Emerging Technology Trends for 2023
Our early-stage venture capital investors seek to find and invest in emerging technologies well before they reach mainstream adoption. However, with the rapid pace of technological innovation, predicting what will be “mainstream” 10 years out is nearly impossible. For example, if we were conducting this exercise in 2006, before the launch of the Apple iPhone, few would have predicted that Apple would grow in just 10 years from generating $0 in revenue from mobile device sales to over $136 billion.1
Rather than try to predict what technology trends will dominate 10 years from 2023, we look back rigorously over the last year to identify key market trends and emerging customer pain points. We then attempt to extrapolate the first- and second-order implications of those trends to identify where we believe the next wave of potentially market-changing early-stage startups will develop. In short, we believe that technological innovation compounds on itself and that one must understand what happened in the past to attempt to predict what might occur in the future.
At the beginning of each year, we undertake this exercise. In the table below, we summarize our assessment of the key technology themes for 2023 across the artificial intelligence/machine learning (AI/ML), data and analytics, cybersecurity, and application infrastructure sectors. Then in the slides at the bottom of the article, we identified what we believed to be the top technology/market trends we observed throughout 2022 and our take on their potential implications.
Key Investment Themes
Theme | Description |
|---|---|
Data Governance 3.0 | The growing volume of analytical data across data warehouses and data lakes requires more automated means of cataloging, tracking (lineage), and profiling using metadata. |
Data Ownership & Monetization | As large language models (LLMs) require ever-increasing volumes of training data, many data producers, in the form of companies and individuals, are realizing they should have greater ownership rights over their data that is used to train those models, including the right to potentially monetize on its use. This will likely create broader marketplace opportunities to track, share, buy, and sell unique training data. |
Digital Identities & Credentials | Solutions that enable identity proofing, credentialing, and validation over time will be increasingly popular as passwords lose their value. |
Embedded AI/ML | Products designed to help implement AI/ML-powered features in traditional SaaS applications without requiring significant refactoring of legacy code. |
Git-Based Tooling | Git is the de facto distributed version control tool for developers/code but can be applied to additional distributed collaboration use cases (e.g., data, documents, etc.). |
Human-in-the-Loop & Fine-tuning Tools | Foundation models are powerful and provide out-of-the-box capabilities for generic/generalized tasks. However, these models require easier infrastructure/tooling to fine-tune for more domain-specific applications, which often involve human-in-the loop processes. |
Identity Detection & Response (IDR) | Identity security-related tools primarily focus on preventive controls (zero trust, just-in-time access, multifactor authentication). There is a need for additional IDR tooling that focuses on catching identity-related breaches across heterogeneous environments (e.g., cloud, internal/custom apps, third-party SaaS, email). |
Integration Observability | Applications increasingly incorporate third-party integrations to improve the end-user experience. There is a need for increased observability and insights around app-to-app exposure. |
Next-Gen Alert Management | Cybersecurity vulnerabilities are typically risk-rated based on the impact of a vulnerability rather than the likelihood that a vulnerability is exploited. Dynamic risk assessments of environments and risk-informed asset management can allow an enterprise to incorporate likelihood assessment into the prioritization of cybersecurity alert triage. |
Online / Real-Time AI | Online learning AI models enable personalized predictions in real-time without requiring prior training data on the individual (e.g., TikTok algorithm). Relatedly, there is also a need for improved infrastructure and tooling for continuous learning to ease the process for data scientists to collect, track, and auto-update/tune their models based on real-world feedback generated from the use of the model in a production environment. |
Proof of Security | Software buyers are seeking to limit their cybersecurity liability and exposure, resulting in increased scrutiny and validation of a third party or supplier’s cybersecurity hygiene. We will likely see more demand for products that can help reduce the asymmetric information gap related to a third party’s cybersecurity posture. |
Streaming Data Analytics | Within the streaming data infrastructure stack, mature tooling exists related to data movement (e.g., Apache Kafka, Kinesis, Pub/Sub); however, real-time querying and the serving/presentation layer for streaming data are still in the early stage of development compared to the traditional batch-based analytics stack. |
WebAssembly (WASM) | WASM is emerging as a popular runtime for both browser-based and server-side applications, due to its speed, multilingual support, and portability across various deployments/infrastructure. As WASM adoption grows, it will also require new security tooling to protect against cyber adversaries. |
1 https://www.globaldata.com/data-insights/technology–media-and-telecom/annual-sales-of-apples-iphone/
Disclosures:
The views expressed are the opinion of Sands Capital and are not intended as a forecast, a guarantee of future results, investment recommendations, or an offer to buy or sell any securities. The views expressed were current as of the date indicated and are subject to change. This material may contain forward-looking statements, which are subject to uncertainty and contingencies outside of Sands Capital’s control. Readers should not place undue reliance upon these forward-looking statements. All investments are subject to market risk, including the possible loss of principal. There is no guarantee that Sands Capital will meet its stated goals. Past performance is not indicative of future results. References to companies provided for illustrative purposes only. The specific securities portfolio holdings identified and described do not represent all of the securities purchased, sold, or recommended for advisory clients. There is no assurance that any securities discussed will remain in the portfolio or that securities sold have not been repurchased. You should not assume that any investment is or will be profitable.
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