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Tag: labs

  • Key learnings on what is needed to successfully manage growth

    Key learnings on what is needed to successfully manage growth

    Business growth can happen in many ways – are you ready for the momentum that comes with it?

    When momentum takes hold, be it from a viral moment, an act of mother nature or planned, business leaders can suddenly find their model or proposition is tested beyond belief as it learns to respond to enhanced demand. What once was a dream for them can start to feel like a nightmare if they do not possess the right tools or make sure sound  infrastructure is in place. 
    How can business leaders ensure growth does not deter them from their ambition as they navigate their way through one of the most challenging yet exciting times for a growing business?
    Here Dr. Tom Mason, Founder and CEO of Bramble Energy – a hydrogen fuel-cell company – shares his experience and learnings since developing the unique piece of technology and launching the business in research labs at Imperial College London and University College of London in 2016.
    In six years, the business has proven to be a real game-changer in solving key issues in the production of hydrogen fuel-cells. Earlier this year, the firm closed a £35m investment round which will be used to help the business grow its UK-based team to over 100 (currently at 60) and take it one step further to achieving its goal of becoming the largest fuel-cell supplier in the world. 

    Never forget the mission

    With growth can come complacency and although you are likely to adjust your thinking along the way, it is imperative that you never forget why you started out on this journey. Going back to your mission can be a useful and important tool when strategising your next steps. Wherever you might be experiencing growth, whether it is your team, your revenue streams, or the markets you plan to insert yourself into – the question should always be ‘Does this feed into our mission? Will this growth ultimately help us reach our goal?’
    Growing a business is no easy task but for me, it is about setting the best possible example and reaffirming Bramble Energy’s goal daily. Of course, we want to be successful and the go to name in clean technology but as we move forwards I want to see success for every individual member of our team who make Bramble the company it is today (and tomorrow).

    Always be ready to be agile 

    When experiencing a period of growth, it is an important exercise to plan and understand what your outlook may be in the near and distant future, with your strategy being imperative to securing your future growth plans. You should be ambitious and use the deep dive knowledge of your marketplace and your competitors to your advantage. Are there companies in your field who have experienced a similar period and how have they been successful with it or what errors did they make that perhaps we could avoid? 
    Dependent on how your business model works, your strategy is a helpful resource to help you navigate the unknown and to respond to the varying opinions of different stakeholders on how best to move forward. Never forget no one understands your business and how it has got to where it is, better than you. With all the will in the world, you cannot plan for everything, and it is in these moments that you must be agile and ready to adjust. Opportunities or risks will present themselves when you least expect it, and it will ultimately be up to you to solve and learn from these moments.

    People are your greatest asset 

    If part of the growth you are experiencing is your team, then it will be important to not lose sight of what the foundations are of your business. There are areas where your knowledge may be limited and the people you choose to be a part of your journey are your best asset where you can always learn something new or look at your business from a different lens. The infrastructure that you start to build from within will be integral in protecting your mission, your business and your team.
    Mistakes will be made – this is inevitable, but you can either learn from them and persevere or give up. If you believe in what you are doing then you only have one option. The people who you choose to take on the journey with you are so important especially in those moments when things do not go the way you expected or wanted. A support network is key in hitting the targets you make for yourself as they can provide a different point of view when you are too close to the issue to resolve it.  

    Embrace the rollercoaster of growth 

    Don’t be afraid of the pace in which things can develop. Use this to your advantage. There will always be tough times during business growth; it’s how you weather the storm and learn at each turn that will make the difference, and ultimately lead to success. 

  • ‘Selling coffee beans to Starbucks’ – how the AI boom could leave AI’s biggest companies behind

    ‘Selling coffee beans to Starbucks’ – how the AI boom could leave AI’s biggest companies behind

    How much do foundation models matter?

    It might seem like a silly question, but it’s come up a lot in my conversations with AI startups, which are increasingly comfortable with businesses that used to be dismissed as “GPT wrappers,” or companies that build interfaces on top of existing AI models like ChatGPT. These days, startup teams are focused on customizing AI models for specific tasks and interface work, and see the foundation model as a commodity that can be swapped in and out as necessary. That approach was on display especially at last week’s Boxworks conference, which seemed devoted entirely to the user-facing software built on top of AI models.

