EUVC Newsletter | 21.07.23
Discover more from EUVC | The European VC
EUVC Newsletter | 21.07.23
An AI special edition covering The impact of AI on VC, Why all that glitters isn't gold, how AI may be the secret ingredient in the future of VC, and a case of AI written job applications
Table of Contents
The Impact of AI on VC 🤖
All That Glitters is Not Gold: Unveiling the Real Value of LLMs and AI in VC
AI is the secret ingredient in the future of Venture Capital
AI-written job applications
AI & VC: A New Investment Era
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The Impact of AI on VC 🤖
Tue, Sep 26, 2023, 12:00 PM - 1:00 PM CET | Claim your seat here.
We’re getting ready for our webinar on the surge of AI and it's impact on VC 🤖 with our All-Star Panel:
Fred Destin, Founding GP of Stride.VC
Claude Ritter, Founding GP of Cavalry Ventures
Doreen Huber, Partner of EQT Ventures
Dr. Andre Retterath, Partner of Earlybird Venture Capital
We’d love for you to submit questions and topics for the panel to cover in the comment section below 📬👇
In the first segment, we'll discuss how verticals are being disrupted and what new business models will arise in their wake.
In the second segment we'll turn our attention to how AI impacts what goes on inside the leading VCs asking questions such as how AI impact the panel's investment theses and strategies, how they deal with the disruption it causes to their established portfolio and finally how they adapt as firms and investors.
And don't forget to head on over to Affinity.co, the leading relationship management software for deal makers and say thanks for supporting us in making this and many other webinars and events for Europe's venture community possible 💞
All That Glitters is Not Gold: Unveiling the Real Value of LLMs and AI in VC
Digging Beneath the Surface to Strike Gold in LLMs and AI with Acrobator.vc
Now, we've seen LLMs left and right, promising the Midas touch of AI. But let's face it, not every language model startup is destined for golden glory. It's time to separate the true gold from the fool's gold! 💰✨
To guide us through this perilous journey, we've enlisted the wisdom of our dear friend Ramon Vigdor, the General Partner at Acrobator Ventures. With his help, we'll navigate the treacherous terrains of natural language processing, contextual understanding, and mind-bending algorithms. 🚀🔍
Without losing a beat, I’ll jump right in: LLMs, also known as Large Language Models, or in more casual terms, chatGPT and its pals, have swooped onto the scene last year and forever transformed it. 💥 Brace yourself for a wild ride, because generative AI has revolutionized our concept of intelligence and the way we tackle tasks. Love it or hate it, know it or not, it's an unstoppable force, here to stay and rock our world! 🚀
By now, you're undoubtedly well-acquainted with chatGPT and might have even had a blast tinkering with it. You've witnessed firsthand the awe-inspiring power of its language comprehension and content creation prowess. It's like having a genius wordsmith at your fingertips, serving up ready-to-consume brilliance. It's mind-boggling to think that just a couple of years ago, this level of technology was unimaginable. But hey, the OpenAI family of chatbots and other transformer-based models are out there, competing and growing, all sharing the common trait of requiring an astronomical number of parameters to achieve their impressive language understanding. That's why we dub them 'large language models.' 🤖
But hold on, my friend, because there's a twist in this story! New and compact models are on the horizon, bringing a delightful blend of fewer parameters, less complexity, and improved performance. It's like the evolution of language models is taking a leap forward! Now, without further ado, feast your eyes on this evolutionary tree view of recent LLMs and their awesome counterparts🌳.
In the words of Matt Turck, the progress of large language models has primarily been evaluated based on the parameter count in recent years. However, Sam Altman, the CEO of OpenAI, challenges the notion that this measure alone is still relevant. According to Altman, the number of parameters no longer serves as a reliable indicator of a model's performance.
I think we're reaching a point where these colossal models are no longer the sole focus, and we'll explore alternative avenues for improvement.
Sam Altman, CEO of OpenAI
Now, imagine a scenario where a groundbreaking technology emerges, unprecedented in its capabilities. Suddenly, a plethora of ideas springs forth, giving rise to new startups and flooding the untapped blue ocean! 🌊💡
What a tremendous opportunity for investing in generative AI! 💸✨
Or is it? 🤔❓
Because generative AI is perceived as a potential “once-every-15-years” type of platform shift in the technology industry, VCs aggressively started pouring money into the space, particularly into founders that came out of research labs like OpenAI, Deepmind, Google Brain, and Facebook AI Research, with several AGI-type companies raising $100M+ in their first rounds of financing.
Generative AI is showing some signs of being a mini-bubble already. As there are comparatively few “assets” available on the market relative to investor interest, valuation is often no object when it comes to winning the deal. The market is showing signs of rapidly adjusting supply to demand, however, as countless generative AI startups are created all of a sudden.
