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1、CONFIDENTIALTable of ContentsLAZARD VGB AI INFRA 40I.EXECUTIVE SUMMARY3II.LAZARD VGB AI INFRA 40 LIST4III.REPORT SCOPE AND METHODOLOGY6IV.KEY MARKET TRENDS6V.SEGMENTATION OF AI INFRASTRUCTURE14VI.APPENDIX I:COMPANY PROFILES21VII.APPENDIX II:REFERENCES42I.Executive SummaryLAZARD VGB AI INFRA 40We are

2、 delighted to introduce the Lazard VGB AI Infra 40 list which showcases a selection of some of the most exciting growth-stage AI infrastructure companies we have identified through hundreds of discussions with CEOs and investors.4 key market trends emerged through our research and company interviews

3、:This report looks at AI infrastructure as a space for potential investments at the growth stage.McKinsey1 estimates that while up to US$25.6 trillion p.a.in economic value could be added to the global economy by Generative Artificial Intelligence(GenAI)and its applications,the build-out of essentia

4、l AI infrastructure is fundamental to realizing this potential:“infrastructure is destiny”,as one OpenAI executive recently put it.2 The current phase of AI infrastructure growth is however being hampered by a series of stark shortages:of hardware(GPUs and other AI chips),of sustainable energy sourc

5、es,of high-quality training data and of talent.We believe that technologists from many companies including our VGB AI Infra 40 list are aiming to respond with innovative rethinking of hardware,software,data and tooling stacks,increasingly led by AI use cases,including the growing need for edge compu

6、te.Rethinking the Stack for the AI AgeThe optimal architectures of hardware,software and associated tooling appear to be shifting significantly for the AI Era and technical efficiency.Architectures will likely become increasingly driven by AI use-case(e.g.,specialized for edge compute)and flexible(a

7、gnostic to both underlying chips and foundational models).Focus on AI SustainabilityRapidly increasing energy requirements of AI training and inference is leading to a focus on architectural energy efficiency and is increasing the need for optimized energy and AI infrastructure integration.Looming D

8、ataConstraintsLimited supply of high-quality language data is leading to opportunities in alternative and synthetic data sources.The Dawn of Autonomous AI AgentsCo-pilot agents are just the start of harnessing the power of LLMs for agents.We see emerging opportunities in multi-agent and autonomous a

9、gent models as a possible key theme over the next 12 months.3II.Lazard VGB AI Infra 40 ListLAZARD VGB AI INFRA 40We are delighted to introduce the VGB AI Infra 40 list of selected companies arranged alphabetically by segment.Detailed profiles of the VGB AI Infra 40 companies are available in the App

10、endix.#CompaniesSegmentSub-segmentRaised to Date ($m)Last Valuation($m)HQLocation1Hardware/SiliconEdge/Embedded AI$130-Netherlands2Hardware/SiliconI/O/Networking$219$500US3Hardware/SiliconI/O/Networking$339$1,175US4Hardware/SiliconSpecialized workloads$124$244US5Hardware/SiliconI/O/Networking$127$22

11、8US6Hardware/SiliconInference Accelerator$161$385US7Hardware/SiliconI/O/Networking$175$295US8LightelligenceHardware/SiliconPhotonic Compute$232-US9Hardware/SiliconPhotonic Compute$421$1,200US10Hardware/SiliconHPC Supercompute$300-US11Hardware/SiliconEdge/Embedded AI$270$770US12Hardware/SiliconHPC Su

12、percompute$131-France13Hardware/SiliconInference Accelerator$154-Canada14Hyperscalers&Compute Compute-as-a-Service$892$1,520US15Hyperscalers&Compute Compute-as-a-Service$13$185UK16Hyperscalers&Compute Compute-as-a-Service$233$1,250US17Model Serving&InferenceOptimization/Acceleration&Hosting$260$1,01

13、4US18Model Serving&InferenceOptimization/Acceleration&Hosting$130$600US19Model Serving&InferenceOptimization/Acceleration&Hosting$133$850USSource:PitchBook Data,Inc.;Public Sources;Lazard VGB Insights4Lazard VGB AI Infra 40 ListLAZARD VGB AI INFRA 40#CompaniesSegmentSub-segmentRaised to Date ($m)Las

14、t Valuation($m)HQLocation20MLOpsAI/ML Platforms$90$374US21MLOpsMonitoring and Observability$106$360US22MLOpsModel Development Tools$77-US23MLOpsAI/ML Platforms$224$800US24MLOpsModel Development Tools$54$229US25MLOpsFeature Engineering$30$51US26MLOpsMonitoring and Observability$45-US27MLOpsSynthetic

