Heartcore Capital - AI & Productivity Report 2023

Heartcore Capital

AI & Productivity Deep-Dive Why AI-enabled software is a catalyst for a new age of human productivity May 2023

About this report AI will reshape every software category and accelerate human productivity. The advancements in machine learning provide unprecedented ways to augment humans across all professions.

Reimagining work will be one of the biggest challenges and opportunities in the coming decades.

AI is undeniably one of the most pivotal technology shifts in recent history. The advent of large models has democratized AI capabilities, making them readily accessible for adoption by all technology companies. Unlike the transition from on-prem to cloud, current incumbents aren't disadvantaged to quickly adopt this new tech. In a game that rewards scale, nimble startups might not necessarily have the upper hand. Nonetheless, there are countless exciting opportunities to build application layer productivity solutions and propel humanity forward with AI-powered software.

In many Western and Asian countries, the looming demographic shifts present a tidal wave that's likely to accelerate the adoption of AI. As companies grapple with challenges to grow their workforce, they'll have no choice but to embrace software automation.

When it comes to AI, it feels like a lot has already been said in recent months, but sometimes a fresh perspective uncovers novel ideas. This report is primarily tailored to founders who want to navigate this technology shift, effectively integrate AI into their roadmap, and steer through the emerging competitive realities arising from deep learning and large models.

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1

Unveiling AI: Why The Hype & Why Now?

2

Productivity Redefined: AI’s Path To Value Creation

3

Innovation Landscape: Category Winners & Emerging Innovators

4

Shaping the Future: Research Breakthroughs & Expert Contributions

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1 Unveiling AI: Why The Hype & Why Now?

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The "iPhone moment" for AI: ChatGPT reached 1 million users within 5 days

AI is one of the most significant technology shifts we’ve seen in the last century.

1200000

5 days

74 days

10 months

2 years

7 years

1000000

800000

600000

400000

200000

0

Category 1

Category 2

Category 3

Category 4

Time from launch to 1M Users for selected technology applications

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Source: CNET, CNBC; Museum of American Speed

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Theislatest AI one of the most significant breakthroughs technology in AI build on shifts we’ve decades worth seen in the of research. last century.

1

AI Awakening Rule based AI

2

Knowledge-Based Era Learning from data

3

Rise of Big Data & Deep Learning

ChatGPT (2022)

Darthmouth conference (1956)

First Expert System giving rebirth to AI (1975)

ImageNet (2012)

GPT 3 (2020)

Alpha Go (2016)

Transformer paper (2017) 1950

1960

1970

1980

1990

2000

AI Winter Lack of funding

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4

New Paradigm of Large Language Models

Source: History of AI - Harvard.edu, Image-Net.org, Wikipedia 1, 2, 3, 4, *no significant drop in funding

2010

2020 AI Winter* Focus on Web3 and Covid

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340 310

Working population, Indexed (1950 = 100)

GDP growth will no longer come from an increase in population - AI will have to fill the productivity gap.

UN population development projections for selected countries (age 20-60, Indexed)

280 250 220 190 160 130 100 70 40 1950

1970

1990

2010

2030

China

France

Italy

2050 Denmark

2070

2090

US

Maintaining real GDP growth requires productivity gains per citizen in the workforce 6 Heartcore Capital – AI & Productivity Report

Source: United Nations

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AI will reshape every software category and accelerate human productivity.

Advancements in hardware are converging with AI architecture innovation. This is applied to lots of data.

Large AI models are a major breakthrough for software development.

Architecture Compute (FLOPs)

(Parameters)

Data

(Tokens)

Large Models: achieving good outputs for a wide set of complex tasks, despite training on unlabelled data

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Source: Image Source 1, Image Source 2, Image Source 3

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3958 4000 3500 3000 2500 A100

2000

1248

1500 1000 500

Better GPUs paved the way for AI innovation, such as LLMs. Once again, hardware advancements were the enabler of software innovation and value creation. This is not dissimilar to technology shifts of the past like on-prem to cloud, and Web to Mobile.

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H100

4500

Int 8 TOPS (Tera Operations per second)

Advancements in AI are made possible by exponential improvements in GPU performance.

Single-chip inference performance for NVIDIA GPUs. 1000x in ten years!

