Speeding the J-Curve

History Rhyming

When the Grit Capital Partners’ GPs were building our companies, we used deterministic machine learning and agentic AI to create incredible customer and shareholder value. In mobile advertising, we used agents to make sense of unstructured data and negotiate bid spreads with astonishing speed. In martech, we used NLP to find effective messaging and ML to identify key customer types.

And we weren’t alone. Our competitors and companies in the broader industries, especially those that centered on marketplaces, adopted artificial intelligence. These early AI deployments understood behavior, analyzed massive datasets, and completed transactions, all with limited human input and interaction.

Using AI to augment specific human tasks, enhance efficiency, and drive engagement is not a new concept in digital experiences and transactions. Companies in media, martech, commerce, and fintech have developed and deployed AI for the better part of two decades, amassing some of the world’s largest and most organized datasets. So when we saw the capabilities of LLMs and generative AI, and we saw the potential of marrying left-brain, rules-based, deterministic AI with right-brain creative AI, we were primed to invest in the coming industry disruptions.

We think of this as accelerating the J-Curve.

Technology, Productivity and the J-Curve

‍One of the mysteries of modern economics is the “lost productivity” of technology deployments. Businesses have invested billions in hardware and software but haven’t seen timely proportional gains in output.

As Erik Brynjolfsson of Stanford tells it, General Purpose Technologies (GPTs) are burdened with a period of foregone output during intangible investment periods that go unaccounted, leading to an underestimation of productivity growth and subsequent overestimation when organizations make the requisite changes to generate measurable outputs.

Brynjolfsson argues that productivity mismeasurement due to AI investment and related intangibles could plausibly account for as much as 0.55% of “lost” output in 2017 GDP.

“We’re in this period right now where we’re making a lot of that painful transition, restructuring work, and there’s a lot of companies that are struggling with that,” Brynjolfsson says. “But we’re working through that, and these J-Curves will lead to higher productivity… we’re near the bottom and turning up,” Brynjolfsson said in early 2022.

Given the increase in AI adoption since 2022, it stands to reason that software companies and their investors in the venture market are experiencing J-Curve effects. This rings particularly true for those industries where AI has been a longstanding component, where we at Grit Capital Partners have operated and continue to invest.

Where We Focus

‍We believe the applied layer – AI applications and the related vertical AI tools that facilitate them – represents the most compelling opportunity available to private investors today. AI technologies are already delivering tangible value across the digitally native sectors of financial services, commerce, media, and marketing. These verticals have three enabling characteristics:

• Large, organized datasets, with massive opportunity to add unstructured datasets
• Business processes that have evolved to incorporate initial deployments of applied AI
• High human capital costs that stand to disproportionately benefit from the labor unit offset effect that will come from continued advancements in AI

In addition, applied AI is closer to the customer, which means immediate revenue opportunities.

For these reasons, we at Grit believe that these ML-mature sectors will be disrupted early, disproportionately, and repeatedly. Extraordinary founders who are unlocking value in the markets we are natives to are building the first wave of applied AI market leaders right now.

What We Avoid‍

Most of the AI hype today centers around silicon, foundational models, and AGI. We believe that it will be difficult for new entrants to compete with the larger players, and even the largest competitors here face commoditization risk. The speculative frenzy around these areas of AI has led to high capital flows into private general AI deals at valuations that fall well outside of our investment parameters, have high capital intensity, and a rather murky path to profitability. For investors who want exposure to the foundations of AI, there are many public market avenues.

AI Startups Growing Faster‍

The combination of customer demand, greater business value, and increased operating efficiency means that applied AI companies are growing users and achieving revenue milestones much earlier than startups in prior tech transitions.

In February 2023, a research report from UBS (cited by Reuters) noted that ChatGPT reached 100 million monthly active users in only two months. It took nine months for TikTok to gain the same number of monthly users. Instagram took 2.5 years.

Payment processor Stripe gathered data on annualized revenues for the 100 highest-grossing privately held AI companies using their payments platform as of July 2024, compared with a comparable cohort of promising SaaS start-ups as of July 2018.

The AI cohort took a median 11 months to hit $1m in annualized revenue after their first sales on Stripe, compared with 15 months for the previous generation of SaaS companies, the data showed. AI start-ups that have scaled to more than $30m in annual revenue achieved the milestone in 20 months — five times faster than past SaaS companies.

Accelerating user and revenue growth means that the time is ripe for applied AI now. Investors that wait much longer will find themselves missing the opportunity for step-function value creation.

Startups focused on those industries ripe for AI disruption — media, commerce, marketing, and fintech — leveraging AI for automation, personalization, process optimization, and decision-making will traverse their J-curves fastest. A shorter J-Curve means that companies can start generating positive returns sooner, reducing the length and depth of the early loss period of their investments for the benefit of their investors.

Specialist Seed Funds Will Win in Applied AI‍

For venture funds focused on seed-stage investing in Applied AI, selecting and winning deals in this environment will rely on a specific understanding of the tasks that AI will enable or disrupt. Specialists come from the industries that they invest in, delivering a competitive advantage in both winning deals and in the associated acceleration power they can bring to the next generation of companies. In addition, it will require significant operating expertise in those industries, napkin sketch-to-IPO experience, and deep-rooted sector knowledge. These are the superpowers required for both selecting and winning deals in this environment.

The Way Forward for Early Stage Investing in Applied AI‍

The pace of change in the AI field is dizzying. The frame through which opportunity is understood is evolving weekly, not yearly. The business value of an infrastructure platform or a toolset changes almost as quickly. The avenues for collaboration between humans and machines for a given task grow constantly.

Investing in this space requires experts who are in the mix every day. We believe that the best venture investors during this cycle will be sector-specific operators who are actively engaged with the technologies first-hand, and subsequently have the trust of the founders who are most tuned in to the opportunity.