We use cookies on this site to enhance your experience. Visit our Privacy Policy for more info.

AI-list: Chips and dips

AI is a rapidly developing sector that Insight has invested more than $4B since 2014. We’re currently tracking 23,315 AI/ML startups and ScaleUps. Naturally, our team has seen a lot, and the discussions around this topic generate a lot of opinions.

Here’s a look behind the curtain into some of what we’re reading, sharing, and discussing across the team at Insight lately. Think I missed something? Email me and tell me all about it.

Less is not more

Like every one of my middle school crushes, this week began with extreme enthusiasm followed by extreme nerd outrage for Meta’s LIMA (Less Is More for Alignment) model.

The concept of an effective language model that doesn’t need to be trained on massive amounts of data is appealing — it would open up incredible possibilities for open-source innovation and speciality uses cases. Alpaca was another model making some noise along these lines earlier in the year:

The False Promise of Imitating Proprietary LLMs

…unfortunately, research published yesterday throws cold water on all of this. “An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Instruct, and others). This approach looks to cheaply imitate the proprietary model’s capabilities using a weaker open-source model.”

“Overall, we conclude that model imitation is a false promise: there exists a substantial capabilities gap between open and closed LMs that, with current methods, can only be bridged using an unwieldy amount of imitation data or by using more capable base LMs. In turn, we argue that the highest leverage action for improving open-source models is to tackle the difficult challenge of developing better base LMs, rather than taking the shortcut of imitating proprietary systems.” Brutal.

Generative AI Pilots Have Companies Reaching for the Guardrails

Via WSJ, chip maker Nvidia recently released a guardrails tool to help companies anxious about use of ChatGPT and other AI tools employees may be using with proprietary or sensitive company data. The release helps developers set limits on what users can do with LLMs, “such as restricting certain topics and detecting misinformation and preventing execution of malicious code.” Per the article, Apple, Verizon, and other companies have restricted or banned access to AI tools like Chat GPT — but from our own study of the space, we see many big financial institutions finding ways to embrace AI tech to their own benefit.

Of course, there’s also always the perpetual question of bias in AI, which we expect to hear more about as responsible AI concerns grow louder with widespread adoption. And all this comes on the heels of Sam Altman’s testimony to Congress last week, with subsequent announcements from OpenAI about AI governance.


Neuralink just got FDA approval to launch their first-in-human clinical study. All snark and singularity commentary aside, chip implants could mean massive quality of life improvements and regained autonomy for those with disabilities.

Indeed, implanted chips have shown incredible, life-changing promise, at least in one recent case.


“Remember when software wasn’t connected to the internet? Didn’t think so.”

This is the savage intro for Microsoft’s blog post covering announcements from this week’s developer conference. Excuse me while I cry in elder millennial…I remember booting up my Gateway desktop (delivered in those cow-print boxes!) in MS-DOS to play Doom II from disk (an incredibly inappropriate game for a grade schooler to play, but that’s a topic for another time).

Anyway, Microsoft announced a multitude of AI-powered plugins integrated into their ecosystem of products. It’s not particularly elegant (hope you love chat bubbles) but the new Copilot stack caught our eye.

After the smashing success of the Clippy jumpers this past holiday season, I was truly hoping for a nostalgic resurgence of this iconic personality in Microsoft’s AI rollout — but it seems like these persistent, faceless bubbles will be the new norm.

Grill fodder

Bill Gates, have you even met Tony Stark? “Whoever wins the personal agent, that’s the big thing, because you will never go to a search site again, you will never go to a productivity site, you’ll never go to Amazon again,” he said this week at an AI event. Yeah Bill, when can I get Paul Bettany at my beck and call?

Another example of incumbents adding generative AI features. Adobe announced a beta of “generative fill” this week in Photoshop, and the demo is as slick as you would expect.

Of course, the internet had some fun with this news.

Say hi. Are you an Insight portfolio company integrating AI into your solution? I want to hear about it! Let’s connect.

Editor’s note: Articles are sourced from an ongoing, internal Insight AI/Data Teams chat discussion and curated, written, and editorialized by Insight’s VP of Content and Thought Leadership, Jen Jordan, a real human. (Though maybe not for long?)

Image credit: Bret Kavanaugh via Unsplash.

The AI-list: Mr. Altman goes to Washington

AI is a rapidly developing sector that Insight has invested more than $4B since 2014. We’re currently tracking 23,315 AI/ML startups and ScaleUps. Naturally, our team has seen a lot, and the discussions around this topic generate a lot of opinions.

Here’s a look behind the curtain into some of what we’re reading, sharing, and discussing across the team at Insight lately. Think I missed something? Email me and tell me all about it.

OpenAI CEO in “historic” move calls for regulation before Congress

It seems unlikely that lawmakers will be able to take any action on AI before it becomes entirely mainstream. Still, concerns arose this week when OpenAI CEO Sam Altman asked Congress for AI regulation “above a crucial threshold of capabilities.”

Via Axios, concerns that top the list include dangerous and harmful content, impersonation of public and private figures (remember Balenciaga Pope?), and, with 2024 already looming ahead of us: election misinformation.

Watch the whole testimony below while you’re clearing out your inbox (or writing an AI roundup that your boss now expects weekly).

Meta pulls the curtain back on its A.I. chips for the first time

After ending 2022 with barely a literal (virtual) leg to stand on, Meta has been dropping open-source AI innovations at a rapid pace in 2023. This week, they gave us a peek into the chip they’re using to power the Metaverse and generative AI technology. Meta dropped some juicy numbers behind their largest LLaMA model: LLaMA 65B contains 65 billion parameters and was trained on 1.4 trillion tokens.

The company also mentioned they developed an internal generative AI coding tool to help their developers work more efficiently, similar to GitHub Copilot.

Per CNBC, Meta has also been “overhauling its data center designs to focus more on energy-efficient techniques, such as liquid cooling, to reduce excess heat.” A theme — and an opportunity — we’ll see more of in the coming months is the incredible environmental toll of training AI models. Training one AI model is estimated to produce 626,00 pounds of carbon dioxide equivalent, “nearly five times the lifetime emissions of an average American car.”

Investing Opportunities With Generative AI [Video]

What do investors want in an AI-driven business? Via Bloomberg, Insight’s very own George Mathew gave his take on what separates the hype from the real opportunity: the ability to have private data available to build AI products, a focus on user experiences, and having a great workflow that fits the way software fits within the industry. The combination of those aspects can make for a “very compelling” business.

Bits and bots

There’s an app for that. OpenAI dropped a ChatGPT iOS app this week, killing dozens (hundreds?) of third-party apps in the process. I’m sure Google is thrilled.

Meanwhile, the chatbot horserace continues. It seems the recent upgrade to PaLM 2 has given Bard a competitive advantage in head-to-head comparison.