K20X

M40

P100

3.94

6.84

21.2

2012

2014

2016

V100

125

Q8000

261

0 2017

2018

2020

2023

NVIDIA state of the art GPU lines have shown exponential improvements in speed and efficiency with which they can generate output from a trained neural network model.

Source: Nvidia

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Transformer models are efficient for training on large data sets without human supervision

Transformer models are taking advantage of GPU compute.

Transformer-based models have shown to be very efficient in training on GPUs by parallelizing the ingestion of large amounts of data. Attention mechanisms allow the model to focus on specific parts of the input sequence while processing it, thereby improving its ability to understand and generate complex patterns.

"Looking at previous words only”

Luke,

I

am

your

worst mother

"Looking at all words at once"

Luke,

I

am

your

father worst death

Self Attention 10 Heartcore Capital – AI & Productivity Report

best

+

📚

www Pretraining heartcore.com

State of the art LLMs were only trained on a tiny fraction of human created text

Large models are trained on large data sets.

But they’re only scratching the surface.

Models like GPT-3 have been trained on terabytes of public text data. These data sets pale in comparison to other text-based content that’s been created by humans.

Future SOTA models will be trained on so far untapped non-public and unstructured data.

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In terabyte (TB) 1 Exabyte

10000000 9000000 8000000 7000000 6000000 5000000 4000000 3000000

Non-public Text data Emails/ Chats, PDFs, ERP

2000000

GPT-3

1000000

45TB

730TB

0 GPT3 training data

Youtube text data in videos

total text data on the Web (estimate)

Source: Report Internet Archive 2021, YT Estimations based on 500hr video content per hour, 288,600 characters/ hour, 1byte/character, total Youtube data amount: 10yrs as proxy for all content uploaded, assuming linear growth GPT3: 45tB unfiltered; 570GB filtered – Source Stefan Langer LMU

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Companies raced to train their models with more parameters.

We've seen an arms race to train larger and larger models with more parameters (neurons). Output quality and parameter amount seemed to scale in tandem until 2021. Nowadays, data architecture and training sequences are back in focus in an effort to address surging training costs.

Switch-C (1.6t)

Pangu-Σ (200B)

BaGuaLu (174t)

1t Gshard (600b)

Number of Parameters

Larger is better? Model size is not all that matters.

GPT-3 (175B)

100b

Pan-Gu (200B)

Megatron Turing NLG (530B) Gopher (280B)

GPT-4 (NA)

PaLM (540B)

Yuan 1.0 (246B)

OPT (175B) LaMDA (137B)

Jurassic-1 Jumbo (204B)

T-NLG (17b)

10b

MegatronLM (8.3b)

LLaMa (65B)*

Chinchilla (70B)

GPT-Neo X (20B) Bard (137B) GPT-j (6B)

GPT2 (1.5b)

1b

ELMo (94m)

BERT-Large (340m) Transformer ELMo (465m) GPT (110m)

MT-DNN (330m)

0.1b 2018

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XLM (665m)

RoBERTa (335m)

2019

Source: Adapted from StateofAI Report 2022, Nathan Benaiche

2020

2021

2022

2023

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We expect impactful LLM innovation to centre around autonomous agents. This will likely allow for another big step in productivity gains.

2

Computational Innovation

(larger models & few shot learners)

3

Interface Innovation (Reinforcement learning with human Feedback / ChatGPT)

4

Tool Innovation (retrival models/ agents/ multimodality)

TBD AI productivity impact

The next step for LLMs is operating outside of their training boundaries.

1

Architectual Innovation (Transformers)

GPT3.5/4

RNNs

-2017

“Embedding a lot of text”

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2020

“Teaching LLMs to perform more than auto-complete”

2022

“Guiding LLMs to give better answers”

2023-

“Helping LLMs to operate beyond their training set”

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In the Generative Web, everything will be programmatic.

Generative AI is leading towards hyper-personalization, allowing for new business models and value creation, but at the expense of traceability and governance. Increasingly content (from image, audio, and text to graphical interfaces) will only exist for a single human and a single moment.

