Latest news with #AIstartups
TechCrunch
06-06-2025
- Business
- TechCrunch
Why investing in growth-stage AI startups is getting riskier and more complicated
Making a bet on AI startups has never been so exciting — or more risky. Incumbents like OpenAI, Microsoft, and Google are scaling their capabilities fast to swallow many of the offerings of smaller companies. At the same time, new startups are reaching the growth stage much faster than they historically have. But defining 'growth stage' in AI startups is not so cut-and-dried today. Jill Chase, partner at CapitalG, said on stage at TechCrunch AI Sessions that she's seeing more companies that are only a year old, yet have already reached tens of millions in annual recurring revenue and more than $1 billion in valuation. While those companies might be defined as mature due to their valuation and revenue generation, they often lack much of the necessary safety, hiring, and executive infrastructure. 'On one hand, that's really exciting. It represents this brand new trend of extremely fast growth, which is awesome,' Chase said. 'On the other hand, it's a little bit scary because I'm gonna pay at an $X billion valuation for this company that didn't exist 12 months ago, and things are changing so quickly.' 'Who knows who is in a garage somewhere, maybe in this audience somewhere, starting a company that in 12 months will be a lot better than this one I'm investing in that's at $50 million ARR today?,' Chase continued. 'So it's made growth investing a little confusing.' To cut through the noise, Chase said it's important for investors to feel good about the category and the 'ability of the founder to very quickly adapt and see around corners.' She noted that AI coding startup Cursor is a great example of a company that 'jumped on the exact right use case of AI code generation that was available and possible given the technology at the time.' Techcrunch event Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | REGISTER NOW However, Cursor will need to work to maintain its edge. 'There will be, by the end of this year, AI software engineers,' Chase said. 'In that scenario, what Cursor has today is going to be a little less relevant. It is incumbent on the Cursor team to see that future and to think, okay, how do I start building my product so that when those models come out and are much more powerful, the product surface represents those and I can very quickly plug those in and switch into that state of code generation?'
ABC News
05-06-2025
- Business
- ABC News
Artists in limbo as music labels negotiate content use by generative AI
Skip to main content Arts Law Centre chief executive Louise Buckingham says there could be major implications, especially for smaller artists, over how negotiations between AI startups and major music companies play out. #ABCBusiness
Yahoo
26-05-2025
- Business
- Yahoo
I mentor startups outside my Meta product job. I tell founders to take 3 steps to sell their AI vision.
Mahesh Chayel, a product management lead at Meta, mentors AI startups outside his job. He outlined the three steps founders need to take to sell their AI vision. "The biggest gap I've seen is essentially, why use Gen AI?" he said. This as-told-to essay is based on a conversation with Mahesh Chayel, a product management lead at Meta. This interview has been edited for length and clarity. Since 2018, I have mentored 12 startups, especially those operating in the enterprise and AI spaces. The biggest gap I've seen is essentially, why use Generative AI? I work closely with early-stage founders to shape product strategy, refine their go-to-market approach, and explore how AI can be meaningfully integrated into their solutions. I bring a unique blend of deep product experience at scale, having worked on Meta Ads, along with a strategic understanding of enterprise pain points from my time in Silicon Valley. I also help teams clarify what real customer problems are worth solving, how to validate their solution early, and how to position their offering to resonate with decision-makers, especially in business-to-business environments. This is what I tell founders who are building in AI. I work with a couple of founders who are like, "Let's use AI and then let's build a product." If this problem could have been solved in other ways, what would it look like? And if they are using Gen AI, is AI the best use of technology in that case? In some cases, it can be. As a customer facing a problem, you really don't care how to solve it. It's more about, are you solving these problems for the customer in a better way? Founders usually start thinking: If somebody else is going to use the same idea, use AI or Gen AI to solve the problem faster. Founders iterate a lot and keep trying to solve the problem through different mechanisms. Can you be super laser-focused on the customer? Can you really identify how this technology can specifically solve the problem? If it doesn't, find other ways to solve it. People sometimes measure product-market fit