AI long-form. Insight’s AI chat went on a tangent this week when asked for their key reads on, “the impact AI could have on society and civilization.” Here are a couple of recommendations, if you’re looking for a good read during the upcoming Memorial Day Weekend:

  • Of God and Machines
  • Superintelligence by Nick Bostrom
  • The Most Human Human and The Alignment Problem, both by Brian Christian (these are next on my list)

Weekend listening. Yann LeCun on Why Artificial Intelligence Will Not Dominate Humanity, Why No Economists Believe All Jobs Will Be Replaced by AI, Why the Size of Models Matters Less and Less & Why Open Models Beat Closed Models 

Cybersecurity is going to get a lot more difficult. And I *just* successfully explained what phishing is to my mom.

The internet can’t get enough of the Wes Anderson aesthetic. From TikTok trends to incredible AI-generated movie trailers like my personal favorite below (happy belated May 4th).

Editor’s note: Articles are sourced from an ongoing, internal Insight AI/Data Teams chat discussion and curated, written, and editorialized by Insight’s VP of Content and Thought Leadership, Jen Jordan, a real human. (Though maybe not for long?)

Image credit: Google Deepmind via Unsplash. “Neuroscience.” Artist: Chris Schramm

Scale up by the numbers: SaaS Sales KPIs from over 300 companies

Insight is excited to release the latest version of the SaaS Sales KPI Report. This data is self-reported from 300+ software companies that we have worked with in the past year, making this report truly one of the world’s most comprehensive and best sources of private SaaS sales KPI data. This report measures software sales operating performance by:

  • ScaleUp company stage, represented by company size (2022 year-end ARR)
  • Go-to-market motion: Transactional, solution, or consultative motions represented by average selling price (ASP)

While this report provides a point of reference for KPIs across various ScaleUp company stages and sales motions, we understand that every company is unique. These metrics should serve as guiding points through each section of your ScaleUp journey and help you understand where your performance lies in relation to that of your peers.

What’s in the report

The report covers five key areas and 19 individual metrics that are crucial for founders and leaders.

  1. Growth and profitability
  2. Sales efficiency
  3. Retention and churn
  4. Go-to-market strategy
  5. Sales productivity

Additional context and recent trends

Two key trends stand out as we look at this data over the past four years.

1. Growth rates were highest in 2021

After a brief slowdown in the first part of 2020, COVID-19 accelerated the value of software. Most companies saw new and expansion bookings growth rates increase in 2020 vs. 2019, as the sudden need to enable virtual work en masse catalyzed investments in digital transformation.

That trend continued into 2021, this time as both bookings growth rates and ARR growth rates increased compared to 2020. However, as the macro environment shifted in 2022, growth rates slowed and returned to 2020, and in some cases, 2019 levels. This trend is easily visualized when looking at the median ARR growth rates for companies between $0-10M of ARR over the past four years.

saas arr growth rate

2. Sales and marketing costs outpaced revenue growth in 2021 and 2022

Year over year, in both 2021 and 2022, sales and marketing (S&M) as a percentage of revenue increased. In 2022, this increase was likely a result of:

  1. Companies increasing spending in anticipation of future growth in late 2021
  2. The subsequent slowing of growth in 2022 due to the macro environment

When we look at the data broken out quarterly we do see that in the second half of 2022, companies started to reduce their ratio of S&M expenses to revenue. For example, inclusive of companies of all sizes, in Q2 2022 we saw a median S&M as a percentage of revenue of 83%, with an eventual decline in Q3 and Q4. This trend becomes even more apparent when we disaggregate companies into their respective ARR groups (i.e., $0-10M ARR, $10-20M ARR, etc.) and take a more granular view. This indicates that companies started to right-size their spending in response to the macro environment shift in the first half of 2022.

median sales marketing spend

Looking ahead into the second half of 2023

While the future of the broader economy in 2023 is uncertain, the trends of the past few quarters suggest that companies should be aware of their costs, efficiency, and productivity metrics as we move into a new post-COVID economic reality. Paying attention to the metrics highlighted in this report can help make sure you’re setting the foundation for your company to drive sustainable growth into the future.

For additional perspectives on which Sales KPIs to focus on by stage of company maturity, please read ScaleUp by the Numbers: SaaS Sales KPIs for Startups at Every Stage.

The AI-list: I/O, let’s go

AI is a rapidly developing sector that Insight has invested more than $4B since 2014. We’re currently tracking 23,315 AI/ML startups and ScaleUps. Naturally, our team has seen a lot, and the discussions around this topic generate a lot of opinions.

Here’s a look behind the curtain into some of what we’re reading, sharing, and discussing across the team at Insight lately. Think I missed something? Email me and tell me all about it. And don’t forget to save the date for ScaleUp:AI in October. 

Google’s all revved up and ready to go

As teased in the title, Google I/O happened yesterday. And what’s more punk rock than a legacy tech giant up on stage, fighting for a comeback as its market share gets eaten up by both agile upstarts and its longtime search rival? Per Ars Technica: “Google I/O is clearly the ‘We’re extremely jealous of ChatGPT’ show, and the first hour was packed with Google announcing generative AI features for every input box the company has control over.” Saving you a click:

  • Generative AI is coming to Google Search.
  • Generative AI chatbot Bard, which is Its Own Thing and not part of Google Search, is available to everyone now.
  • Generative AI will be powered by PaLM 2, which will introduce multilingualism (over 100 languages), reasoning (datasets including scientific papers and improved capabilities with math and logic), and coding.

In short:

Word of the week: Multimodal

The most unexpected news to drop this week was more open-source innovation from Meta re: multimodal embedding.

Read the research: “ImageBind: One Embedding Space To Bind Them All.”

But that’s not all, folks: Huggingface demonstrates how impressive multimodal capability is in this transformers agent update: “In short, it provides a natural language API on top of transformers: we define a set of curated tools and design an agent to interpret natural language and to use these tools. It is extensible by design; we curated some relevant tools, but we’ll show you how the system can be extended easily to use any tool developed by the community.”

Meta Open Sources Another AI Model, Moats and Open Source, Apple and Meta

From Stratechery, a killer roundup that touches on two of the hottest topics we’re seeing in AI discussions right now: moats (and specifically, where they will occur in open- versus closed-source innovation), and multimodal (specifically, the fourth and most recent AI-related release from Meta).

Bits and bots

  • Humans can’t determine explainability, but maybe other LLMs can, via OpenAI.
  • Good listen from No Priors: Personalizing AI Models with Kelvin Guu, Senior Staff Research Scientist, Google Brain
  • Check out this cool visualization of the LLM space. Source: Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
    large language model (LLM) map visualization
  • Anthropic expands their chatbot (Claude) context window to ~75k words — which is roughly the length of the first Harry Potter book.