15、Data$68$335US28MLOpsModel Security$56$200US29MLOpsData Labeling$30$125US30MLOpsModel Development Tools$31$52US31MLOpsAI/ML Platforms$62$290US32MLOpsData Preparation-US33MLOpsSynthetic Data$31-Austria34MLOpsModel Security$49$110US35MLOpsData Preparation$122$595US36MLOpsModel Development Tools$157-US3

16、7MLOpsData Labeling$138$1,000US38MLOpsMonitoring and Observability$128-US39Unstructured AIMLOpsData Preparation$68$223US40MLOpsModel Development Tool$265$1,250USSource:PitchBook Data,Inc.;Public Sources;Lazard VGB Insights5III.Report Scope&MethodologyLAZARD VGB AI INFRA 40Since publishing our work o

17、n AI commercialization and go-to-market strategies in mid-2023(see report here),the Lazard VGB Insights team has spoken to 100+CEOs of private AI companies,across the stack.This report focuses on our selected AI infrastructure growth-stage companies and a complementary report on enterprise-ready Hor

18、izontal and Vertical AI Applications will follow at a later date.Our screening methodology can be summarized as follows:We utilized extensive public information,industry reports and databases such as PitchBook to screen 2,000+companies in our core markets of North America and Europe which we then id

19、entified for detailed research and interview.We only included companies categorized as“AI-centric”,meaning AI functionality was assessed to be core to the companys native architecture or product offering.We excluded companies employing single-use features or enhancements.Companies considered by us t

20、o be incumbent or dominant players were not included,as well as foundational model builders,such as Anthropic or OpenAI,which we see as a maturing market segment.We limited our selection to companies with known valuations under US$1.5 billion.12346IV.Key Market TrendsIntroductionThe immense potentia

21、l of the AI Era has been catalyzed at an astonishing pace in the 18 months since the launch of ChatGPT.Yet it can be challenging for observers to read signal versus noise amidst the use of both superlatives and skepticism.Indeed,a recent survey3 indicated that experts are split on whether AI-related

22、 public stocks arein a bubble:40%said“Yes”,45%said“No”.It could be that both are correct.The investor Bill Janeway(who recently delivered a keynote at our VGB T500 Conference in London)argues that not all bubbles are alike,and that in“productive bubbles”,speculation attaches itself to transformation

23、al general-purpose technology but with the real potential to create new economies.4Boom or Bubble?“Artificial intelligence andgenerative AI may be the mostimportant technology of any lifetime.Marc Benioff,CEO Salesforce“A bubble within a bubbleis totallyunprecedented.The best guess is that thisAI bu

24、bblewill at least temporarily deflate.Jeremy Grantham,GMOTextbox Sources7LAZARD VGB AI INFRA 407However,the potential opportunity is too large to ignore.Generative AI has catalyzed a step-change and represents a reported5 US$1.3 trillion market opportunity by 2032,potentially quadrupling to as much

25、as 12%of total global technology spend.Including the impact of new use cases and productivity gains,McKinsey estimates GenAI could add upwards of US$25.6 trillion of economic value per annum to the global economy.6 While there are still notable challenges to adoption,we are seeing significant enterp

26、rise-pull for use cases which is“mostly top down,coming from CEO/C-suite or a steering committee”,according to 71%of recently surveyed executives.8 More than 80%of enterprises are expected to have used GenAI by 2026,up from less than 5%in 2023.9 Almost all the companies in our AI Infra 40 list have

27、paying marquee enterprise customers,typically on multi-year recurring contracts and deploying in production.We have,however,included a few pre-revenue companies by exception.As outlined in our previous report on AI commercialization,we note a continued trend for VC-backed AI companies to aim to reac

28、h large audiences through monetized multi-year strategic partnerships,often backed by equity investment.Recent examples of such partnerships since our previous report include those between Amazon/Anthropic,Snowflake/Mistral,and Google/HuggingFace.10 We note,however,that the Big Tech partnership appr

29、oach may have limited runway due to antitrust concerns.The US Justice Department and the FTC have reportedly agreed an approach to potential investigations into Nvidia,OpenAI and Microsoft(including the recent Inflection AI transaction).11While the US received the vast majority of VC investment with

30、 70%of the total in 202312,and 3x more than Europe13,we note emerging vibrant ecosystems in Canada(Toronto and Waterloo),UK and France.Although outside the detailed scope of this report,we note that moves towards“Sovereign AI”14 by some countries in the interests of cultural and linguistic preservat

31、ion,economic growth,talent development,and cybersecurity may materially impact the funding environment in coming years.For more on the Geopolitics of AI,see this October 2023 report from Lazard Geopolitical Advisory.15LAZARD VGB AI INFRA 408The energy and AI infrastructures to satisfy escalating pot

32、ential demand is being built out today for the AI Era of tomorrow.While Nvidia has captured a dominant market position in the making of specialized chips for running generative AI models in the first phase of the AI Era,we believe that the broader AI infrastructure sector,or“picks and shovels”,could

