Israel Generative AI predictions for 2024

In November 2022 I published a post titled “generative AI will go mainstream when it goes from playful to useful“, and I think you will agree that this transition is in full swing. Not so long ago, we were all posting AI profile pictures on social media, but very quickly we’re now seeing an emergence of generative AI tools and LLM applications to assist almost every role in the organisation, across industries and verticals.

2023 was the year generative AI went mainstream

The pace of advancement in generative AI has been astounding. In just over one year since ChatGPT was introduced in November 2022, it’s been the fastest consumer adoption tool to cross 100 million users, OpenAI reached $1.6 billion in annualised revenue, an a myriad of new companies were born, from open source LLMs to application layer startups that were able to move fast and do much more with less.

According to Crunchbase data, generative AI startups pulled over $50 billion in funding in 2023, led by megarounds into OpenAI, Anthropic, inflection AI and others.

According to GitHub, the largest open source repository (owned by Microsoft), developers are flocking to open source generative AI projects, and some entered the top 10 most popular open source projects by contributor count in 2023.

As an example, consider the advancement of generative AI in video in 2023:

Enterprise adoption is still in the early innings

While individuals and startups were quick to adopt generative AI tools, adoption has been much slower on the enterprise side. As Viola points out, there are 5 main barriers to adoption: Security, privacy, intellectual property, performance, and cost.

With that being said, corporates have generally leaned in to the generative AI trend and are currently working out their own strategy to implement generative AI and automation into their products safely.

As Thomas Tunguz points out in his 2024 predictions, AI is no longer a standalone category but rather a component of every product. This shift is particularly significant in the realm of Large Language Models (LLMs).

Much like mobile technology became a de facto part of every startup, AI is no longer a category but the core or a component of every product. It’s still early days with LLMs, and there’s a lot of work to do; however, LLMs have already wholly transformed data in many ways, and innovations with data will continue to command VC investment. Likewise, venture dollars will still funnel into startups in the space. LLMs have driven an increased demand for data, caused a complete architecture change inside companies and changed how data is manipulated. As the technology evolves, we’ll continue to see an increase in new data products and data teams.

Theory Ventures 2024 predictions

Israel remains one of the major AI hubs globally

In terms of funding, it’s safe to say that Israeli generative AI startups have yet to live to their full potential. Overall, Israeli startups raised $7.3 billion in 2023, the lowest amount since 2018 and a 60% decrease from 2022. The judicial reform proposed by the government gave international investors jitters and international venture investment further slowed down due to the war against Hamas in Q4 2023. It’s important to put this in context, as US investments also declined by 35% and UK investments dropped by about 40%.

According to IVC online, Israeli GenAI companies raised $5.57 billion in 390 deals between 2014 and 2023. I think the numbers are a bit fuzzy, since there are companies that incorporated generative AI into their products, such as MyHerritage, which aren’t GenAI startups per-se but are included in the count.

Nevertheless, In the past 3 years, Israeli generative AI startups attracted over $2.2 billion in funding, placing Israel 3rd globally in generative AI capital raising following the US and China, based on a recent report on Generative AI startups in Israel, created by our colleagues at Viola Ventures.

This is consistent with our own findings at Remagine Ventures, published in Calcalist on September 2023, which calculated that Israeli generative AI startups raised over $2.5 billion in the last 5 years, and the number of Israeli startups more than doubled in this space more in the past 8 months.

The Remagine Ventures Israeli generative AI startup landscape (published September 2023) – scan the QR code to add a company

2024 will be a sink or swim for generative AI startups

While generative AI continues to be red-hot, investors are deploying capital with caution. The fear of commoditisation, questions about copyright, impending regulation, platform dependencies, and costs remain valid concerns in 2024.

While it’s still anecdotal evidence, some of the early ‘GPT-wrappers’ application layer startups have flamed out relatively quickly, leaving investors with a bad taste. Others, may have raised funding at high valuations during peak hype, and might face difficulties raising up-rounds without significant traction. The latter is true for all startups, not just in generative AI.

In July 2023, I’ve outlined a number opportunities for Israeli GenAI startups. To a large extent, they are still valid, but the progress made by OpenAI and open source has shuffled the deck slightly and I would add the following 3:

  1. Tools for Open source LLM adoption – while OpenAI remains the ‘IBM’ of LLMs, the attractiveness of Open Source LLMs is on the rise. The downside of OpenAI is the cost, platform dependency and limitations of its API. Open source LLMs have been improving at a rapid pace, have an active developer community and can be an attractive alternative, if managed properly.
  2. Smaller, vertical models – we still measure LLMs by the number of parameters, but it’s unreasonable to expect one model to do it all. For example, a customer service oriented use case for a specific product can be done with a smaller model, trained specifically on that use case. It’s expected that smaller models will also suffer from less hallucinations. Startups enabling this while still providing security, privacy, copyright, etc might be a good candidate for enterprise adoption.
  3. Automation and workflow integration in niches – imagine if IFTTT was tailored for every manual, repetitive task in the company. For example, Jasper.com is a copyright tool serving marketers. GPT-4 can of course provide the copy, but the copy generation is only one of many steps in the job of a marketer. It needs to be formatted to ads, connect to hubspot, run A/B tests and choose the top performer, connect the text to relevant images, etc. OpenAI will compete in this space with its shiny new app store.