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Generative Web Web 2.0 Read-only Web

Curated content for one user or demographic

Ephemeral content for one user

1 website for millions of visitors

News sites Corporate websites eCommerce

Recommendation engines Social-media feeds Programmatic ads

Momentary media Generative UI Personal Chatbots Open-ended video games

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Large models and finetuned derivative models will power the application layer Large models are commoditizing AI capabilities. Companies providing productivity solutions are now able to adopt a combination of proprietary and thirdparty AI solutions. To achieve industry specific value creation a proprietary approach to finetuning derivative models (L2) and orchestrating tooling is required.

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Application layer

Orchestration

L2 – Finetuned models

Operating System Software

L1 – Large models

Hardware layer

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The industrialization of AI-capabilities is an exciting opportunity to create value at the application layer.

Despite the justified hype around AI, entrepreneurs will have to be mindful that LLMs are redefining the nature of competitive moats.

2 Productivity Redefined: AI’s path to value creation

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Ø User interface

The race to become a platform has begun.

Many successful AI companies follow the same strategy - just from different ends of the stack. LLM providers are building vertical and horizontal application layer solutions. Application layer companies seek to establish their own finetuned and even foundational models, all in an attempt to build up platform moats and capture value.

Ø Use case specific models

Application layer

Ø Integrations Orchestration

L2 – Finetuned models

L1 – Large models

Hardware layer

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Ø Proprietary finetuned models & Ø Foundational models

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Training State of the Art (SOTA) large language models is expensive… ….and thus we are left with a game of the few: companies with access to Talent and Capital (Compute).

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The structural and sustaining scarcity of compute and talent will peg costs of scaling AI

Compute

Talent

There is a limited supply of enterprise GPUs: highly concentrated production (60% TSMC market share) & fabs require investments of +$10Bn

There are only around 10k AI researchers in the field

A100 GPU costs up to $15k >20k GPUs needed to train GPT3

OpenAI: $550M OPEX (2022) with ~500 employees

Source: Box1: Statista: Leading semiconductor foundries revenue share ww; Asionometry.com: Economics of TSMCs Giga Fabs // Box 2: Stanford University AI Index // Box 3: mybroadband.co.za // Box 4: Fortune.com , Linkedin

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As AI adds another COGS layer…it’s bye, bye SaaS margins.

Without increasing value and ACVs, AI-enabled companies will likely see lower margins vs "pure play" SaaS. Access to capital becomes a competitive moat, as well as actually solving hard problem sets where software solutions can be monetized.

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SaaS Company

COGS Compute, Storage, Payment

Gross Margin

AI-Enabled SaaS Company

COGS Compute, Storage, Payment

COGS (ML) Training, Inferencing

Gross Margin

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Running AI on the edge could push inferencing (and training) costs to the user.

Hosted AI Model

Local AI Model

Inferencing Cost

With the release of Web GPU for major browsers, even some large models can be run "on the edge" (e.g. Stable Diffusion and Meta's LlaMA). As hardware improves and models become more efficient, some of the inferencing and finetuning will be done on device and thus reducing cloud costs.

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The growing landscape of large language models (LLMs) includes multiple open source models

Large models will likely not monopolize, despite the barriers to scale.

Producing state-of-the-art AI innovation is a costly endeavour and could lead to a small amount of dominant players.

However, open source models have historically commoditized new AI capabilities in surprisingly short periods of time.

Open Source models in green

FLAN (137B)

Gopher (280B)

Chinchilla (70B)

GPT-4 (NA)

GPT-j (6B) GPT-3 (175B)

Megatron Turing NLG (530B)

Pan-Gu (200B)

June 2020 May 2021

Aug 2021

Jan 2022

Yuan 1.0 (246B) HyperCLOVA (204B)

GPT-Neo X (20B)

Mar 2022

LaMDA (280B) Ernie 3.0 Titan (260B)

GLM (130B)

OPT (175B)

May 2022

Jul 2022

Bloom (176B) PaLM (540B)

Galactica (120B)

Nov 2022

Bard (137B)

Feb 2023

LLaMa (65B)*

Jurassic-1 Jumbo (204B)

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Source: Adapted from StateofAI Report 2022; *Llama model was not intentionally made open-source (leak)

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Microsoft Teams vs. Slack

Competitive moats in AI-enabled software companies often stem from capital (team & compute), as well as from data. Both leverage better with a large and existing customer base.