33、 represent a significant investment opportunity,estimated to be valued at US$309 billion by 2031.16 The second phase of AI evolution will likely involve a broad range of companies building specialized AI-related infrastructure,across software and hardware,AIOps(automating IT systems using ML and big

34、 data),MLOps(standardizing the process of deploying ML systems),data infrastructure and more.“US$1 trillion worth of current equipment in data centers would have to be replaced with AI chips.”JENSEN HUANG,NVIDIA1.Rethinking the Stack for the AI AgeThe AI infrastructure stack will be significantly di

35、fferent from historic datacenters,cloud,and software infrastructures due to the many unique characteristics and demands of AI workloads.Training currently accounts for the majority of computational requirements,but as market demand scales,efficient real-time inference and low latency may become equa

36、lly critical.While the transition from CPUs to AI chips including GPUs,ASICs and TPUs has significantly improved the efficiency of AI workloads,there is room for further optimization and innovation as well as rethinking hardware/software integration needs.The majority of GPUs are underutilized durin

37、g peak times.Improving effective GPU deployment efficiency is set to become a key issue in 2024 through 2025.17 A“staggering 74%of companies are dissatisfied with their current job scheduling tools and face resource allocation constraints regularly,while limited on-demand and self-serve access to GP

38、U compute inhibits productivity”.18Architecture innovations such as interconnect technologies and High Bandwidth Memory(HBM)have become key to the AI stack in order to optimize GPU usage:Ayar Labs optical I/O solution seeks to address data movement bottlenecks in AI systems,to result in higher bandw

39、idth and lower latency with greater power efficiency.“Were on the cusp of a new era in high performance computing as optical I/O becomes a must have building block for meeting the exponentially growing,data-intensive demands of emerging technologies like generative AI”.19 Meanwhile Celestial AIs pho

40、tonic fabric interconnect addresses the“Memory Wall”20 by aiming to enable bandwidth delivery directly to the point of compute within the chip.At the hardware/silicon level,there could also be significant opportunity for new and specialized players given market demand potential,the opportunity to di

41、versify supply bases given the dominance of Nvidias estimated 80%market share21 as well as potential future supply chain constraints.22 SiPearl seeks to provide a high-performance,low power microprocessor for high performance computing(HPC)and AI workloads which integrates Samsungs HBM solution,to i

42、mprove processing speed with reduced thermal resistance,rather than by simply adding more GPUs.23Edge acceleration will require a range of AI-ready and energy efficient solution sets,whether for automotive,defense or enterprise for all of whom we are already seeing noticeable adoption.Edge computing

43、 market size was estimated at US$16 billion in 2023 and is expected to grow at 37%CAGR to US$156 billion by 2030.24 Meanwhile,Gartner predicts that by the end of 2026,100%of enterprise PC purchases will be an AI PC,with an integrated Neural Processing Unit(NPU).25 SiMas embedded edge machine learnin

44、g system-on-chip(MLSoC)aims to allow customers to run entire applications on a chip,while Axeleras AI acceleration platform seeks to enable inference processing with YOLO(You Only Look Once,convolutional neural networks for real-time object detection)for edge AI computer vision applications.Most of

45、the emergent semiconductor/hardware category companies we spoke to are working on innovative technical approaches to workload management,recognizing that the computational requirements of data preparation,training and inference vary significantly.Integrated hardware/software solutions and use of com

46、putationally appropriate models will also vary significantly by use case.The use of industry or function specific GenAI models used by enterprise is expected to increase from approximately 1%in 2023 to 50%by 2027.26LAZARD VGB AI INFRA 40“The bottleneck to many companies growth quickly became not cus

47、tomer demand but access to the latest GPUs fron Nvidia.”SEQUOIA9“We are in the midst of a massive technological shiftinnovation within this emerging AI infrastructure stack is progressing at an unprecedented pace.”BESSEMER VENTURE PARTNERS Cornelis Networks delivers end-to-end high-performance inter

48、connect solutions with a proprietary scale-out architecture,incorporating telemetry-based adaptive routing,congestion control with low latency and enhanced support for messaging,memory models and AI optimization in large-scale hyperscaler,cloud AI and on-prem AI/HPC environments.2.Focus on AI Sustai

49、nabilityLAZARD VGB AI INFRA 40The rapid growth in generative AI has sparked concerns about intensity of energy use.The International Energy Agency estimates that by 2026,datacenters could globally consume more than 1,000 terawatt-hours of electricity,more than double that of 2022 and roughly equal t

50、o Japans total energy usage.27 As energy consumption shifts from training towards inference as modern models are deployed at scale,energy efficiency throughout the model life cycle is increasingly under scrutiny.28 We identify two potential emerging trends resulting:Energy and AI infrastructure inte