After a tough 2023, I expect 2024 to be a bumpy year for Israeli generative AI startups. But I’m generally optimistic. Companies can do more with less with generative AI, and Israel remains a major AI hub in terms of talent, capital and ideas.

For additional AI predictions and startup opportunities in 2024, I recommend the following resources:

  1. 10 predictions on the future of AI – Data Driven VC
  2. AI in 2024 by Sequoia
  3. Where VCs are betting in 2024
  4. The state of generative AI in the enterprise – Menlo VC
  5. What will happen in 2024 – Fred Wilson
  6. 10 AI predictions for 2024 – Radical Ventures
  7. 5 M&A observations for 2024 – Bessemer’s Jannelle Teng
  8. Optimistic Nihilism for 2024 and trends of consumer spending – Rex Woodbury
  9. Themes for 2024 – Wing Ventures
  10. Deloitte 2024 TMT predictions

18 years of VC Cafe

“The best time to plant a tree was 20 years ago. The second best time is now.”

– Chinese Proverb

Happy New Year! I can hardly believe it’s been 18 years since I first launched VC Cafe back in December 2005! Reflecting on this journey, the tech world was a completely different landscape back then. Facebook was just stepping out of college campuses, the revolutionary iPhone was still in the realm of Apple’s future plans, and burgeoning concepts like big data and cloud computing were just starting to emerge. I’ve written on the VC boom (when money was cheap and due diligence was minimal) to the major reckoning that followed and we’re still yet to see its full impact. It’s absolutely astounding to ponder the leaps and bounds we’ve made in this period.

During these nearly two decades, I’ve had the extraordinary privilege of being a front-row witness to Israel’s incredible startup scene. From licking its wounds following the dot com burst, Israeli entrepreneurs have been making indelible marks across a multitude of industries. I’ve seen the explosion of new unicorns created, from cybersecurity pioneers to AI disruptors and billion dollar revenue gaming startups. While it is going through a challenging period now, Israel continues to stand as a powerhouse of innovation and technology, ranking 6th in global innovation according to Global Finance. Mark my words – Israeli tech will continue to deliver #nomatterwhat.

But VC Cafe’s reach extends beyond the Israeli tech scene, providing me with a unique lens into the evolution of global tech megatrends. The contrast is stark when I think about the state of mobile technology back in 2006 compared to its dominance today, or the explosive growth and influence of social media and most recently, AI. This is a game changer in so many levels, one I started writing about in 2019 (when we at Remagine Ventures made our first investment into HourOne). Back then, we called it synthetic media, and over time we evolved to calling it creative automation and now you’d have to live under a rock to not know about generative AI.

While we are all attracted to success stories, it’s been equally interesting to learn from failures, like the rise and fall of metaverse (mostly due to too high expectation and unfinished tech), creator economy, VR, web3 (NFTs and ownership of the Internet) and various crypto scandals. These stories are not fully written yet, and we should not write them off, but their timeline has certainly shifted.

I started VC Cafe from a real curiosity about the bourgeoning new medium of blogging back in the day (see my 15 year VC Cafe anniversary post), and from a desire to help shine a positive light on Israeli startups. That curiosity and desire haven’t subsided over the years. With time, my focus changed from reporting on the news, to focusing on writing about the sectors and trends I’m particularly excited about or areas we invest in with Remagine Ventures, a fund I co-founded with my partner Kevin Baxpehler who’ve I known for over 20 years. And while it’s not always been easy to write or keep it going, I’m proud of all that I’ve accomplished with it. I look forward for what the future of tech, and in particular, Israeli tech, has to bring, and hope to be able to continue writing about it in the next 18 years! Thank you for your support.

Generative AI that can do WHAT?

It’s just been the first anniversary since ChatGPT burst into the world and launched the term ‘generative AI’ into the mainstream. It’s been a big year for AI. While the year end numbers are still being calculated there’s been over $15.2 billion invested in generative AI startups globally in the first half of 2023 according to Pitchbook. The figures VC investments in generative AI for the whole of 2023 are expected to be way over $20 billion.

Much of the funding was raised by a handful of companies building their own LLMs and foundational models (including OpenAI, Anthropic, Cohere, Mistral AI, Stability AI etc), there’s been a number of application layer unicorns created including Character.ai, Runway ML, Synthesia, Hugging Face and others.

The pace of technological advancement has been frantic. From Open Source LLMs, to new APIs, both startups and tech giants continue to push the envelope on what is possible to automate and improve using generative AI technologies.