  • In response to the leaked “Google has no moat” memo I posted about last week, I present you with *yet another* AI MEGATHREAD in response:

  • Ashton Kutcher, best remembered by this author as the star of Dude, Where’s My Car? (and a mediocre Steve Jobs biopic that I forgot about) reportedly raised a nearly $250M AI fund for Sound Ventures in just five weeks. Does celebrity interest mean we’ve jumped the shark from early adoption to more mainstream awareness?

From the Insight portfolio

  • Nice overall generative AI explainer from Insight portfolio company Assembly AI.
  • “We’re excited to announce the launch of our AI Content Generator — available in Optimizely’s Content Marketing Platform!” (via LinkedIn)

Editor’s note: Articles are sourced from an ongoing, internal Insight AI/Data Teams chat discussion and curated, written, and editorialized by Insight’s VP of Content and Thought Leadership, Jen Jordan, a real human. (Though maybe not for long?)

Insight has invested in Assembly AI and Optimizely.

Image credit: Google Deepmind via Unsplash. “Unsupervised Learning: Depiction of patterns and connections between objects representing of one method in which AI systems learns from their own experiences. Artist: Vincent Schwenk.”

Founder 101: How to Build a Brand Scorecard 

Measuring the ROI of brand investments is a challenge for founders and CEOs. Yet brand is a key growth lever for companies at every stage of growth.  

If you’re not measuring your brand ROI, you’ll find yourself unable to field questions from your board to justify brand spend and prove the impact of your brand-building efforts.

Catalysts to start measuring your brand may include:

  • You’re hiring brand headcount, and need to know what to focus them on 
  • You’ve rebranded and can’t tell if it moved the needle 
  • You’ve hired a brand agency but cannot gauge if they’re doing a good job 
  • You want to mitigate the rising costs of performance marketing as your company scales and competition gets fiercer 
  • You want to get smarter about where and why you invest in brand

While there are many ways to approach brand measurement, the following scalable and customizable method can quickly equip your team with the tools to approach brand with a data-driven mindset. 

Step 1: Align on your brand strategy

Brand is highly misunderstood and overlooked in the world of B2B SaaS. Read Founder 101: Why and when to invest in brand to better understand why brand matters, how to know when you’re ready to invest in brand, and the elements of a strong brand strategy.  

Step 2: Define and understand your core audience

The activation and execution of your brand strategy may vary by audience. While demand generation efforts focus on reaching specific personas, companies, and segments within your target market, your brand efforts will cast a wider net. Brand will build trust and connection with all key stakeholders within your ecosystem. 

Measuring your brand within these audiences can help you track key organizational goals at every stage of growth. As your company matures, your brand measurement will track more complex metrics to capture more channels and audiences as your marketing team and resources grow. 

  • Early stage companies will initially focus on how their brand resonates with customers, prospects, and partners to ensure product-market fit. Example: As a little-known developer tools company, Company A focuses its brand measurement efforts on targeting the developer community by tracking metrics (including mentions, reposts, shares) that correlate with user engagement across sites such as Reddit, GitHub, and Stack Overflow. 
  • Growth companies will start to tailor their brand strategy to incorporate employees, competitors, and industry analysts and influencers to continue driving leadership within their market space. Example: As a well-known brand within the developer community, Company A expands its brand measurement efforts to include how it stacks against competitors, and how well its brand resonates in the market, including with employees, partners, and analysts. 
  • Late-stage companies will start to consider the performance of their investor brand in preparing for exit and aspiring to become a household name.  Example: As a well-known company within the B2B SaaS world, Company A is looking to exit, talking to analysts and investors, and tracking brand metrics that align with their north star of building a brand the market trusts. 

Step 3: Define your scorecard and establish KPIs to track brand impact

Following the strategy you defined in steps one and two, you’ll now build a brand scorecard. To illustrate, we’ll show you the brand scorecard Insight has built for our portfolio companies, the Brand Impact Score™, and walk you through best practices for building a brand measurement framework that’s customizable and scalable, so it can evolve with you at every stage of growth.   

Insight’s Brand Impact Score™  

The approach illustrated below categorizes the elements of brand we generally advise portfolio companies to track. For each of these categories, we’ve chosen 3-5 metrics and created an algorithm that weighs and aggregates these metrics to produce category scores out of 10 and an overall score out of 100.  

Insight brand scorecard

Brand measurement best practices

In creating, testing, and iterating on the Brand Impact Score™ we’ve established the following best practices for brand measurement.   

Make sure you establish…

  • Regular tracking cadences 
  • Track individual metrics systematically in reporting dashboards 
  •  Analyze metrics in aggregate on a quarterly basis 
  • Frequent progress updates, including QoQ and YoY calculations 
  • Quarterly and yearly targets that align with broader organizational goals 
  • Buy-in from executive leadership on metrics and targets to measure success 
  • Visibility by making your brand scorecard accessible across the organization 

Choose metrics that are…

  • Easily accessible 
  • Tied to your company’s revenue goals 
  • Most important to your unique needs and goals as a business  
  • Best suited to your stage of growth, industry, selling motion, geography, and target audience 


  • Brand measurement should align with your funnel and show impact on conversion and revenue. 
  • Start early; you can always iterate as you go. Ensuring real-time visibility into brand tracking is the goal. 
  • The impact of brand work does not happen overnight. Set achievable targets based on historical data.  
  • Quantitative data only shows part of the story. The best brand scorecards incorporate qualitative feedback from voice of the customer programs.

Brand measurement at every stage of growth

Building your brand measurement approach is an iterative process. Your brand scorecard will change as your business grows and your brand strategy evolves. As you continue to invest in brand building, we recommend evaluating brand intelligence solutions like Insight Partners’ portfolio company, BlueOcean, whose AI-powered brand measurement approach provides real-time insights and actionable recommendations to help companies make data-driven decisions that drive growth and strengthen their brand equity. Here’s what that might look like as your scale. 

Brand measurement at every stage of growth

Measuring brand ROI and creating a brand scorecard that tracks progress toward organizational goals is essential for founders and CEOs at all stages of growth. Developing a scalable and customizable approach to brand measurement that aligns with your funnel and shows impact on conversion & revenue will help you maximize investments and drive brand-driven growth. With the right metrics, targets, and visibility, your team will be well-equipped to identify opportunities to improve your impact and reach new heights with your brand.  


The AI-list: The latest AI news as of May 5

AI is a rapidly developing sector that Insight has invested more than $4B since 2014. We’re currently tracking 23,315 AI/ML startups and ScaleUps. Naturally, our team has seen a lot, and the discussions around this topic generate a lot of opinions.

Here’s a look behind the curtain into some of what we’re reading, sharing, and discussing across the team at Insight lately. Think we missed something? Email us and tell us all about it. And don’t forget to save the date for ScaleUp:AI in October.