75m 70

Daily Active Users (m)

60

50

Daily Active Users (m)

The distribution game does not favour new entrants to win.

40

30

20

12m

10

0 2014

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2015

Source: Business of Apps / Company Data

2016

2017

2018

2019

2020

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LLMs

… Powering Incumbent Software

Is AI a disruptive or strengthening innovation?

Probably both.

Incumbents can now relatively easily retrofit their applications with L1 and L2 models into their backends. Especially where a UI/frontend does not require radical changes, an incumbent player will be capable of capturing value through AI.

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Backed by Google

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If an incumbent will not have to meaningfully change their user interfaces to capture or create value through AI, their competitive advantage will be strengthened. New entrants should focus on radical UI shifts, as well as addressing new user groups.

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New User Group

Startups want to focus on AI applications where UI has to be different.

Existing User Group

AI Startup Opportunity Quadrant

Existing UI

New UI

Strong Incumbent Advantage

New Entrant Innovation Advantage

Incumbent Distribution Advantage

Blue Ocean For New Entrant

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25000

20000

15000

10000

5000

02-23

12-22

10-22

08-22

06-22

04-22

02-22

12-21

10-21

08-21

06-21

04-21

02-21

12-20

10-20

08-20

06-20

04-20

0 02-20

We can expect a tidal wave of new (and often incremental) software innovation on top of large models. Obvious problems and "easy to build" solutions will create red ocean markets.

New open-source repositories on 🤗 Hugging Face per month

12-1 9

Competition all around: barriers to entry are already low while the LLM & MLops stack is still maturing.

AI-enabled Customer Support Startups (some of them)

#New Repositories*

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Source: Huggingface.com; Heartcore Research

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A guide to identifying the best AI opportunities in times of hype

1 3 5

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Model improves continuously through data loops à Early mover advantage No capable incumbent owning UI à Distribution advantage Solving expensive problems that allow for early monetization à Funding advantage

2 4 6

Model is hard to train and/or implement à Competitive advantage Access to proprietary data à Model advantage AI is vital to improve/create value proposition à Technology advantage

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3 Shaping The Future: Research Breakthroughs & Expert Contributions We consulted the Heartcore team and eight external contributors (who are distinguished experts in the AI field) to select a groundbreaking research paper published in the last year and provide insight into why they chose it.

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Konstantine Buhler

Nathan Benaich

Partner at

General Partner at

Chosen research paper:

Chosen research paper:

Released in Apr 2021

Released in January 2023

Generative Agents: Interactive Simulacra of Human Behavior

Large Language Models Generate Functional Protein Sequences Across Diverse Families

Stanford University - Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein

Profluent, Salesforce - Ali Madani, Ben Krause, Eric Greene, Subu Subramanian, Benjamin Mohr, James Holton, Jose Luis Olmos Jr, Caiming Xiong, et al.

Why it’s important:

Why it’s important:

"In this paper, the team out of Stanford places several generative agents in a shared digital world somewhat similar to the game Sims. These agents, built on LLMs, interact with each other. The interactions are surprisingly realistic, including a coordinated Valentine's day party. If the AI revolution is a continuation of the personal computer revolution, as in a revolution of computation, prediction, and work, then this type of multi-agent interaction is reminiscent of the early days of PC-networking, which eventually led to the Internet."

"Madani et al. demonstrate how a language model architecture originally designed for code can be adapted to learn the language of proteins. Through large-scale training, they use a protein language model (ProGen) to create artificial protein sequences that encode functionality that is equivalent to or better to naturally occurring proteins. This means we can generate proteins (drugs or otherwise) with desired functions in a far more systematic way than ever before."

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Levin Bunz

Christian Jepsen

Partner at

Partner at

Chosen research paper:

Chosen research paper:

Released in October 2022

Released in Jan 2023

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

Mastering Diverse Domains through World Models Deepmind - Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap

OpenAI: Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune

Why it’s important:

Why it’s important:

"The research from OpenAI applies semi-supervised imitation learning for computer agents to learn to act by "watching" unlabeled video data. The model was pre-trained with 70k hours of online videos of people playing Minecraft, finetuned with a small amount of labeled data (video labeled with keypresses and mouse movements). The trained model was able to craft diamond tools with human-level performance.