But while the main tools like ChatGPT or Dall-e 3 are well known, there’s a very long tail of products, projects and research papers that while relatively unknown, they will drop your jaw off. Let me dive in to a few examples in the world of video.

Animate Anyone by Alibaba

Animate Anyone is an SDK created by the AI research team of Alibaba that can animate a single picture into a dancing character video with remarkable consistency and control. To explain the potentials implications of this consider that TikTok has 400 million videos uploaded to its platform daily. How many of those are of people dancing? It’s hard to know for sure, but this can transform content creation for social media.

Seamless Communication by Meta AI

Another example of cool new tech that is relatively obscure is the “Seamless communication” models by Meta AI. This suite of AI language translation models not only enables characters to seamlessly have a character speak in another language, but also to keep its tone of voice, pauses, and emphasis. It also helps preserve facial expressions and improve streaming.

Gaussian Avatars by the Technical University of Munich and Toyota

Gaussian Avatars are Photorealistic Head Avatars with Rigged 3D Gaussians. The avatars are edited and rendered in realtime. While the technology is still not 100% reliable, you can imagine what a potential nightmare in can create for people impersonation on video calls, or ads…

The list of examples, with really incredible results, goes on and on. While each of these demos has a lot of promise, their significance is not in the specific tech features, but in what they represent for the generative AI space as a whole.

The big tech giants have the 3 pre-requisites for AI innovation

Incumbents have a lot of power when it comes to AI research – to create ground breaking technology in generative AI, companies need 3 things:

1) access to top notch AI researchers – $$$

2) access to vast amounts of data – $$

3) access to Nvidia GPUs and cloud resources – $$$$ (it’s expensive to train a model and subsequently to offer it to the public)

All of these lend themselves well to large tech companies. Alibaba, Google, Microsoft, Amazon, and even companies like Toyota, can satisfy all three criteria. But for startups, it’s a difficult feat unless they have access to deep pulls of capital, hence the hundreds of millions rounds raised by Anthropic, Mistral, Runway ML, etc.

It’s tricky for investors to allocate in this space

The biggest risk for investors allocating capital in the generative AI space (apart from FOMO driven decisions) is the risk of commoditisation. The space is moving so quickly, that what is novel today, becomes abundantly available tomorrow. There are many examples already. Such as services that offered AI generated avatar pictures for a fee. While there may still people paying $19 for a picture, it won’t take long until they learn they can do it for free (or for the same price of a pro subscription for OpenAI’s Dall-e 3).

In addition, investors should care about where the data to train the models came from. There’s a reason why OpenAI offers to cover the legal fees of business customers sued for copyright infringement. In doing so, OpenAI joins IBM, Microsoft, Amazon, Getty Images, Shutterstock and Adobe who’ve also explicitly said they’ll indemnify generative AI customers over IP rights claims.

Asking for forgiveness rather than permission might work for startups, but definitely an inhibitor to adoption for enterprise clients. A bank for example, wouldn’t risk a lawsuit for using pirated content. Several lawsuits are currently in motion and might create precedents for the future. Despite Biden’s executive order on AI, stipulating that training data should be licensed, this area is still a mess.

Regulation will have a big impact on the generative AI space

There’s not a question of ‘IF’ generative AI will be regulated, but the question of ‘HOW’ is still wide open. The UK government recently hosted an AI safety summit in Bletchley Park, and the EU is about to launch its AI Act, a well intended set of rules, that companies will struggle to keep, therefore forcing products not to be active in the market and inhibiting innovation.

In addition, experts already warn that we don’t have the guardrails in place for companies to rush into deploying AI. Hackers and bad actors are already leveraging generative AI technology for nefarious reasons including spam, phishing and impersonation.

Open Source might be the future, but it faces big hurdles

Tools like Chatbot Arena are useful in comparing the quality of results on various models. For example, it’s still pretty clear that ChatGPT pro, based on GPT-4, the largest language model commercially available, is better than Anthropic (although the latter is catching up), which is in turn better than LLaMa-2 etc. But companies like Mistral are claiming that new LLMs can be much smaller, more accurate when focused on specific tasks, and fully open sourced.

Mistral AI believes in the democratization of AI and the power of open collaboration. They aim to make their models and research readily available to the public, fostering innovation and accelerating the development of beneficial AI technologies. But companies like OpenAI and Anthropic advocate that we should be careful on what companies get to train new models. This could create a situation in which regulation is helping the incumbents concentrate power and slow down open source development significantly.

Listen to Anthropic’s CEO talk about the challenge of Open Source:

It’s hard to make money

While OpenAI is reportedly on track to close on $1 billion in revenue in 2023, many companies in the pace, primarily in the application layer (i.e. not developing the generative AI tech directly, but rather using an API and building a wrapper product) have struggled to maintain the revenue over time. Pay attention to what I said – it’s not that they struggled to generate revenue altogether – some made a quick buck by being first to the market or offering a novel use of the tech. But many also suffered from churn just as quickly, as their services become commoditised.