Geoffrey Hinton tells us why he’s now scared of the tech he helped build

One of the more impactful headlines in recent weeks is that Geoffery Hinton, pioneer of deep learning and widely considered one of the grandfathers of modern artificial intelligence, is leaving Google. Most headlines — including the one I’ve included here, by way of the MIT Tech Review (paywall, sorry) — focus on the doom narrative of “inventor killed by their own invention.” And that’s part of it. The interview with Hinton reveals his reflections on AI being smarter than humans and his motivation to step out of the Google executive lens to take time to think more philosophically about these issues and work with leaders. Not all experts agree, of course: joint recipient of the 2018 Turing Award, Yann LeCun, agrees with Hinton’s premise but does not share his fears. “I believe that intelligent machines will usher in a new renaissance for humanity, a new era of enlightenment,” LeCun is quoted, adding: “Even within the human species, the smartest among us are not the ones who are the most dominating.”

Leaked Google document: “We Have No Moat, And Neither Does OpenAI”

From Simon Willison’s blog, analysis of the juicy internal Google document leaked via Discord. From Simon: “The premise of the paper is that while OpenAI and Google continue to race to build the most powerful language models, their efforts are rapidly being eclipsed by the work happening in the open source community.”

Spoiler: AI improved productivity! But not necessarily for the employees you might expect — it was the less-experienced and less-skilled workers who benefitted the most. From NPR, can AI tech help close the skills gap?

IBM plans to replace 7,800 jobs with AI over time, pauses hiring certain positions

From Ars Technica, it’s the headline many people have feared: being replaced by AI. IBM CEO Arvind Krishna announced this week that they’re pausing hiring for 7,800 positions and says he could see 30% of back-office functions replaced by AI over the next five years. The “year of efficiency” across tech companies (both large and small) continues.

White House unveils an AI plan ahead of meeting with tech CEOs

From CNN, AI leaders met with President Biden this week. In advance of the meeting, the White House announced plans to introduce policies that shape how federal agencies procure and use AI systems. The National Science Foundation also announced they will spend $140 million to promote research and development in AI: “The funds will be used to create research centers that seek to apply AI to issues such as climate change, agriculture and public health.” We’ve come a long way from asking Sundar Pichai basic questions about how the internet works.

Highlights from our portfolio

  • “Deci AI has just dropped a game-changing object detection model that you won’t want to miss: YOLO-NAS 🚀” (via LinkedIn)
  • “ChatGPT can’t generate internal code documentation – because telling the story of your codebase is more complex than that. Guess what? Swimm can ✨” (via LinkedIn)
  • “Honeycomb announces generative AI-driven natural language querying for observability” (via VentureBeat)

Bits and bots

  • We’re seeing some pretty amazing examples of ChatGPT Code Interpreter.

  • Midjourney got an update this week, and I’ve already pinned one of these examples to my Spring outfits mood board.

  • An underrated benefit of AI: making medical reports more accessible. (Related-ish: in mid-April Google Cloud announced their medical LLM, Med-PaLM 2, to “significantly enhance medical experiences.”)

  • Can AI-generated text be reliably detected? Apparently not. “We then provide a theoretical impossibility result indicating that for a sufficiently good language model, even the best-possible detector can only perform marginally better than a random classifier.” This appears to be the case even with watermarks built into LLMs, but there’s sure to be more development here, so stay tuned.
  • Another newsletter covering the zeitgeist of AI. It’s published by Matthew Lynley, Business Insider’s lead reporter on AI and big data. Check out Supervised here.
  • Your college BFF got a sexy AI glowup. LexisNexis launched Lexis+AI to power legal research by incorporating conversational search, summarization, and drafting, to start. (Not to be left out, Thompson Reuters also recently announced a $100M+ investment in AI with plans to incorporate generative AI capabilities later this year.)

Editor’s note: Articles are sourced from an ongoing, internal Insight AI/Data Teams chat discussion and curated, written, and editorialized by Insight’s VP of Content and Thought Leadership, Jen Jordan, a real human. (Though maybe not for long?) 

Deci.AI, Swimm, and Honeycomb are Insight Partners portfolio companies.

Photo by Alina Grubnyak

Investor POV: Parsing the generative AI application layer

Before the ChatGPT release, many had been underestimating the pace of progress in AI – its release was like a spark that lit the fire of innovation around generative AI technology, along with a renewed fundraising dynamic. From ChatGPT (also see ChatGPT plugins and Code Interpreter), to Anthropic’s Claude, to the new BloombergGPT, the pace of innovation in this space has been nothing short of remarkable.

Swift technological progress is evident throughout the tech stack:

Goals of generative AI

If you are new to generative AI, it refers to the use of AI algorithms to:

“autonomously generate novel, high-quality, and coherent content, data, or solutions by understanding and mimicking patterns, structures, and features found in existing input data, often with the aim of enhancing creativity, automating tasks, or solving complex problems”

 – ChatGPT-4 answer for ‘What is the goal of generative AI’ on 3/28/23

Generative AI, for the first time, makes it so that AI not only analyzes existing data and finds patterns in such data but can also create entirely new content itself.

Read more: Our colleagues Ganesh, George, Nikhil, Jenna, and Sunny share some of the areas that excite Insight around the foundation model and tooling/infrastructure layer in The Next Stack: Generative AI from an Investor Perspective.

We are also seeing rapid innovation in generative AI across the application layer. The rest of this post will explore recent developments at the application layer (layer 4: ‘Create & Automate’ in the below graphic), highlighting areas that excite us and a few examples of how generative AI applications deliver substantial value to end users.

generative AI Insight Partners investor perspective

Parsing the generative AI application layer 

We have seen an influx of generative-AI-native companies (meaning, new startups with generative AI at their core) arise in recent months. At Insight, we are tracking 350+ such companies today and have already made several investments in the broader space, including in Jasper (AI copywriter/content creator), Lightricks (AI-powered visual creative app), Mutiny (personalized website and marketing copy), DigitalOwl (AI-powered analysis and summarization of medical records), Writer (AI writing platform), Uizard (AI-powered design), Profluent (AI-powered protein design), Deepdub (AI-powered voice dubbing), and HourOne (video creation tool), to name a few.

We have found it helpful to break the application layer into three parts:

1. Horizontal tooling

Gen-AI-native platforms that seek to own a particular use case or workflow, such as Jasper (AI copywriter and content generator), RunwayML (AI-powered video creation and editing tools), and Tome (AI-powered presentations).

2. Vertical turnkey applications

Gen-AI-native platforms with a focus on a particular vertical or sector, such as Harvey (‘Copilot for law’), Cradle (AI-assisted protein design tools), and Wonder Dynamics (AI-powered platform for game studios/filmmakers).

3. Captive generative AI

Where the capability is so core to an industry that companies will want to build their own generative AI functionalities, either by leveraging foundation models and tooling or (like Bloomberg) training their own foundation models in-house.

