital is measured in billions, AI is now measured in a scale of its own. The AI funding tsunami. July saw 1,082 venture capital deals close across all sectors. Of those, 300 were AI-focused startups. That means nearly 28 percent of all VC activity this month involved companies building artificial intelligence products, platforms, or infrastructure. The absolute scale of capital involved is staggering. AI startups alone attracted $2.3 trillion in committed capital during the period - a number that reflects both mega-fund closures and standard venture rounds. The average AI deal came in at $13 billion, which tells you something important about the composition of these signals: the largest fund closures (Dimension Capital's $800 million fund, for example) are getting bundled into the same data stream as seed-stage AI companies raising $5 million. But even when you account for that mix, the trend is unmistakable. Traditional venture capital firms are pouring resources into AI at a pace that would have seemed impossible two years ago. This isn't just Khosla Ventures or Andreessen Horowitz doubling down on AI - it's every major venture firm on the planet reallocating capital in the same direction simultaneously. The investor stampede. Look at which investors are writing the most checks. Khosla Ventures and General Catalyst led the pack this month, each closing 28 deals. Index Ventures, Y Combinator, and Accel followed close behind with 26, 26, and 19 deals respectively. What's remarkable here isn't just the volume - it's the consistency. These firms aren't taking bets on one AI narrative. They're investing across the entire spectrum: AI model developers, infrastructure plays (compute, memory, chip design), AI applications in enterprise software, AI in defense tech, AI in robotics, AI in biotech. It's a strategy that says "we don't know which AI bets will win, so we're making as many as possible." That approach wasn't possible five years ago when venture capital was more selective. Now, with AI fundamentals moving as fast as they are, the safe strategy for VCs is paradoxically to diversify broadly within AI rather than make concentrated bets on "the" AI winners. Series A rounds are no longer where the money stops. Seed and Series A rounds used to be the bread and butter of venture capital. July's data shows a pronounced shift: Series A rounds remain the most common (76 mentions), but they're now followed closely by Seed rounds (75 mentions). More significant, Series B and beyond deals have declined relative to historical norms. This pattern makes sense when you consider the venture landscape AI has created. A startup building a generative AI application can now reach meaningful revenue and user scale in 12-18 months instead of the traditional 3-4 years. That acceleration compresses the typical venture timeline. Companies are either raising Seed or Series A - or they're raising mega-rounds at the growth stage (Series C and beyond) because they've proven out a real business model. The traditional middle stage - where Series B used to matter most - is compressed. Investors see this as a feature, not a bug. It means AI startups with solid founders and product-market fit can reach profitability or significant scale faster than their predecessors. Defense, healthcare, and physics are the new frontier. When you look at individual deal announcements, a few themes emerge consistently. Defense tech startups are raising at unprecedented scale. Resist.UA closed a €50 million European defense tech fund. BRINC raised $125 million from Motorola Solutions for autonomous systems. Humanoid raised $152 million for industrial robotics powered by AI. Healthcare applications are booming. TerraFirma (SpaceX's climate-focused spinoff) raised $115 million. Chai Discovery, an AI drug discovery company, closed $400 million from a blue-chip investor syndicate including Kleiner Perkins, Sequoia, and OpenAI. And across these categories, a single word keeps appearing in deal announcements: "physics." Dimension Capital's $800 million fund explicitly targets "the intersection of science and compute." That's shorthand for AI companies using machine learning to solve real-world physics problems - molecular dynamics, protein folding, materials science, energy systems. This is venture capital's way of saying: AI isn't just software anymore. It's infrastructure. It's industrial equipment. It's national security. The implication for founders and competitors. For AI startups, this environment is historically generous. Capital is abundant. Competition for the best deals is intense, which means terms are favorable for founders. Round sizes are larger than they used to be, which means more runway before the next fundraise. For founders building non-AI products, the message is less comfortable. Venture capital's total dollars are finite. The disproportionate allocation toward AI means less capital is available for other sectors - infrastructure, consumer software, enterprise tools that don't involve machine learning. Some of this is rational; some of it is herding. The broader implication: if your startup's competitive advantage doesn't involve AI in some material way, you're competing for a shrinking pool of VC capital. That's not necessarily fatal, but it means lower valuations, smaller round sizes, or a need to prove profitability faster than AI-first startups. What's next. The venture capital industry is not known for subtlety. When a trend emerges, investors tend to overshoot - more capital flows in than the market can productively deploy, which eventually leads to corrections. InforCapital, partnership is almost certainly in an overshoot phase with AI right now. But overshoot is not the same as misjudgment. AI is genuinely reshaping every major software category, and now hardware categories too. Some percentage of these 300 AI startups will become category leaders. Many won't. That's venture capital's expectation, not a surprise. What should be watched: whether AI deals maintain this pace in August and beyond, or whether summer was a peak. Whether the mega-funds that closed this month actually deploy capital, or whether InforCapital, partnership see slower follow-on investing. And whether the companies raising at these historically large checks can actually justify the valuations that capital implies. For now, the data is clear. AI is the only narrative in venture capital that matters. Every other story is secondary.