A good exception to this challenge is Jasper. The company was valued at $1 billion just before of ChatGPT and it’s safe to assume they’ve lost a lot of customers to free alternatives. But they are still in business. The primary reason is that they’ve been able to build workflow automations that save time, and streamline the task automation for their clients. I suspect a lot of companies will choose to adopt similar tactics to survive, the question is, would that be enough?

We need to talk about hallucinations

While incredibly impressive, no model is free of limitations. One of the major limitations of LLMs is the risk of model hallucinations. Read: the model’s tendency to write very convincingly answers, that are totally wrong/ made up. While products like Anthropic’s Claude.ai claim to reduce hallucinations

We need to talk about AGI, and its risks

While some like Meta’s head of AI Yan LeCun downplay the existential threat AI could pose on humanity, others, like OpenAI’s co-founder and CTO Ilya Sutskever are pretty certain that we’re on the path to achieving AGI (artificial general intelligence), a tool so powerful that will be able to teach itself and ‘think’ for itself in the foreseeable future (no specific timeline, but assume a decade to be pragmatic). Ilya suggests that the AI might treat humans as inferiors, akin to how humans treat animals. A lot of what happened behind closed doors that led to the board firing (and later the investors re-instating) Sam Altman as CEO, I suspect it had to do with the path and timeline to AGI.

Once we achieve AGI, most of the technologies that have been developed (and funded) before that have the risk of becoming obsolete. Let alone incumbent technology products that have yet to be powered by AI. While regulation is trying to address some of these challenges, there are not enough people, professional bodies and companies talking about the risks and need to balance innovation with safeguarding measures.

***

If you read this far, allow me to indulge in a short self-pitch. Given our background in media and entertainment at Remagine Ventures, we always cared about technology that automates content creation, distribution and monetisation. Combined with a natural curiosity, that led us to make our first generative AI investments in 2019. We’ve since invested in 5 other generative AI startups, including companies developing foundational models as well as fast-growing startups in the application layer. We’re primarily focused on Israel and the UK. If you have an original approach, and building the next big thing in the generative AI space, we’d love to talk to you and give you a friendly investor perspective.

State of Israeli tech in Q4 2023

Two new industry reports paint the picture of the Israeli tech ecosystem in 2023. The picture that emerges is one of a major decline in venture activity (consistent, but a bit stronger than the declines in US and Europe), but on the other hand, we’re starting to see the signs of stability in the rate of decline.

According to the preliminary Q3 2023 report by IVC (a revised version should come out mid October), Israeli tech companies raised $1.67 billion in the third quarter of 2023 in 85 deals. The volume of investment reflects a 14% decrease from Q2 2023, and a 38% decrease compared to Q2 2022. The number of transactions is down as well, 41% less than 2022.

A bright spot is emerging in early-stage funding, signaling renewed investor risk appetite and startup momentum. Seed funding increased in the third quarter, the first rise after five straight quarters of decline. The number of seed rounds dipped from the previous quarter, but is expected to show an upward turn once final data comes in. The revival of seed funding is an encouraging sign of a return to startup growth after a period of caution. More investors are again willing to bet on young companies, providing the capital for the next wave of innovation. If the seed funding resurgence continues, it could mark a turning point for the startup ecosystem.

The second report, by Startup Nation Central (SNC) Policy Institute paints a similar picture but points a picture at the governments proposed judicial reform as the cause for reduced foreign investor activity (down 40% year over year up to Q3 2023). Historically, foreign investors represent the bulk of the capital invested in Israeli high tech.

Another interesting stat coming out of the SNC report, backed with data from Pitchbook, finds that the rate of decline in Israeli tech investments has been sharper than that of the US and Europe, after coming down from the peak in 2021.

Generative AI is a beacon of hope

In this grim scenario, Artificial Intelligence, and more specifically Generative AI, emerges as the global industry’s lifebuoy, attracting investments and elevating valuations of entities across the spectrum. The rise in the number of funding rounds for AI startups in Q2 and Q3 is a testament to this trend, with Israel being no exception. A recent study by Capital Economics suggests that the US will lead the AI revolution, with the Asian Tigers, UK, Israel, and parts of the Nordics also well-placed to benefit.

Just last week, our team at Remagine Ventures published the updated Israeli Generative AI startup landscape and found that the number of Israeli generative AI companies more than doubled in the past six months and that funding to date has reached $2.3 billion. The actual amount might be higher as companies choose to stay in stealth for longer.

Looking forward and conclusions

To keep its reputation as ‘Startup Nation’ Israeli entrepreneur need to overcome global trends of rising inflation, but also self inflected wounds caused by its local government. The proposed judicial reforms caused some short-term uncertainty. But Israel’s fundamental strengths – world-class tech talent, cutting-edge R&D, entrepreneurial culture – remain unmatched. Investors know this. With a compromise on judicial reform, I would expect (and hope) investor confidence to bounce back.