These companies typically employ API-based foundation models to power their products, though some occasionally build the models themselves (as seen with BloombergGPT, for instance).

Applications that drive productivity, creativity, and automation

We’re seeing early-stage gen-AI-native companies emerge around horizontal- and vertical-specific applications. These startups can leverage generative AI to create a ‘wow’ experience that gets new users in the door, building out additional workflows around that generative technology over time to retain users and drive further stickiness.

In addition to the companies mentioned above, we are also seeing startups arise with generative AI at their core in many areas, for example:

Horizontal use cases

  • Code – Companies like Bito, Adrenaline, Magic, Mintlify, and Tabnine are leveraging generative AI to drive better developer experiences.
  • Design – Companies like Diagram, Galileo, and Alpaca are leveraging generative AI to enhance the workflows of designers and build ‘Copilot for design.’
  • Cybersecurity – On the negative side, generative AI can increase threats as hackers use it to craft more sophisticated attacks and find vulnerabilities; in light of this, companies like Recorded Future are using generative AI to automate different stages of threat analysis/intelligence reports, helping to better mitigate/respond to threats.
  • Productivity/Knowledge Management – Companies like Mem, Supernormal, and Fermat are driving enhanced user productivity through the use of LLMs.
  • Creator – Companies like Captions and Lightricks are bringing generative AI technology to creatives, paving the way for new creative possibilities.
  • Sales & Customer Success – Companies like Lavender, Regie, and Cohere are bringing personalization and enhanced workflows to GTM teams.
  • Data – Companies like Hex, Akkio, and Seek leverage generative AI to drive enhanced workflows/capabilities for data analysts and SQL / Python lovers everywhere.
  • Search – Companies like Perplexity, You.com, Andi, and Metaphor are hoping to change the way consumers go about search, with generative AI and LLMs at their core.

Vertical use cases

  • Healthcare/Bio – Companies like Profluent, 310.ai, Xyla, and Latch Bio are leveraging generative AI to create new proteins/drugs and drive better patient outcomes.
  • Legal – Companies like ContractPodAi, Harvey, Casetext, Lex, and Rally (Spellbook) are leveraging LLMs to drive efficiency for lawyers and law firms.
  • Gaming – Companies like Kaedim, Inworld, Wonder Dynamics, Latitude (AI Dungeon), and Scenario are using generative AI to drive down the cost of game development and unlock new paradigms for gaming creativity.
  • Construction/Manufacturing – Companies like Augmenta and Architechtures are leveraging generative AI to design buildings, materials, etc.
  • Education – Companies like Iris.ai, Genei, and Scispace are enhancing the workflows of academic researchers through AI-powered summarization and other tools.

Not just for young upstarts

While upstarts are constantly emerging, many established software vendors are also retrofitting generative AI into their product suites. We are excited about the potential for generative AI to serve as a catalyst for users to adopt the latest leading vertical software platforms, especially in verticals with outdated, manual processes and legacy software solutions.

We think traditional scale advantages of vertical SaaS are further compounded in the age of generative AI with reinforcement learning and domain-specific data. We are also excited about application providers with fast-moving teams who continuously reimagine the way their products can work given the power (and limitations) of generative AI technology as it evolves and improves.

We have already started to see this.

  • Microsoft is bringing ‘Copilot’ to the Office 365 suite. This particular three-minute segment (timestamped here) shows the ability to automatically convert a Word doc into a Powerpoint presentation and the auto-analysis of spreadsheet data in Excel.
  • Google introduced new generative AI features to Google Workspace.
  • Salesforce recently announced the first generative AI for CRM, Einstein GPT.
  • As of February 2023, GitHub’s  AI-powered coding assistant, Copilot, had 1M+ users and generated 46% of its users’ code(!)
  • Quora’s chatbot, Poe, lets users ask questions and get answers from the Quora platform.
  • LexisNexis released Lexis+ AI, which summarizes and generates brief and includes a conversational interface.

These are just a handful of the incumbent companies that have incorporated generative AI and are leveraging existing distribution, relevant products and users, and strong, adaptable engineering teams to maintain a competitive edge.

Examples from Insight’s portfolio

Many companies in the Insight portfolio are also integrating generative AI into their product suites to drive higher ROI and build even better product experiences for customers. Insight is working on programs to help existing portfolio companies to incorporate these capabilities into their offerings in a way that is safe, privacy-preserving, and compelling.

  • Code documentation company Swimm’s AI-powered code documentation frees up time and effort for engineering teams.
  • Code intelligence company Sourcegraph’s LLM-powered coding assistant, Cody, is helping drive further developer productivity.
  • AI-powered GI company Iterative Health’s generative healthcare documentation drives time and cost savings, along with higher accuracy.
  • AI-support company Espressive’s recently announced Barista LLM Gateway enables companies to use LLMs such as ChatGPT safely.
  • Database company SingleStore launched an extremely fast and scalable vector database capability that lets you safely and securely incorporate private corporate data into large language model responses.
  • Cybersecurity company Recorded Future, as mentioned above, is using generative AI to automate different stages of threat analysis/intelligence reports.
  • Workflow automation company Bardeen’s AI-powered automation tool, Magic Box, uses LLMs to generate automations for users.
  • Synthetic data company Tonic uses generative AI to create the strongest possible synthetic datasets for customers.
  • Conversational AI company Cognigy’s LLM-powered contact center automation drives enhanced ROI for customers.
  • AI infrastructure company Landing AI recently announced ‘Visual Prompting,’ which allows users to build computer vision workflows with natural language prompts.
  • Last week at RSA, SentinelOne unveiled a novel threat-hunting platform that incorporates various layers of AI technology to provide exceptional security capabilities and an immediate, self-governing response to attacks throughout the whole enterprise.
  • Observability company Honeycomb recently announced the launch of a natural language powered querying, making it possible for developers to ask questions and gather insights around code observability and performance in plain English.

Numerous other portfolio companies will be announcing generative AI functionality in the next few weeks and months, so stay tuned.

These companies can capitalize on their existing ownership of users’ workflows to foster robust user adoption, enhanced user experiences/outcomes, and increased stickiness, which over time, can compound into a strong business/product moat.

Building defensible moats

It will be very interesting to watch where the pools of value will accrue and where economic moats will be the deepest with these new generative AI technologies, if anywhere.

Some believe that companies building large foundation models will reap the most due to the significant investments of time, technical expertise, money, and resources required to create them.

Others argue that companies fine-tuning models for specific use cases will have the deepest economic moats due to the demand-side economies of scale resulting from proprietary training and feedback data. Using the Embeddings APIs to turn datasets into prompt enhancements is another powerful way to leverage private datasets as an advantage to enhance results.