in an incorrect way. For some, even before building the product, their product market fit is not very clear. It's not about 100 or 10,000 people looking at this product and just being aware of it. Many times, the first-time sale is not a good mechanism because all of those metrics can be gamified. You can spend a lot of money on ads and make the product available to a lot of people. You can provide discounts and sell the product first time to a set of customers. The most important thing for product market fit is: Do your set of customers really love and trust the product to keep using it and coming back? That's the real crux of it. Product market fit comes down to retention metrics or the repeat purchase of a product. A startup I gave advice to was essentially building an AI tool for young adults — AI companions. The founder was not generating money from this. There are a couple of parts. Young adults are not the real users who are going to pay for the product. You would need to identify who can really pay. It can be the parents, it can be the schools where these kids are studying. It was a breakthrough to help this founder really understand the business model that can work in such a space. We uncovered that the market wasn't quite there yet, at least not in the way the founder had imagined. While disappointing at first, it helped them redirect their focus toward a more validated pain point, saving months of effort and repositioning the company for a better product-market fit. Take a step back: Who is the product used by? Who is the product paid by? How can it scale? As a founder, sometimes you get so attached to the problem that you don't see the larger space. This restart has helped the startup build a more sustained business now. Whether it's unlocking growth or steering away from misaligned markets, I measure success by how much clarity and conviction founders gain in their next move. Read the original article on Business Insider Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Forbes
16-05-2025
- Business
- Forbes
AI Startups That Focus Small Are Winning Big
Forget big teams and bigger models. The AI startups growing fastest seem to be solving one clear ... More problem — and doing it really well. The AI boom has largely been defined by size — large models, huge funding rounds, and teams numbering in the hundreds. But a new trend is emerging — one where lean, focused AI startups are thriving by mastering specific use cases. Take AiHello for instance. Founded by Saif Elhager and Ganesh Krishnan, the 40-person startup has built a profitable AI platform focused solely on Amazon advertising. With no outside funding, they've grown to seven-figure annual revenues and continue to double each year. Their approach: build for a well-defined problem and automate everything possible. 'We just built a business around the problems we were most familiar with and sold it to people we knew would need it,' said Elhager in an interview. 'Instead of trying to look for something that sounded impressive.' This strategy stands in contrast to the scale-first model dominating much of the AI industry today. Rather than building large, generalized tools and searching for product-market fit, Elhager told me that AiHello focused from day one on a single platform, a single use case and a set of customers they understood deeply. And that, according to him, has made all the difference for their company. According to McKinsey's 2024 State of AI report, 65% of businesses now use generative AI in at least one function — double the rate from 2023. Despite such a commendable figure, the most consistent revenue gains are showing up not in flashy creative tools, but in targeted applications like inventory management, operations and marketing optimization — domains where specialized AI solutions thrive. This shift from broad AI ambition to narrow execution that's hyper-focused on a specific domain mirrors what AiHello is doing in ecommerce. The company's laser focus on Amazon's ad ecosystem allows it to improve its models continuously and respond directly to customer needs. Saif Elhager- Cofounder, AiHello 'When you have a more focused number of use cases, you can also spend a lot more time making sure the AI performs well,' Elhager explained. This level of precision isn't possible in generalized platforms trying to cover dozens of workflows at once. And more industry leaders are now echoing the sentiment that the path to lasting impact isn't scale but specificity. As Sarah Guo noted in a previous edition of the No Priors podcast, which covered AI investment hype, foundation models, regulation and more, 'there is real opportunity for vertical specific models where you can imagine that control for either compliance or safety, or just performance makes sense.' While many AI startups spend aggressively on sales, compute and hiring, AiHello went in the opposite direction. The team relies heavily on internal automation, offshores most of its talent and keeps its operating costs low. 