Startup death rates spike as we approach Q4 2023

You know the adage that 90% of startups fail in their first 3 years of existence? According to a startup genome report from 2019, that figure is actually a bit higher, 11 out of 12 startups fail (which is 91.6%).

But now, startup failure rates, which were already elevated in 2022, have trended even higher as we approach Q4 2023 according to new data from Carta. As the chart below shows, 543 startups have shut down so far this year compared to just 467 in all of 2022 (the data is limited to Carta users only and more US-biased, so the bigger picture is likely to be worse).

The Carta data showed the following trends:

Startups shutting down as of Q3 2023

Reasons for elevated startup failure

According to CBInsights, the #1 cause for startup failure is running out of money. Of course, that’s more often than not a symptom, not the direct cause (i.e. bad management, lack of product market fit, costly mistakes that accelerated the said running out of money). But in the recent figures, in addition to the ‘normal’ startup risks, something else is at play.

While the reasons for failure vary, it’s clear that mounting macroeconomic headwinds combined with a reduction of over 50% in VC funding in 2023 have contributed to the accelerating failure rate. Startups that raised in 2021 and 2022 often at high valuations, had to adjust quickly to the new market conditions of 2023.

Many conducted layoffs, reduced burn and and either aimed for profitability or to become ‘default alive’, meaning being able to survive indefinitely without external funding. But many had to swallow a bitter pill and either raise extension rounds at flat/down valuations (causing painful dilution) or sell, often at prices lower than their last round valuation.

Now the effect of those extension rounds, debt (which has become expensive to serve given the higher interest rates) and slow commercial markets (consumer cost of living up and discretionary spend down, companies tightening the belt etc) have also meant that companies found lower organic growth to a large extent.

Of course, a certain startup mortality rate is expected and even healthy for the broader ecosystem. It comes with the territory. But the current times call for tighter planning and founders would be wise to adjust their expectations on growth at all cost, and expect longer funding cycles and smaller round sizes in the near future. There are exceptions to every rule of course, but in a nutshell 2023 has been, and will probably continue to be, a challenging year.

The Israeli Generative AI startup Landscape (Sep 2023)

* This post was written by Kevin Baxpehler, Amit Revivo and Eze Vidra, and originally published in Calcaslist on September 21st, 2023.

So much has happened in the generative AI space globally, and in Israel since we published the previous version of the Israeli generative AI landscape, back almost exactly five months ago. We’ve seen new foundational models, open source LLMs and all sorts of new companies building the co-pilots for every category. The technology is advancing at a breathtaking pace and it’s enough to look at Hugging Face’s LLM leaderboard to understand that generative AI is much more than just ChatGPT. So far in 2023, generative AI startups raised $16 billion, according to Dealroom.

In the past five months, the number of Israeli Gen AI related startups in Israel has more than doubled from 67 to 144 companies included in the current landscape below. The total amount raised by Israeli generative AI startups so far has reached $2.3 billion, a 2.5x increase compared to April this year (This is not all new funding, as we added companies that were established prior but were not on our first landscape). Israeli startups were largely ‘skipped’ in the mega rounds that occurred in this space. The largest round so far in Israeli generative belongs to AI21 Labs, which recently raised $155 million series C. (Source: SNC, IVC, public data and Remagine Ventures’ data)

The Israel generative AI startup landscape (September 2023)

At Remagine Ventures, we’ve been interested in tools for content creation, distribution and monesation from day one. While we called it ‘Synthetic media’, or ‘Creative Automation’, we made our first generative AI investment in 2019, writing the first cheque for Hour One, the company building foundational models for text to video based on human and animated avatars. We’ve since invested in 6 more startups in the generative AI space, mainly at the intersection with entertainment and gaming. For example, Munch, providing automated vertical video clips from long form content or KwaKwa, the latest product by Piggy, powering communities. Our most recent investment in June this year has been Playo, a company developing foundational models to power full game experiences created from a text prompt.

We believe that Israeli founders have the necessary ingredients to be successful in the generative AI space: 

While Gartner confirmed that generative AI has reached the peak of the hype cycle for emerging technologies, at Remagine Ventures we believe that LLMs and generative AI technologies are game changers that have the potential to change entire verticals and that large companies will be built in this space. We are excited to continue to be the first backers of Israeli founders re-inventing the future with generative AI and see exciting opportunities in vertical SaaS (co-pilots for specific industries), task oriented models, and tooling for low-code/no-code gen AI based applications. You can read more about our areas of interest in our blog, VC Cafe or via the Remagine Ventures Pulse, our monthly newsletter. 

Housekeeping:

Three buckets of generative AI opportunities

I was watching a fireside chat from SaaStr 2023 between Jason Lemkin and David Sacks (ex Paypal Mafia, All In podcast co-founder and managing partner at Craft Ventures, with $3.3 billion under management). David and Craft are considered top SaaS investors and David said that basically, the industry has been waiting for a catalyst, something that will accelerate digital transformation and help SaaS companies grow faster, and Generative AI seems to be it.

https://www.vccafe.com/wp-content/uploads/2023/09/Exploring-AI-Opportunities_-Assessing-the-Big-Tech-Landscape-1.mp4

Interestingly Craft Ventures divides AI opportunities into three main buckets:

?? Infrastructure – massive rounds getting done at high valuations, but ultimately, David believes infrastructure will be owned by the the big tech and cloud platforms.