    Part of what is driving this is that the scaling benefits of pre-training — that initial process of teaching AI models using massive datasets, which is the sole domain of foundation models — has slowed down. That doesn’t mean AI has stopped making progress, but the early benefits of hyperscaled foundational models have hit diminishing returns, and attention has turned to post-training and reinforcement learning as sources of future progress. If you want to make a better AI coding tool, you’re better off working on fine-tuning and interface design rather than spending another few billion dollars worth in server time on pre-training. As the success of Anthropic’s Claude Code shows, foundation model companies are quite good at these other fields too — but it’s not as durable an advantage as it used to be.

    In short, the competitive landscape of AI is changing in ways that undermine the advantages of the biggest AI labs. Instead of a race for an all-powerful AGI that could match or exceed human abilities across all cognitive tasks, the immediate future looks like a flurry of discrete businesses: software development, enterprise data management, image generation and so on. Aside from a first-mover advantage, it’s not clear that building a foundation model gives you any advantage in those businesses. Worse, the abundance of open-source alternatives means that foundation models may not have any price leverage if they lose the competition at the application layer. This would turn companies like OpenAI and Anthropic into back-end suppliers in a low-margin commodity business – as one founder put it to me, “like selling coffee beans to Starbucks.” 

    It’s hard to overstate what a dramatic shift this would be for the business of AI. Throughout the contemporary boom, the success of AI has been inextricable from the success of the companies building foundation models — specifically, OpenAI, Anthropic, and Google. Being bullish on AI meant believing that AI’s transformative impact would make these into generationally important companies. We could argue about which company would come out on top, but it was clear that some foundation model company was going to end up with the keys to the kingdom.

    At the time, there were lots of reasons to think this was true. For years, foundation model development was the only AI business there was — and the fast pace of progress made their lead seem insurmountable. And Silicon Valley has always had a deep-rooted love of platform advantage. The assumption was that, however AI models ended up making money, the lion’s share of the benefit would flow back to the foundation model companies, who had done the work that was hardest to replicate.

    The past year has made that story more complicated. There are lots of successful third-party AI services, but they tend to use foundation models interchangeably. For startups, it no longer matters whether their product sits on top of GPT-5, Claude or Gemini, and they expect to be able to switch models in mid-release without end users noticing the difference. Foundation models continue to make real progress, but it no longer seems plausible for any one company to maintain a large enough advantage to dominate the industry.

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    Join 10k+ tech and VC leaders for growth and connections at Disrupt 2025

    Netflix, Box, a16z, ElevenLabs, Wayve, Sequoia Capital, Elad Gil — just some of the 250+ heavy hitters leading 200+ sessions designed to deliver the insights that fuel startup growth and sharpen your edge. Don’t miss the 20th anniversary of TechCrunch, and a chance to learn from the top voices in tech. Grab your ticket before Sept 26 to save up to $668.

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    We already have plenty of indication that there is not much of a first-mover advantage. As venture capitalist Martin Casado of a16z pointed out on a recent podcast, OpenAI was the first lab to put out a coding model, as well as generative models for image and video — only to lose all three categories to competitors. “As far as we can tell, there is no inherent moat in the technology stack for AI,” Casado concluded.

    Of course, we shouldn’t count foundation model companies out just yet. There are still lots of durable advantages on their side, including brand recognition, infrastructure, and unthinkably vast cash reserves. OpenAI’s consumer business may prove harder to replicate than its coding business, and other advantages may emerge as the sector matures. Given the fast pace of AI development, the current interest in post-training could easily reverse course in the next six months. Most uncertain of all, the race toward general intelligence could pay off with new breakthroughs in pharmaceuticals or materials science, radically shifting our ideas about what makes AI models valuable.

    But in the meantime, the strategy of building ever-bigger foundation models looks a lot less appealing than it did last year — and Meta’s billion-dollar spending spree is starting to look awfully risky.