Generative AI could also democratize access to powerful tools in a way that makes it easier for non-AI incumbents to retrofit existing products since many generative AI capabilities are provided as an easy-to-use service from foundation model players.

Given the accelerating pace of innovation in the space, it is likely that by the time you are reading this, there will already have been major new advances on the application layer and throughout the generative AI stack. As adoption increases, safety concerns will shift to the forefront. We are closely following the evolution of generative AI. As with any new technology, there are many nuanced issues, opportunities, and dangers on which businesses and governments should be focused.

Efficient runtime infrastructure, a strong focus on user experience stemming from an understanding of user workflows, and domain-specific data are showing promise as sources of economic power.

In the end, usage may be the most enduring moat. As AI systems benefit from more use, we expect value to accrue on the application layer for companies who truly understand their users’ pain points and needs and build phenomenal product experiences around this.

If you are a founder in the space or exploring ideas in this space, we’d love to hear from you at ljaffe@insightpartners.com, mspiro@insightpartners.com or ahong@insightpartners.com.

Disclosure: Weights & Biases, Run:ai, Deci AI, Profluent, Hour One, AssemblyAI, Jasper, Lightricks, Mutiny, DigitalOwl, Writer, Uizard, Deepdub, Recorded Future, Swimm, Sourcegraph, Iterative Health, Espressive, SingleStore, Bardeen, SentinelOne, Honeycomb, Landing AI, ContractPodAi, and Cognigy are Insight portfolio companies.

Insight’s portfolio gets a taste of the sweet life at RSA with ScaleUp Suite, Sugarhill Gang, and Sugar Ray

This year, another kind of event took center stage at the 2023 RSA Conference: the Insight Partners’ ScaleUp Suite!

RSA is the biggest cybersecurity conference in the world. This annual event brings together leading cybersecurity companies, experts, and enthusiasts to share their latest innovations, network, and learn from each other.

Stepping up to support our portfolio

For cybersecurity companies, the RSA Conference is an opportunity to showcase products, services, and solutions to a global audience. It’s their best chance to engage with prospects, partners, and customers, share insights, and learn from industry peers. With the increasing number of cybersecurity threats, the conference also provides a platform to discuss emerging trends and technologies to counter such threats.

Join us: Unpacking RSA 2023

Steve Ward, MD at Insight Partners and Betsy Wille, Former CISO at Abbott host this dynamic webinar where they’ll delve into the hot topics and key takeaways from RSA 2023.

With more than 80 cybersecurity companies in the Insight portfolio, we wanted to take our support of them to the next level by providing them with a beautiful meeting space a short walk from the conference center. This space allowed them to meet with prospects, get work done, and take a break from the conference showroom floor. As a result, Insight’s portfolio companies were able to maximize their presence at RSA and benefit from the collective value Insight and our portfolio brings when we work together.

Insight’s portfolio companies were able to maximize their presence at RSA and benefit from the collective value Insight and our portfolio brings when we work together

Insight’s RSA by the numbers

  • 28 portfolio partners joined the ScaleUp Suite
  • 25 portfolio partners leveraged meeting space for executive meetings and elevated branding
  • 40+ portfolio companies had booths at RSA this year
  • 130+ meetings booked across four days
  • 70+ enterprise introductions facilitated
  • 8 enterprises who took 5+ portfolio company meetings
  • 80+ VIPs joined Cyber@Scale’s CISO panel and keynote from the former ambassador to Russia
  • 1,000+ people joined the party at the ScaleUp Club
  • 3 rap battle contestants duked it out with the Sugarhill Gang
  • 10 classic hits belted out by Sugar Ray
  • 8 portfolio companies have already verbally committed to partnership for 2024!

ScaleUp Suite: Comfortable, upscale meeting space for our portfolio partners

Insight Partners’ ScaleUp Suite provided a range of amenities, including meeting space, an outdoor lounge area soaked in the Californian sun, and a stocked snack bar. This activation created a relaxed and comfortable environment where portfolio companies could meet with potential clients and partners in a professional setting.

High-powered relationship building

Insight’s enterprise business development arm, Insight IGNITE, was also on site, meeting with our global network of CISOs, swapping insights on the industry, and actionable innovations they were seeing at the conference. This provided portfolio companies in the suite the chance to build relationships, generate leads, and grow their businesses through IGNITE.

C-suite insights and thought-provoking conversations

Insight also hosted two exciting events during the conference, which were a huge hit with the attendees.

The first event, co-hosted with Diligent, the leading modern governance provider, was a VIP CEO and CISO panel on Tuesday night featuring the CISO of Bank of America and the CTO of Raytheon. The panel delved into how they operate with their leadership team and their board for success, how they stay on top of risk management while also seeking out opportunity sets for their large organizations. The panel also shared a thought-provoking look into the cybersecurity industry’s challenges and opportunities as ScaleUps and enterprises continue to collaborate.

Following the panel, the audience was treated to an engaging talk by Michael McFaul, former ambassador to Russia. McFaul provided key insights into what is going on across the global geopolitical climate and how it impacts the cybersecurity sector. The evening ended with cocktails, where attendees had the opportunity to debate key topics discussed throughout the event.

A sugar-sweet end to RSA

The second event, held on Wednesday night, was the ScaleUp Club party, which attracted over 1,000 attendees. The party was the place to be, with attendees dancing to the tunes of Sugar Ray and the Sugarhill Gang and enjoying a night of great food, fantastic networking, and good old-fashioned conference fun.

Thank you to our RSA portfolio partners

Adaptive Shield, Bionic, Brinqa, Calamu, Dazz, Detectify, DNS Filter, DoControl, Island, Keyfactor, Pentera, Perimeter 81, PlexTrac, Prelude, Privado, Semperis, Slim.ai, Tailscale, Torq, Transmit, Veriti, Wiz, deepfactor, Reco

How the Wiz cofounders turned a longstanding friendship into a revolutionary cybersecurity company

The leaders of cloud security company Wiz were close friends long before they became cofounders.  

Ami Luttwak, Assaf Rappaport, Yinon Costiva, and Roy Reznik originally met as teenagers back in 2001 when they served together in the Israeli army. 

“I was 17 when we met,” says Luttwak. “It was 22 years ago. That’s part of our story. This is not the first startup we’re building together.”

“I think part of the magic of that relationship is how different we all are,” says Rappaport. “Each cofounder has a distinct personality, and it allows us to bring out the best in each other.”

They grew up together, came to understand the world together, and ultimately managed to evolve their strong and distinctive bond from friends to business partners.

Adallom acquisition and Microsoft years

It’s a bond that has lasted throughout a number of companies. In 2012, the foursome created Adallom, a cloud access security broker. Three rounds of funding led to $50 million of investment. Adallom wasn’t an overnight success story. The company gave customers visibility and control over access to their applications and data, but it took years for the market to develop, catching up to what the founding team had recognized as an opportunity.