?? Co-pilots – every industry will have its own co-pilots. Lawyers, doctors, architects, designers, etc. There will also be horizontal, cross industry co-pilots like marketing, accounting, sales, etc. This is an opportunity for founders as large companies are less likely to venture in here.

? Pre-AI SaaS companies that are getting turbo charged by new AI capabilities.
Gong is able to analyse content of calls, Notion autocompletes page, etc.

Interestingly, one of the major points of discussion was if today, a company trying to say, disrupt payroll, can attract funding if it’s not using AI. The answer, at least for now, was yes, but the question is for how long.

As I mentioned in my previous post on VC Cafe, I believe there’s value in generative AI beyond the picks and shovels. Publicly traded companies that have been founded 20-30 years ago and are slow to adapt will become targets for disruption by AI-first products that require lower costs and increase automation.

The full talk is embedded below:

There’s more to generative AI than picks and shovels

It’s a bit crazy to think that even a year ago, the term ‘generative AI’ was not exactly on everyone’s lips. ChatGPT, and to a large extent LLMs, burst into the scene only in November 2022 (after many years of research). Large language models (LLMs) have not only transformed the activities performed by computers but have also fundamentally altered the core nature of human-computer engagement.

Consider what followed since November:

To put things in context, let’s start with the basics:

2023 witnessed an impressive influx of $15 billion invested in generative AI startups, significantly surpassing last year’s $4 billion. It’s essential to note that this figure is influenced by several massive transactions, including the $10 billion round of funding received by OpenAI.

In the US alone, the share of venture capital dedicated to AI doubled in 2023 alone

There have been a few other ‘mega rounds’, mostly into foundational models, with some exceptions (such as Character AI)

If you take into account how that capital has been distributed over the past year, the majority went to what you’d refer to as the ‘picks and shovels’ of generative AI – new LLMs (to rival GPT), GPUs (to rival Nvidia), data and training, etc

As opposed to other major platform shifts, where perhaps the incumbents were late to lean in (Microsoft missed mobile, Google missed social, etc), when it comes to generative AI, while many were hit by surprise to the success of ChatGPT, they were quick to respond and lean in massively.

There’s a question on whether all this GPU hogging, and building capacity is not just a bubble waiting to pop, similar to the Telecom crash in the early 2000’s. As David Sacks from All in put it:

On aggregate, a lot of funding was also deployed to startups in the application layer – investing in founders leveraging generative AI to either solve narrow tasks, or bring automation to tasks to reduce costs, increase accuracy, speed etc. As a data point, 60% of the 212 startups in the Summer 2023 Ycombinator batch that has just graduated identified themselves as AI startups.

Generative AI startups that raised more than $5M, March 2023

Gartner recently confirmed that we’re at the peak hype of generative AI, and that perhaps because of it, many investors have been waiting by the sidelines. Why should they risk investing in a startup that could get commoditised by either OpenAI, Google, Microsoft or open source technology? And even if they find a fantastic team working on a valuable problem, would it be redundant if we reach AGI in the next decade?

But rather than dismiss anything that is not ‘picks and shovels’ in generative AI, few opinionated funds and investors (ourselves at Remagine Ventures included) are saying – “don’t be so quick to dismiss this”. I recently wrote about opportunities for Israeli startups in generative AI as well as why gaming is ripe to be disrupted.

Seth Rosenberg at Graylock published ‘Product Led AI’ – pointing to opportunities for founders building AI-first companies. For example, he sees great potential ‘for co-pilots are “branded” sales people, like wealth managers, insurance brokers, and mortgage brokers’ and re-defining the product surface area, for example: how would AI change customer service or productivity?

Here’s an interesting excerpt on finding value despite the noise:

There’s lots of noise in AI. From true techno optimists who envision AI as the great amplifier of humans, to pessimists who see every app as just a thin layer on top of OpenAI, to the optimists-turned-pessimists who believe AI will automate all jobs (and take over humanity).

Undoubtedly, there will be detractors who believe many products are simply features on top of foundational models. But builders who see AI as the driving force behind product development and GTM strategy will actually create new markets and experiences that never existed before.

By combining expertise in products and domains with a fundamental understanding of human behavior and AI, these builders will bring defensible, valuable AI-first products to life.

Seth Rosenberg, Greylock

Christoph Janz, co-founder and managing partner of Point Nine Ventures, pointed to the potential of Generative AI to accelerate the adoption of vertical SaaS. As a seasoned SaaS investor (and the person behind the SaaS napkin, one of my favourite SaaS industry benchmarks), he rightfully points out that the new crop of SaaS companies will be built with AI in mind, and leveraging AI, startups can create ‘magical user experiences’, that will accelerate the ‘time to wow’ and unlock a lot of automation for SMBs, and not just enterprise clients. Index Ventures also pointed out the potential of generative AI to disrupt vertical SaaS.