AI-Visibility tracking matures: Profound hits $1B valuation. Profound raised a $96M Series C at a $1 billion valuation, a signal that answer-engine analytics has become its own software category. Here is what brands are actually measuring across ChatGPT, Gemini, Perplexity, and Claude. NYFTY Labs · GEO · 2026-06-27 AEO GEO AI visibility answer engines A $1 billion bet on a roughly 18-month-old company. According to a February 2026 Fortune report, Profound announced a $96 million Series C led by Lightspeed Venture Partners that values the company at $1 billion. Sequoia Capital, Kleiner Perkins, and other firms also participated, bringing Profound's total funding to more than $155 million. The company was founded in 2024 and is based in New York, which makes the climb to unicorn status unusually fast for enterprise software. Lightspeed framed the thesis as a migration of consumer attention from search engines to answer engines. Real enterprise traction, not just hype. Profound reports more than 700 enterprise customers, including roughly 10% of the Fortune 500. Named clients cited in coverage include Target, Walmart, Ramp, MongoDB, U.S. Bank, and Figma. The product tracks how brands are mentioned across major answer engines, covering brand mentions, sentiment, and share of voice in AI-generated responses. Specific performance claims, such as large jumps in AI referral traffic, come from the vendor and individual clients, so treat them as illustrative rather than independently audited benchmarks. When measuring whether your brand appears in ChatGPT and Gemini answers becomes a standard marketing line item, a billion-dollar valuation isn't hype, it's the new search analytics taking shape. Profound is the most visible name, but it sits inside a fast-growing field of AI-visibility tools. Semrush has folded AI visibility into its platform, scanning ChatGPT, Gemini, Google AI Overviews, and Perplexity, and surfacing both linked citations and unlinked brand mentions. Mid-market and budget-focused competitors such as Peec AI and Otterly.ai round out the category at lower price points. The common job across these tools is the same: submit large volumes of prompts, then measure whether and how a brand shows up in the answers. What brands are actually measuring. The core metric is citation and mention frequency across ChatGPT, Gemini, Perplexity, and Claude, tracked prompt by prompt rather than by keyword rank. Teams also watch sentiment and the surrounding context of a mention, since how a brand is described matters as much as whether it appears. Each assistant pulls from its own sources and applies its own citation logic, so a brand's visibility can look completely different from one platform to the next, which is exactly why teams track them side by side rather than relying on a single engine. Many of these tools historically diagnosed visibility gaps but stopped short of fixing them, leaving content, schema, and authority work to the brand's own team. Profound has since moved beyond pure diagnostics: alongside its Series C it launched Profound Agents, autonomous workers that automate execution and content generation, though brands still own strategy and review. Why this matters for marketing teams now. ChatGPT reached roughly 800-900 million weekly users and Gemini's app passed 750 million monthly users by early 2026. As more buying research happens inside answer engines, being absent from AI responses carries a real cost that classic SEO dashboards do not capture. Funding at this scale signals that measuring AI visibility is becoming a standard line item, not an experiment. The practical takeaway is to start measuring presence across the major assistants before committing budget to changing it. Key takeaways. * Profound raised a $96M Series C led by Lightspeed at a $1 billion valuation, with more than $155M in total funding for a company founded in 2024. * It reports 700+ enterprise customers and about 10% of the Fortune 500, reportedly including Target, Walmart, Ramp, MongoDB, U.S. Bank, and Figma. * Answer-engine analytics is now a defined category, with Semrush's AI Toolkit, Peec AI, and Otterly.ai competing alongside Profound. * The shared metric is brand citation and mention frequency across ChatGPT, Gemini, Perplexity, and Claude, measured per prompt rather than by keyword rank. Related services. Questions, answered. Want this applied to your site?