The market did eventually catch up, and Microsoft bought the company in 2015. The foursome joined the tech giant to lead cloud security. There, they got a crash course in building for the enormous scale that Microsoft required 

“We came to Microsoft with the Adallom product,” says Luttwak. “Part of the transition was to remove maybe 75% of the features, but to make it scalable.” They integrated Adallom into Microsoft and ultimately deployed the product to 100 million users. 

“The transition to Microsoft taught us how to view startups in a different way,” says Luttwak. “It wasn’t a feature fight. It was: ‘How do I build a scalable product that, from an engineering perspective and also from a usability perspective, anyone can use? And how do I do that without adding complexity in deployment and usage because you’re aiming for huge scale.” 

Creating the “Switzerland of cloud security”

The Adallom team was Microsoft’s biggest cybersecurity acquisition at the time and a key part of building a new business within the company. Luttwak recalls the four years at Microsoft as being “amazing,” not just for Adallom and the cloud security product but for seeing how a huge organization can scale a completely new business.

“When we started, Microsoft didn’t have any security sales,” says Luttwak. “When we finished, it was around $1 billion a year.”

When we started, Microsoft didn’t have any security sales. When we finished, it was around $1 billion a year.

Still, the four cofounders got restless and saw an opportunity to build products that could function across many cloud platforms. “One of the things that spurred our departure was the prospect of providing a solution for cloud security teams that extended beyond Azure and truly supported multi-cloud environments,” says Rappaport. “You might say what we envisioned was the ‘Switzerland’ of cloud security.”

They wanted to go it alone once more. “We decided that it’s time to start something completely new, to try to disrupt an existing market,” says Luttwak. “And that’s when we decided it’s time to leave and really try our luck and create Wiz.”

Wiz is born

The decision to give up comfortable jobs in a large company like Microsoft is a tricky choice at any time, but the stakes felt even higher for the four longstanding friends. “When you start a company, it’s quite a frightening experience because no one likes to fail,” Luttwak says. “But of course, the second time, there are higher expectations.”

The founders knew that people would be comparing the trajectory of their new company to the success of Adallom – and anything other than enormous growth would be seen as a disappointment.

But they didn’t let high expectations dissuade them from their big idea.  

While at Microsoft, they had identified a hole in the cloud security market: Unlike with on-premise cybersecurity, there wasn’t a way for security teams to view all their cloud servers in a “single pane of glass.” 

“The market existed for 15 years,” says Luttwak. “Although you had multiple products, none of them actually solved the problem. That happens sometimes: you have products, you have a market, security teams buy the product. But they don’t actually solve the problem. And that’s where opportunity arises.”

You have products, you have a market, security teams buy the product. But they don’t actually solve the problem. And that’s where opportunity arises.

The team launched Wiz in January 2020 to rethink how cloud security teams could understand their infrastructure across multiple cloud vendors: “After talking with a lot of teams, we came to the realization that what’s broken is not a specific feature or functionality,” says Luttwak. “What is broken in cloud security is that the entire operating model of how you do security needs to change.” 

They convinced customers and financial backers that they could be that change. “We worked with our existing investors, and we got a very big investment from day one – around $20 million,” says Luttwak. The high investment was a double-edged sword. “That’s pretty scary,” says Luttwak. “You want to succeed. You believe in yourself. But it’s still a startup, you know. There’s a lot of expectations from you.”  

Building for scale

Wiz ultimately became one of the fastest growing security startups of all time, and the team attributes that, in part, to the cofounders’ dynamics and their experience building both startups and within Microsoft. “I feel this team now has multiple superpowers,” says Luttwak. “We had gone through a startup phase and the Microsoft phase.”

The group had built what Luttwak calls “the scalable startup.” It’s a different model from how the team operated at Adallom: usually, in a startup, the founders are focused on whatever the customer wants. “But in a scalable startup, you think like Microsoft: this is going to be used for 10x, 100x the load we have today because we’re going to succeed. So let’s build in a scalable manner.”

“An unbelievable journey”

The Series A funding from Insight Partners and others in December 2020 has allowed Wiz to scale in a way that has defied expectations. It was aided by many of the team being long-tenured colleagues of the founding team. Just as the four cofounders had sustained connections, a surprisingly large number of Wiz’s engineers are people the founding team encountered 15 years ago at Adallom.

That inherited institutional knowledge, alongside nailing the product-market fit, has helped Wiz to grow to what it is today following its launch out of stealth. “It’s been an unbelievable journey,” says Luttwak, “and a lot of it is because of our customers, right? Customers using the product is the best way to get more usage.”

Word of mouth, combined with the fact that cloud security isn’t an issue going away any time soon, has helped pave a path to continued success for Wiz, reckons Luttwak. The company hit a $100 million ARR within only 18 months of reaching $1 million, showing the scale of growth in the sector.

The company hit a $100 million ARR within only 18 months of reaching $1 million

“What’s beautiful in the problem we’re trying to solve for customers is that it just keeps growing,” he says. “The cloud gets bigger. There are more technologies in the cloud. The complexity of the cloud just grows.”

And Wiz plans to help companies navigate that complicated world. “We want,” says Luttwak, “to help companies get clarity in that complexity.”

The Investor POV

Teddie Wardi Managing Director, Insight Partners

How one call with the team behind Wiz changed Teddie Wardi’s mind on the company.

Tech investment is a connections-based business. Instinct can play as significant a role in whether a deal comes to fruition as careful research and diligence, as the story of Insight Partners’ investment in Israeli cybersecurity company Wiz shows.

“The ecosystem in Israel in startups overall, but especially in cybersecurity, is pretty tight-knit,” says Teddie Wardi, Insight Partners managing director. Insight had previously invested heavily in the Israeli cybersecurity sector. So it was only natural that Wardi would hear about what the founding team behind Wiz was cooking up.

“We kept hearing stuff about the team, but we had never met them,” says Wardi. When Wiz raised a seed round early in 2020, they bubbled back up to Wardi’s desk in late 2020, seeking Series A funding. At that point, Wardi decided to get in touch with the four cofounders behind Wiz.

“It was a short window of time getting to know them and partnering up,” he says. “It was almost like one phone call where we decided these guys were up to something pretty brilliant, and we needed to partner with them.”

“Timing matters a lot”

The hour-long phone call helped convince Wardi that he should support the startup at the start of its journey. “Investment decisions are always a composite of things,” he says.

One of them is understanding the context in which you’re making an investment. Insight Partners has a storied history of investing in cybersecurity companies. They knew the problems with cloud security and how startups could help solve those issues. “Wiz was there at exactly the right time – and timing matters a lot,” he says.

“Wiz was there at exactly the right time – and timing matters a lot”

The technology behind the company was also scrutinized. “They had this initial technological hook called agentless scanning,” says Wardi. “It allows them to plug into the customers’ environments in a very fast and cloud-native way, which means the product is quick to try out and deploy.”