Investors, innovators, and visionaries should look deeper and recognise generative AI as a powerhouse, not just a tool in the shed. While it’s not an easy time or area to deploy into, I believe that a lot of large companies are going to be created with an AI-first approach, and with less resources required than their pre-AI predecessors.

Gaming investments were down 81% in H1 2023, tips for gaming founders raising in this climate

Gaming investments were down 81% in H1 of 2023 compared to the equivalent period last year, according to the latest report by Investgame. It’s not that consumers are spending less time playing games, but rather investors are being more selective in what companies they back. The majority of the decline comes from much lower investment volumes in the growth stage, but also fewer companies getting funded in the early stages.

Figures released by Carta paint a similar picture.

If you’re a gaming founder, at the earliest stages, looking to raise money, below are a few tips to maximise your chances of success.

  1. Do your research and understand the market. Before you start pitching to investors, make sure you have a deep understanding of the gaming market, including the latest trends, the target audience, and the competition. What genre are you in? How big is that market? Is it growing? Who would be your closest competitors? Having clear answers to this will help you to create a more compelling pitch and demonstrate that you have a good understanding of the business.
  2. Build a strong team – this sounds obvious, but is especially important at the early stage, where there are less metrics or revenue to assess the attractiveness of your company to investors. Investors are more likely to invest in a team with a proven track record and a strong passion for the gaming industry. Make sure you have a team of experienced professionals who are committed to making your game a success. It’s better if the team has a track record of working together, and if they bring best practices from larger gaming companies.
  3. Have a clear vision and strategy for where the current round takes you. Investors want to know that you have a clear vision for your game and a plan for how you are going to achieve it. They are thinking about the next round and it’s important you’re able to articulate where the funding you’re looking to raise will take the company in terms of milestones/metrics. Be able to articulate your vision in a concise and compelling way. What roles will you need to hire? Do you have the skills in house to do UA/ retention? Sometimes, it’s better to think about starting with a smaller round to hit the right D1 and D7 KPIs, but without those it would be tough to raise a seed round.
  4. Demonstrate traction. Investors conversations go much easier when you already have some form of validation that your game mechanics work well even with a small cohort of testers. If you have a test flight version, offer investors to play the game, even ahead of a pitch. Investors want to see that there is already some interest in your game. This could include things like pre-registrations, social media following, or positive press coverage. If you can demonstrate that there is already some demand for your game, it will make it more attractive to investors.
  5. Think about innovation and differentiation. Take Steam for example – over 12,000 games were released on Steam in 2022. Assuming the 2023 has a similar rate, new games on Steam are expected to hit 13,728 this year…which is 37.6 games, every single day. How do you stand out in this environment? Is your studio embracing technology in a way that will help you reduce cost, increase speed and maximise KPIs? Generative AI poses a lot of interesting new opportunities in this space and you’ll be smart to be ahead of the curve.

In addition to these tips, it is also important to be persistent and patient. Raising investment can be a long and challenging process, and not every game will necessarily fit the venture capital model. Even if your pitch didn’t succeed at first, take note of investors who are supportive and offer help and keep them posted on your progress. At Remagine Ventures for example, we love to meet founders early in their journey and provide candid feedback. Want to have a chat? You don’t need to wait for a warm intro, just get in touch with us at info @ remagineventures dot com.

ChatGPT for Enterprise is Ready. But are Enterprises ready to adopt Generative AI?

“Since ChatGPT’s launch just nine months ago, we’ve seen teams adopt it in over 80% of Fortune 500 companies”

OpenAI blog

OpenAI announced this that ChatGPT Enterprise is now available, offering better data protection/privacy, more security, improved speed/performance and longer context windows. The Information revealed that OpenAI passed the $1 billion revenue pace over the next 12 months, far ahead of its projections.

According to the Information, as of March of this year, OpenAI had between 1 million and 2 million ChatGPT subscribers paying $20 per month, so conservatively speaking, most of the company’s revenue is coming from its enterprise clients. Despite the fact that ChatGPT isn’t yet a year old, OpenAI counts amongst its enterprise clients Fortune500 companies across industries including Stripe, Duolingo, Databricks, Volvo, Coca Cola, Morgan Stanley, Zoom, Canva, PWC, Shopify, Square, Zendesk and others. Impressive list given the short time the product has been in the market.

For many companies, the base use case is to deploy an internal chatbot, powered by GPT-4 that can serve employees to search and engage with the company’s internal information (with the right level of access control and data management), without model hallucinations. The consumer product that was available until now, fell short in a number of features that OpenAI addressed with their current launch. But will it be enough for enterprises to adopt ChatGPT into their organisations?