Taking this further, complex and sequential tasks could be automated by simply "observing" humans doing the work, e.g., for data entry tasks within or across applications.”

"A research team from Deepmind show that a Reinforcementlearning-based general and scalable algorithm can master a wide range of domains with fixed hyperparameters. By interacting with the game, the model learned to obtain diamonds in the popular video game Minecraft despite sparse rewards, and without human data or domain-specific heuristics.

"Learning by doing" across different domains and sparce/delayed rewards is a trait of human intelligence and hence this research presents a potential path towards a "general" AI."

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Felix Becker

Pete Huang

Associate at

Author of

Chosen research paper:

Chosen research paper:

Released in Mar 2023

Released in Feb 2023

Alpaca: A Strong, Replicable Instruction-Following Model

LLaMA: Open and Efficient Foundation Language Models

Stanford University, Meta- Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, Tatsunori B. Hashimoto

Meta- Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, et al.

Why it’s important:

Why it’s important:

"Generative AI models are pivotal for productivity, especially when users can run them on their own hardware. Alpaca showed that a combination of an open-source foundational model and extracted instruction-output pairs can achieve similar performance to text-davinci-003. More importantly, this leap to democratization happened very cost-efficiently (<600 USD).

The paper initiated a discussion on how defensible even humanlabeled training data is. It foreshadowed a missing moat of big tech and hints at forthcoming possibilities of AI models stealing from each other."

"Meta's release of LLaMA was important for two reasons. First, it showed that training a smaller model for longer could yield really impressive results - the 13B model outperformed GPT-3, which has 175B parameters. Second, an entire ecosystem has bloomed around LLaMA. Alpaca and Vicuna for starters, but also the efforts to open source a LLaMA-equivalent with commercial licenses, running these models on your laptop and your phone, etc. A lot of progress in the large language model space from 2023 is thanks to Meta and its work with LLaMA."

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Sahar Mor

Nicole Büttner

AI Product Lead at & Editor of AI Tidbits

CEO at

Chosen research paper:

Chosen research paper:

Released in Mar 2023

Released in Apr 2023

Towards Expert-Level Medical Question Answering with Large Language Models (Med-PaLM 2)

Segment Anything

Deepmind - Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres , Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl, Heather Cole-Lewis, et al.

Meta - Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, et al.

Why it’s important:

Why it’s important:

"Singhal et al present a model designed to tackle the grand challenge of medical question answering with performance exceeding SOTA across multiple datasets. Med-PaLM 2 combines improvements in LLMs with medical domain fine-tuning and novel prompting strategies. The model scored up to 86.5% on the MedQA dataset, surpassing the previous SOTA by over 19%.

Combined with the recent progress in multimodal AI, which would allow AI models also to see and hear - we can imagine a world where individuals can access personalized, timely, and accurate medical advice conveniently, empowering them to make informed decisions about their health, improving healthcare access and outcomes for humans across the globe.”

"This paper is something like the GPT Moment for Computer Vision. The Segment Anything Model (SAM) trains on more than 1b segmentation masks on 11M images. The model's zero shot capabilities solve many of the computer vision tasks without training data through a prompt, which revolutionizes the field."

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Niko McCarty

Darian Shirazi

Author of

General Partner

Chosen research paper:

Chosen research paper:

Released in April 2023

Released in June 2022

Efficient Evolution of Human Antibodies from General Protein Language Models

ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Stanford University - Brian L. Hie, Varun R. Shanker, Duo Xu, Theodora U. J. Bruun, Payton A. Weidenbacher, Shaogeng Tang, Wesley Wu, John E. Pak

Microsoft - Zhewei Yao , Reza Yazdani Aminabadi, Minjia Zhang Xiaoxia Wu, Conglong Li, Yuxiong He

Why it’s important:

Why it’s important:

"Large language models can massively accelerate evolution experiments in the lab, including for clinically-relevant applications. This study used six language models, altogether, that were trained on protein sequences in the UniRef database. The model would suggest mutations - without knowing the target antigen - based on which substitutions have a higher evolutionary likelihood across the six models. The evolved antibodies had improved affinities comparable to those achieved by a state-of-the-art lab evolutionary system (which takes weeks to perform) suggesting that LLMs can massively accelerate clinical development times in some scenarios.”