[venturebeat.com / Mattturck.com]
As we can see above, funding Generative AI is skyrocketing. The current trend reflects an unbridled eagerness to jump on anything GPT-powered, driven by the hope that it will lead to astronomical returns. 🚀💸
However, let's not overlook the fact that the tech is far from stable. LLMs are on their way out, and new disruptive models are poised to emerge from various players. Numerous challenges still loom over existing models, ranging from security, privacy, and real-time data access to explainability, long-term memory, trainability, and bias, just to name a few. With high demand for AI's promises and unprecedented availability, even a new startup can dip its toes into the AI pool by leveraging chatGPT within a matter of days, with little real understanding of AI. 🌊🤖
Here we find ourselves in the midst of a new gold rush—a race to stake your dollar on the finest nugget of large language models. 🏆💰
The signs of a bubble are becoming evident. Let's take a look at some figures:
OpenAI received a staggering $10B investment from Microsoft in January 2023.
Runway ML, an AI-powered video editing platform, raised $50M in Series C funding at a $500M valuation in December 2022.
ImagenAI, an AI-powered photo editing and post-production automation startup, secured $30M in funding in December 2022.
Descript, an AI-powered media editing app, raised $50M in Series C funding in November 2022.
Mem, an AI-powered note-taking app, secured $23.5M in Series A funding in November 2022.
Jasper AI, an AI-powered copywriter, raised a whopping $125M at a $1.5B valuation in October 2022.
Stability AI, the generative AI company behind Stable Diffusion, secured $101M at a $1B valuation in October 2022.
You, an AI-powered search engine, raised $25M in Series A financing.
Hugging Face, a repository of open-source machine learning models, raised $100M in Series C funding at a $1B valuation in May 2022.
Inflection AI, an AGI startup, raised an impressive $225M in its first round of equity financing in May 2022.
Anthropic, an AI research firm, secured a massive $580M in Series B funding (including investments from SBF and Caroline Ellison!) in April 2022.
Cohere, an NLP platform, raised $125M in Series B funding in February 2022. 📈💥
Having a chat with my favorite digital buddy, Writesonic, powered by GPT-4 and capable of searching live data using Google, I asked her (for lack of a better word—language is still catching up on this one) to describe the generative AI investment bubble. Her response was:
Venture capitalists are worried that generative AI could become the next technology bubble [1][3]. The rapid growth and hype surrounding the sector have led to concerns about overvalued startups and unsustainable valuations [1]. Despite these concerns, positive investor sentiment towards the technology exists, with the increasing amount of data being generated and collected being a key driver of the AI boom [2]. The popularity of consumer tools like ChatGPT, DALL-E, and Lensa AI has also fueled the expansion of the sector [1]. While generative AI has limitations in contextual understanding and originality, it can be a valuable tool for language-based tasks and interactions when its capabilities and limitations are understood and appropriately applied [2]. Businesses are using generative AI to improve customer engagement and decrease support costs, but to truly transform industries, AI startups will need to scale their solutions, develop sustainable business models, and focus on developing solutions that can be easily integrated into existing workflows and processes [2].
References:
[1] Whoops! Is generative AI already becoming a bubble?
[2] Is Generative AI the Next Tech Bubble?
[3] Generative AI is a bubble – we know what happens next
Mmm... I wholeheartedly concur.
Now, the million-dollar question: What can we do about it?
Here at Acrobator, we fully recognize the disruptive potential of AI. However, we also acknowledge the allure that every startup possesses when powered by LLMs.
But, as I learned back in school (not in my AI faculty, mind you), all that glitters is not gold. 🌟💰 This brings me to the avenues Acrobator is actively pursuing, and I've decided to spill the beans and share them with you today:
Counter the prevailing high availability and ease of building AI-powered apps by investing in must-have use cases within domains that require deep domain knowledge. It's the key to defensibility and standing out from the crowd.
Similar to the above point, combat the abundance by offering solutions that rely on data accessible to only a select few. This creates a moat of knowledge that fortifies your training models.
Empower your own processes with AI. At Acrobator, we're diving headfirst into leveraging AI for qualifying and sourcing (pre)seed teams and startups. It's no small feat when there's limited data available, if any at all. It's a challenge we're determined to tackle!
Last, but certainly not least (I couldn't resist a wordplay here), we firmly believe in enabling infrastructure plays that support the explosion of AI use cases. While many investors are fixated on finding the best gold seekers, we find ourselves most exhilarated by the companies developing metaphorical shovels and tools. After all, they are the ones who enable the gold rush!
In an era where AI is widely accessible and implementation has become a breeze, it may seem like in-depth knowledge of AI is no longer necessary. And perhaps that holds true for straightforward cases. But from a VC's perspective, we firmly believe that it's critical to deeply understand AI—its technology, current research directions, trends, and challenges. This understanding allows us to separate the wheat from the chaff and make informed investment decisions. 🧠💼
AI is the secret ingredient in the future of Venture Capital
by Marc Penkala, GP at āltitude
#Gartner predicts that by 2025, 75% of venture investment decisions will involve AI. The future of VC is being reshaped, and this is your chance for many VCs to stay ahead of the curve!
𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗔𝗜 𝗺𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗩𝗖𝘀:
1. 𝗗𝗲𝗮𝗹 𝗦𝗼𝘂𝗿𝗰𝗶𝗻𝗴 & 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: AI-powered algorithms can quickly scan the startup landscape, separating the grains of sand from the buried treasure. It can also provide better evaluation and forecast startup success.
2. 𝗗𝘂𝗲 𝗗𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 & 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: AI can streamline and enhance this process by analyzing financial data, legal documents, and scanning the internet for potential issues.
3. 𝗘𝗮𝗿𝗹𝘆 𝗚𝗿𝗼𝘄𝘁𝗵 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: Machine learning can analyze public data to identify early signs of rapid company growth, giving VCs a competitive advantage.
4. 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗧𝗶𝗺𝗶𝗻𝗴: AI can determine the optimal timing for investments based on growth indicators, helping VCs make informed decisions.
5. 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 & 𝗘𝘅𝗶𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: AI acts as a meticulous supervisor, tracking portfolio performance, identifying challenges, and guiding VCs in making the best exit decisions.
𝗧𝗵𝗲 𝗗𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘀𝗮𝘁𝗶𝗼𝗻 𝗘𝗳𝗳𝗲𝗰𝘁:
AI will bring significant benefits to (emerging) GPs and their investment teams. GPs can leverage AI to quickly evaluate deal quality and assess risks, leading to better forecasting of startup success and improving overall investment strategies.
Principals and Associates can benefit from AI streamlining their deal sourcing and due diligence processes, enabling them to focus more on building strategic relationships with founders instead of spending time on manual research and analysis.
Furthermore, AI provides a democratisation effect for emerging GPs, as they often face challenges in deal sourcing due to limited networks and lesser-known brands.
𝗩𝗖𝘀 𝗦𝗽𝗲𝗮𝗿𝗵𝗲𝗮𝗱𝗶𝗻𝗴 𝗔𝗜 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻:
Leading the charge in AI-powered VC are firms like Correlation Ventures, EQT Group, Sapphire Ventures, Hone Capital, Moonfire Ventures, and Level Ventures. They've harnessed the power of AI to gain a competitive edge and drive better investment outcomes.
The AI revolution in VC will unlock unprecedented opportunities for the ones staying ahead of the curve 🚀
In case you missed it… Dan Bowyer on AI-written job applications
I tested Superhuman's email writing Ai feature yesterday.
Impressive.
Generic, but impressive stuff out of the gate.
Then this morning I noticed an application for one of our open roles here at SuperSeed.
𝗙𝗮𝗯𝘂𝗹𝗼𝘂𝘀.
At first glance a smart, thoughtful, relevant chap has applied to join us.
But oh, hmmm, 𝘁𝗵𝗶𝘀 𝗱𝗼𝗲𝘀𝗻'𝘁 𝘀𝗺𝗲𝗹𝗹 𝗿𝗶𝗴𝗵𝘁...
He'd obviously used ChatGPT or a n other Ai engine to create the email and application letter.
(What about when it creates your CV, automatically applies, deals with responses. Should my Ai respond to his?! Where is the line.)
You can easily sniff it out.
(The image below isn't his. I used Superhuman to create an example)
𝘐𝘴 𝘵𝘩𝘪𝘴 𝘭𝘢𝘻𝘺 𝘰𝘳 𝘴𝘮𝘢𝘳𝘵?
𝘐𝘴 𝘪𝘵 𝘪𝘯𝘢𝘶𝘵𝘩𝘦𝘯𝘵𝘪𝘤?
𝘜𝘭𝘵𝘪𝘮𝘢𝘵𝘦𝘭𝘺 - 𝘐𝘴 𝘪𝘵 𝘢 𝘥𝘦𝘢𝘭 𝘣𝘳𝘦𝘢𝘬𝘦𝘳?
𝗦𝗵𝗼𝘂𝗹𝗱 𝗜 𝘁𝗮𝗸𝗲 𝘁𝗵𝗲 𝗴𝘂𝘆 𝘁𝗼 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝘀𝘁𝗮𝗴𝗲?
𝘞𝘰𝘶𝘭𝘥 𝘺𝘰𝘶?
Check out Superhuman’s AI-written application below 👇
Join Affinity’s webinar on AI & VC: A New Investment Era
JULY 25TH, 2023 at 7 PM CET
Artificial intelligence is rapidly transforming Venture Capital. VCs are using AI to automate tasks, improve decision-making, and identify new investment opportunities.
In this webinar, the panel will discuss how VCs are using AI in:
Identifying and researching potential investment opportunities
Assessing the financial and operational health of potential investments
Tracking the performance of investments and making exit decisions
Before we go.. don’t forget that if you wanna invest internationally, there’s nothing like an LP investment to get you access to the best parties 💃