'Our payroll is 80% lower than usual,' noted Elhager. 'We spend very little on sales or marketing, and that's kept us profitable from day one.' Capital efficiency has become a growing concern in AI, especially as funding conditions tighten. Industry veteran Andrew Ng has also noted this trend, arguing that AI's real value lies in embedding it into specific workflows — not just building general-purpose tools. 'AI won't replace human workers,' Ng said in a March 2024 talk, 'but people that use it will replace people that don't.' That distinction favors platforms like AiHello, where AI works quietly in the background — cutting costs, saving time and letting the business run smarter. Rather than trying to compete with Amazon or build a new ecommerce stack from scratch, AiHello built its tools directly within the existing system. 'Building on an existing platform and going to market with an obvious ICP is much quicker and less capital-intensive,' said Elhager. 'If your goal is to build a 7–8 figure business, then this is one of the higher probability ways of doing that.' It's a reminder that not every breakthrough requires reinvention. Sometimes, the smartest move is to enhance what already works. AiHello isn't the only one taking this path. Other startups like Rebuy — which helps Shopify merchants personalize shopping experiences using AI — Typeface which generates on-brand content for marketing teams — and Adept — which builds AI agents that can take actions across enterprise software tools — are succeeding by solving specific problems inside defined ecosystems. 'Having limited headcount means we have to focus on only 1–2 things that matter,' said Elhager. 'That's paradoxically a faster way to make progress.' In a market already flooded with general-purpose AI pitches and bloated burn rates, the future may belong to companies that stay small, move fast and go deep rather than wide.
Forbes
16-05-2025
- Business
- Forbes
AI's Pace Of Change: Six Indicators You Are Too Slow
AI startups are setting a faster pace of change than ever before. You know you are in trouble, said the legendary CEO Jack Welch, when the pace of change outside the company is faster than that inside. If that's true, then the rate of growth of AI startups should be striking terror into corporate board rooms around the world. I have been skeptical of the pace at which AI will convert its potential into an economic revolution. However, I do not think that is a reason for complacency. Now is the time to ask if we are moving fast enough to ride the wave when it comes or if we will be washed aside. AI startups are converting ideas into revenue at 10X the speed of previous generations with a fraction of the cost and far smaller teams. The old logic was that it takes a software startup anything from 3 to 5 years to get to $50M of revenue and another 5 to 7, to go to $1B. The AI generation is making this look sluggish. Self-coding AI Lovable has posted $40M of annual recurring revenue in 5 months of trading on the back of just $7.5M of venture funding. Bolt's numbers are roughly the same, $30M in 4 months, with just $7.9M. Both of which look like slackers compared with image generation company Midjourney, which scaled to $200M ARR without any funding and an initial team of less than 10. [MK1] Given the billions of dollars corporations spend to keep up in the AI race, one would think they are keeping up. However, all the evidence is that most are struggling to convert playing with AI into tangible outcomes. Managers get ahead in large corporations by projecting confidence and certainty. You reassure the board and senior managers by demonstrating you have a plan, that there is 'alignment' between stakeholders, and that you will deliver 'unique' advantages. The problem with this traditional approach is that nobody is certain how AI will play out. Corporations have struggled to find solid use cases to convert the hype into revenue. Consumers have started to roll their eyes at promises of embedded AI in everything from mobile phones to personal computers. It is time to admit we don't know. We need to make a virtue of living with the uncertainty. That means lots of disciplined, small-scale efforts to learn what works, before converting it into the next big thing. Guessing how AI will deliver benefit and spending big on a master plan is a dangerous game. My colleague Michael Kaplun has been working on this problem. How do we know we are going fast enough? I converted his more thoughtful work into six ugly errors that we see companies committing. If any of these apply, it's time to get the skates on and figure out how to get to the head of the puck. This is just six big issues we are seeing out there as companies wrestle to turn AI's potential into commercial reality. Hype cycles have a predictable path, and we are headed for the moment at which we all draw breath, realizing that the change isn't as fundamental as we thought. Or at least we were before we saw what Lovable has achieved. The message is clear. We need to move faster.