"As large language models become even larger, there are significant memory and processing power limitations which increases latency and cost for many applications. This paper introduced a novel technique for post-training Quantization of LLMs by representing parameters in 8 bits rather than 32 bits with minimal accuracy loss and a >5x response time. In time, many products will leverage different optimized models for different use cases and Quantization is one of the major steps towards this reality. While a simple and elegant solution, ZeroQuant has led to a number of other optimization methods such as SmoothQuant and other methods."

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Max Niederhofer

Lars Maaløe Co-founder & CTO at Adj. Professor at DTU

Corti.ai

Chosen research paper: Released in Mar 2023

Are Emergent Abilities of Large Language Models a Mirage?

Ryan Schaeffer, Brando Miranda, Sanmi Koyejo

Partner at

Chosen research paper: Released in June 2022

Semantic Reconstruction of Continuous Language from Non-invasive Brain Recordings Jerry Tang, Amanda LeBel, Shailee Jain, Alexander G. Huth et al.

Why it’s important:

Why it’s important:

”With the impressive progress and the many use-cases of Large Language Models, it is important to learn what we can expect. As Yann LeCun stated: Auto-Regressive Large Language Models will always hallucinate and it is not fixable. Do they, however, have ‘emergent abilities’: “abilities that are not present in smaller-scale models but are present in large-scale models …”. The authors of this paper presents an intelligent study showing that previous belief that these models possess emergent abilities are wrong, and that it simply has to do with the evaluation metrics. Hence, completing a multiple-choice medical exam is not evidence that a model has emergent abilities.”

“In this paper, researchers developed a non-invasive decoder that can reconstruct continuous natural language from brain recordings. This allows for the interpretation of perceived speech, imagined speech, and even silent videos. Although cooperation from subjects is still needed, the paper makes us wonder how long this will prevail. Advanced techniques could have the potential to infringe on mental privacy. While fMRI is currently a key tool in this research, the rapid pace of technological advancement means that other methods may eventually supplant it.”

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4 Innovation Landscape: Category Winners & Emerging Innovators

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AI-Enabled Productivity for Creative Work Category Winners

Soonicorns & Challengers

Midjourney

Image Generation US

Image Generation UK $89M funding

Game asset generation US $6M funding

Ai powered Design tool DK $19M funding

Product image photo studio tool FR $19M funding

2D to 3D recorder US $28M funding

Audio/ Video editor US $100M funding

Content creation US $131M funding

Synthetic voice API UK >$15M funding

Synthetic video avatars UK $67M funding

VFX software for studios US $17M funding

Physics-based animations for games UK >$2M funding

Mobile first retouching tool IL $335M funding

AI first Video editor/ effects suite US $196M funding

Video Dubbing UK $33M funding

Interactive avatars IL $48M funding

Audio/ Video recording suite IL $47M funding

Generative Music platform US $20M funding

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AI-Enabled Productivity for Healthcare Category Winners

Soonicorns & Challengers

Revenue cycle automation platform $857M funding

Precision medicine/ personalized treatments $304M funding

Medical Contact Center Support US $82M funding

Synthetic healthcare data US $104M funding

AI workflow automation US $85M funding

Patient documentation US *$72M funding

Integrated workflow platform $105M funding

Alerting and Care coordination platform $292M funding

Intervention guidance for cardiologists FR $69M funding

Clinical notes and care management platform FR $21M funding

Patient documentation US $72M funding

Breast cancer screening DE $14M funding

Remote monitoring platform $233M funding

Data driven primary care provider $271M funding

Personalized cancer therapy US $90M funding

Radiology workflow platform US $63M funding

Medical coding US $61M funding

Simulated low scale clinical trials US $85M funding

37 Heartcore Capital – AI & Productivity Report – *Heartcore portfolio company

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AI-Enabled Productivity for Science Category Winners