Unpacking ChatGPT Enterprise offering

To gain enterprise adoption, products face a higher bar than consumer. Security, SLAs, access control, are just a few of the minimum requirements to enter. The new ChatGPT for Enterprise runs faster, and offers more security and privacy features. The enterprise features followed “Fine Tuning”, a feature by OpenAI that enables developers using the OpenAI API to customise GPT 3.5 Turbo model by restricting the data sources, showing strong performance for narrow tasks. This enables to adjust the tone of the model, so the output fits the company’s brand/language, improve model streerability (i.e. the model’s ability to follow instructions), and to better control the format of the model’s responses (critical for use with third party APIs). The Enterprise version takes all these features one step further.

OpenAI for enterprise key features:

Samsung Electronics, Google and others have restricted employees from using generative AI bots for fear that confidential information will be leaked. OpenAI’s new enterprise features addresses their data privacy concerns, to some extent, as prompts won’t be used for training. Security has been a another major concern in enterprise adoption, according to a survey by McKinsey & Company, and the Soc2 compliance might help IT departments tick the box on security and privacy compliance. Another major feature is the performance

Generative AI related risks that organisations raised in April 2023 (McKinsey & Company)
Barriers for enterprise adoption of generative AI tools, KPMG survey March 2023

Who lives who dies who tells your story?

Much excitement for AI remains in the techindustry. Microsoft, Google and otherestablished companies are investing heavilyand rolling out new AI products, and business isbooming for Nvidia, whose chips are used totrain AI models.

WSJ

On the curtails of OpenAI’s announcement, Google Cloud today shared a number of new generative AI features coming to Google’s Cloud services: Model Garden, a collection of 100 different models including Meta’s LLaMa 2 and Anthropic’s Claude 2, automations to Gmail and Google Docs, improved performance on code generation, Google Meet automations, new copy creation tools, etc.

One of the main beneficiaries of all of this is of course Nvidia, which currently provides the best performing GPUs for generative AI. Nvidia’s CEO Jensen Huang stunned Wall Street with a record $13.5 billion quarterly revenue, driven by surging demand for its AI chips. It represents an 88% increase QoQ. Many suspected this was the peak for Nvidia, but the company yesterday announced a partnership with Google Cloud, which took the company’s market cap to $1.2 trillion, fast approaching Apple ($1.4 trillion).

But it’s not just large tech companies fighting for their share of cloud revenue – startups are entering the space quickly. As I mentioned before, I believe we will see generative AI tools supporting every role companies and adding automation, especially to manual repetitive tasks.

Enterprise AI market map (by Weekend Fund)

As an example, take Ycombinator. Generative AI startups accounted for 22% of YC’s winter 2023 batch across several categories:

Investment into the generative AI space is growing too. In 2023 so far, Generative AI startups raised $15.2B so far in 2023. In Q2 2023, investment in generative AI startups—those focused on systems that produce humanlike text, images, and computer code—increased 65% to $3.3 billion. Goldman Sachs forecasts that AI investments AI investment will approach $200 billion globally by 2025.

But not all is rosy. Venture investors are realizing that generative artificial intelligence might not be enough to stem years long startup downturn. As Index Ventures Partner Mark Goldberg put it in a WSJ article: “there is a shallow trough of disillusionment”.

Startups that enjoyed the buzz are now realising that they also need to become good businesses, not just cool technology, to survive.

Barriers remain for Enterprise adoption of Generative AI tools

While the current OpenAI release opens the door for enterprises to start using a super-version of ChatGPT, wider enterprise adoption will also depend on:

1) Price – OpenAI didn’t publish its price for enterprise clients, but it’s safe to assume it ain’t cheap.

2) Accuracy (unclear if OpenAI can completely stop hallucinations)

3) Copyright – should enterprises risk adopting Generative AI tools that are based on copyright infringed training data?

4) Pending regulation

5) GPU shortage and availability

Are we ready for the impact of Generative AI enterprise adoption?

“AI will not replace you, but the person using AI will”

We’re starting to see announcements from CEOs like IBM CEO Arvind Krishna, who said the company will replace ~8k jobs with AI, largely in non customer-facing roles across departments like HR, finance, and accounting. Or PWC, who said it will invest $1 billion in generative AI over the next 3 years.

There are many studies and statistics on the number of jobs that are likely to be lost due to AI and automation. But we don’t yet have much information about the jobs that could be created as a result of these new tools.

Goldman Sachs study on the economic impact of generative AI

One thing is certain, for startups, it’s an incredible time to be building tools and solutions in this space, even at the application layer of generative AI. Clever founding teams can do much more with limited resources and automate workflows for both consumers and enterprises across verticals. For investors, deploying capital in this space remains attractive, but it’s not always straightforward. The many startups using OpenAI’s API to bring ChatGPT to the enterprise are finding themselves nearly redundant today, and with the pace the generative AI space is moving, it’s nearly impossible to predict what isn’t going to get commoditised soon.

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