The team and the tech

Then investors looked at the team – and Wiz’s cofounding team. They had together founded a prior company, Adallom, a security company that was acquired by Microsoft in 2015, where the founding team ended up working for a number of years. “They had success in the past selling their startup,” says Wardi. “They had done well in a big corporate environment and proven they can also achieve large scale things and work with large enterprises.” 

But even if a company is in the right place, at the right time, with the right product from the right founding team, making that choice to buy in can be tricky. So a phone call was still needed.

Wardi prides himself on approaching those conversations with a technological mindset. He focuses specifically on products over just people. “For us, even in early stage investing, it’s important that the team has built out a first iteration of the product,” says Wardi, “and there’s some evidence that they’re selling.” That evidence? Wiz entered the phone call with Insight after having secured two major multinational enterprise customers for its cloud security services.

The full-stack founder

However, there were still doubts in the back of Wardi’s mind. Many businesses come to pitch themselves to Insight, and the company’s experts have learned how to separate the wheat from the chaff. “Besides telling the story about why they started the company and what was their advantage, I poked [Wiz cofounder Assaf Rappaport] on the technological approach,” he says. “What has he built so far? What is he going to build in the next year?”

The goal was to understand whether the promise of the future was hot air or if the team could do what they said they would. Wardi was blown away by the level of detail and clarity of vision within the responses. “Assaf rolled with the punches very well,” he says. “He knew every detail, to the extent that a CEO wouldn’t even have to. It impressed us that he’s a full-stack founder. He knew the ins and outs of this thing. And that gave us confidence in him as a leader.”

“They broke the mold”

Wardi came out of the call far more confident in the idea of investing in Wiz than he entered it. Indeed, he almost didn’t take the call — the company was raising a very large round of funding for the stage they were at in their development, which made Wardi naturally skeptical of whether the company had been overhyped.

However, when Rappaport started getting into the nitty gritty details of how the technology actually works, how quickly they can deploy it within a customer, and why it was transformative compared to all the other solutions already out there on the market, he was convinced.

“It’s been a tremendous story from a growth perspective,” says Wardi. “They’ve accomplished much more than what they said they would and what we expected them to do. They broke the mold.”

The new generation of cloud providers: Why some programmers are moving away from megaclouds

In a world where AWS, GCP, and Azure are starting to look like Costco with too many aisles and value packs, developers want simpler options. In comes a new generation of cloud providers. 

Exciting times are ahead as startups like Vercel and Fly emerge as strong contenders against megacloud solutions. The key to their success isn’t serverless or edge features – it’s ease of use.

Resurgent demand for easy-to-deploy “PaaS”

Developers want a platform that’s fast, efficient, and doesn’t require a doctorate in cloud architecture. 

And that’s exactly what this new class of cloud providers deliver. By mission statement: 

  • Vercel: “Develop. Preview. Ship.” 
  • Fly.io:Our raison d’être is to deliver your applications to your users globally, with the highest possible availability and the lowest possible latency, with a great developer UX.”
  • Railway: “Bring your code, we’ll handle the rest.” 
  • Fermyon: “From blinking cursor to deployed app in less than two minutes.” 
  • Modal Labs: “Modal lets you run code in the cloud without having to think about infrastructure.” 

Edge is part of the picture, but not the main driver

While there’s certainly been a lot of chatter around edge and serverless capabilities, the truth is, these features will soon be table stakes and not the means for differentiation. Sure, developers want the ability to deploy once and run their apps anywhere in the world, and they’d rather not pay for idle hours.

But how will a new entrant execute Wasm modules faster than incumbents like Amazon (AWS) when they decide the technology is ready to replace lambdas? Or beat Cloudflare at running V8 isolates all around the world? Do developers even need edge when low-latency, multi-region availability can be spun up with a few lines of code?

The answer will more often than not be “no!”

Differentiation = community

When it comes to choosing a next-gen cloud, it’s all about the community. Vercel, Deno, and Bun are leading the way in the “Javascript cloud” category. Meanwhile, Fly has carved out a niche as the go-to provider for container-based applications.

Looking ahead, I see the Wasm clouds like Fermyon and wasmCloud, which will support multiple programming languages and help developers build cross-language applications, as the polyglot clouds.

In the end, the most successful next-gen clouds will be those that can build vibrant, engaged communities of developers around their platforms.

Stateful applications are on the way

Next-gen cloud providers are expanding their offerings to include databases and other services that enable developers to build and deploy stateful applications more easily. For example, Fly.io now supports SQLite, Fermyon supports KV-stores, and Vercel is forming partnerships with various databases.

We may see backend-as-a-service and serverless database offerings merge with next-gen clouds to achieve greater performance and profitability, namely by eliminating the megacloud egress chokehold.

Two paths to building an enduring next-gen cloud

As long as cloud providers continue to charge high markups on raw computing, there’s a limited opportunity to make money on developer experience alone. How will this new crop of companies build enduring businesses?

Option #1: Vertical integration

Vertical integration has long been a method used to combat commodity pressures. If next-gen clouds want to maintain sustainable margins in the long run, they can move down the value chain and control their own hardware. That’s Fly.io’s approach, where they rent or own all of their servers. It’s a model I believe others will adopt as they scale their businesses. 

Option #2: Value-add products

Some companies may choose to build higher-margin products on top of their deployment platforms as a way to increase their profit margins as they scale. Vercel is one example of a company that has done this by offering Turborepo, a JavaScript build system that can be used either as part of the Vercel platform (and immediately drive its usage) or as a standalone product that draws customers to the platform over time. 

Incumbents are both friends and foes

It’s unlikely that megaclouds will stop the progress of next-gen cloud providers, as many of them run on core compute offerings from AWS, GCP, and Azure. But they do have their eye on the CDNs, who also benefit from the increased utilization driven by next-gen cloud providers using their infrastructure.

It’s possible that major cloud providers like AWS and Azure will establish more locations in metropolitan areas, while CDNs are expected to expand their compute offerings and may acquire next-gen cloud providers to improve the developer experience.

In conclusion, it’s the ease of use that drives success, not just serverless or edge features. To ensure long-term viability, next-gen clouds must invest in their own hardware or build higher-margin products atop their deployment platforms. This year we’ll see next-gen clouds support stateful applications. Next year and beyond, incumbents will start to make combative moves. 

Editor’s note: Deno, Fermyon, and Tailscale are Insight portfolio companies.

Certain statements made throughout this post that are not historical facts may contain forward-looking statements. Any such forward-looking statements are based on assumptions that Insight believes to be reasonable, but are subject to a wide range of risks and uncertainties and, therefore, there can be no assurance that actual results may not differ from those expressed or implied by such forward-looking statements. Trends are not guaranteed to continue.