Soonicorns & Challengers

Cradle Bio

AI-enabled drug discovery UK $292M funding/ public

Data-driven cancer treatment solutions US $1.3B funding

RNA drug discovery US $42M funding

AI-enabled drug discovery pipeline FR $32M funding

Antibody design platform US $99M funding

Protein design software CH $6M funding

Drug development platform (cancer) HK $402M funding

Oncological research tool US $300M funding

Genomics data platform UK $71M funding

Experiments and testing platform CA $95M funding

AI-first protein design US $9M funding

Pathological assistance tool DE $20M funding

Next-gen pharmaceutical company US $420M funding

Next-gen pharmaceutical company UK $375M funding/ public

Pathological diagnostics tool (cancer) US $220M funding

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AI-Enabled Productivity for Deskwork Category Winners

Soonicorns & Challengers

Universal text/ image engine US $11.3B funding

Contact center automation US $158M funding

Presentation design tool US $75M funding

Customer service tool CA $191M funding

Browser/ App automation US $415M funding

Customer contact automation UK $66M funding

Robotic process automation RO $2B+ funding/ public

Knowledge search software US $155M funding

Calendar and scheduling tool US $77M funding

Knowledge search software US $71M funding

Customer contact automation/ real estate US $32M funding

Time management software US $13M funding

Enterprise content generation US $65M funding

Programmatic content generator US $6M funding

Customer service tool US $92M funding

API-first content generator US $26M funding

Translation tool DE $100M funding

39 Heartcore Capital – AI & Productivity Report

heartcore.com

AI-Enabled Productivity for Finance & Legal Category Winners

AI-powered accounting software US $115M funding

AI-powered contract intelligence US $156M funding

40 Heartcore Capital – AI & Productivity Report

Soonicorns & Challengers

Legal research tool US $65M funding

Legal workflows/ Copilot US $26M funding

AI-powered accounting software FR $61M funding

Contract drafting tool BE $11M funding

AI-powered accounting automation US $33M funding

Billing for enterprise law firms US $56M funding

Loan portfolio optimization US $41M funding

ESG insights for investors US $80M funding

heartcore.com

AI-Enabled Productivity for Software Development Category Winners

Soonicorns & Challengers

Browser IDE US $202M funding

Coding Copilot US

Synthetic data for developers US $68M funding

AI first terminal US $23M funding

Code Copilot IL $32M funding

Transcription APIs US $63M funding

Enterprise AI platform US $135M funding

ML deployment platform US $132M funding

Code Review PT $29M funding

Automated React/ Flutter programming IN $10M

Data science platform US $90M funding

Code Debugging US NA

AI App builder UK 195M funding

ML Ops platform US $200M funding

ML Ops platform US $81M funding

Coding copilot US/ EU $28M funding

Self-programming scripts IN $0.5M

AI data science platform US $102M funding

41 Heartcore Capital – AI & Productivity Report

heartcore.com

AI-Enabled Productivity for Industrial Automation & Workflows Category Winners

Soonicorns & Challengers

Robotics platform US $222M funding

Robotic logistic automation US $196M funding

Frontline worker protection US $30M funding

Humanoid robots NO $37M funding

Autonomous yard truck management US $58M funding

Fleet inspection US $196M funding

General purpose robots DE $270M funding

General purpose low code robot arms DE $18M funding

Automated textile inspection PT $27M funding

Frontline worker protection 3PL autonomous fulfilment US US $102M funding $22M funding

Autonomous agriculture vehicles US $58M funding

Robot software DE $123M funding

Autonomous excavators US $112M funding

42 Heartcore Capital – AI & Productivity Report

Robot assistants DE $86M funding

3PL autonomous fulfilment US $115M funding

Robot software PL $31M funding

heartcore.com

AI-Tooling and Infrastructure Category Winners

Soonicorns & Challengers

Hugging Face

Foundational model US $11.3B funding

Model orchestration platform US $160M funding

Foundational model DE $29M funding

Foundational model FR NA

Semantic Search tool DE $16M funding

Foundational model IL $119M funding

AI first chip company UK $682M funding

Vector database US $138M funding

Foundational model US $415M funding

Annotation & Orchestration platform UK $43M funding

Open Source LLM Orchestration US $10M+ funding

AI Orchestration and workflow builder FR NA

Vector database DE $10M funding

Vector database NL $68M funding

Foundational model US $1.8B funding

43 Heartcore Capital – AI & Productivity Report

heartcore.com

Thank you!

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Authors:

Published with:

Levin Bunz, Partner at Heartcore

Christian Jepsen Partner at Heartcore

Felix Becker Associate at Heartcore

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