51、gration,and innovation in energy-efficient hardware and models.Interdependence of energy supply chains and datacenter infrastructure is seeing“The Magnificent 7”(Mag7)29 integrating with energy infrastructure,increasingly co-locating datacenters with sustainable energy sources.Yet it is not clear th

52、at sufficient clean energy resources can meet demand.Microsoft and OpenAI are reportedly planning a massive US$100 billion,5GW“Stargate”AI datacenter,potentially powered by alternative energy sources including nuclear,at an unspecified location.30 While Microsoft signed an agreement with nuclear pow

53、er producer Constellation Energy in 2023,31 the analysis reports that this would not provide the scale of power required,and few existing global nuclear facilities could.32 Microsoft also recently announced it is backing an estimated US$10 billion in renewable energy projects in a partnership with B

54、rookfield AM.33 Meanwhile in March 2024,Amazon acquired Talen Energys datacenter campus at a nuclear power plant in Pennsylvania for US$650 million.34The capital intensity of the AI Era is reflected in the Mag7s burgeoning Capex and R&D spend of US$374 billion in 2023.Amazon,Meta,Alphabet and Micros

55、oft alone have already pledged to spend a combined US$200 billion on Capex in 2024,mostly on AI infrastructure,up 35%on 2023 figures.This represents as much as 21%of the total capex of the entire S&P500,up from 4%a decade ago.35 Some analysts are beginning to question however how and when this“AI Ar

56、ms Race”will generate commensurate returns.36 Given the billions of dollars that Big Tech companies have been pouring into the AI boom,investors are cautious that this may ultimately result in infrastructure overbuild minus the promised future profits,according to one.37 There is a question about th

57、e sustainability of such capital and energy intensive AI datacenters and potentially a complementary need for innovation solutions to address the issue.10LAZARD VGB AI INFRA 40While many hyperscalers have long claimed to be 100%renewable or carbon neutral,there are broader issues surrounding the dis

58、placement of renewable energy supplies from alternative uses such as EVs,which could lead to increasing scrutiny of the need for intensive AI energy use.38 We believe that innovations in chip technology,model architecture,orchestration,scheduling and energy efficient acceleration will likely play an

59、 important role and may present investment opportunities in the context of sustainability.Sustainability of energy resourcing is expected by analysts to result in 30%of GenAI implementations using energy-conserving computational methods by 2028.39 We see the increasing use of more energy efficient S

60、mall Language Models such as Microsoft Phi3 as a trend for 2024,a trend which may present positive opportunities for AI developer platforms such as Anaconda,which supports the creation and flexible deployment of efficient models and a high-performance version of Python that maximizes efficiency of A

61、I workloads,as well as Weights&Biases.40 Nvidias acquisition of Run.ai could be indicative of the need for dynamic resource allocation and workload orchestration to minimize idle resource and optimize energy efficiency.Similarly,Anyscale aims to enable dynamic scaling,advanced scheduling and resourc

62、e management tools as well as reduced data movement to improve energy efficiency across AI workloads.Octo AIs strategy is to optimize models for energy efficient deployment on edge devices through advanced model compression and optimization of specific target edge devices.Cerebras wafer-scale chips

63、and software stack seek to enable faster,more efficient processing of large AI models,reducing energy consumption.It is also designed to be air-cooled,addressing certain sustainability issues surrounding water-cooling.41 Lightmatters photonic AI accelerators and Untether AIs approach to at-memory co

64、mputation are also designed to be air-cooled.3.Looming Data Constraints,Synthetic and Alternative DataAccording to research from EpochAI,models are using increasing quantities of training datasets,growing at a rate of 2.8x per annum.There are limited resources of public human-generated text,with res

65、earchers estimating that high-quality data stocks for model training may become fully exploited by 2026-2032.42 This timing is uncertain as on the one hand,there may be advances in data efficiency,yet overtraining(using more data over reduced parameters with a view to optimizing compute for inferenc

66、e)could also exploit available public language data stocks even sooner.11LAZARD VGB AI INFRA 4020102012201420162018202020222024GPT-4GPT-3Publication DateBERT(large)Training Dataset Size(Words)Log Scale10510710910111013Source:EpochAI,Lazard VGB InsightsThere are a number of potential mitigating strat

67、egies and technologies to this bottleneck,including:speech recognition systems(e.g.,OpenAI Whisper)enabling use of abundant audio data for training,Optical Character Recognition(OCR)enabling visual transformer models for paper academic documents(e.g.,Metas Nougat),as well as the generation and use o

68、f synthetic data.By 2025,it is estimated that 75%of businesses will use generative AI to create synthetic customer data,up from less than 5%in 2023.43 We believe there could be a possibility of medium-term constraints on ML models transitioning from compute to data and synthetic data providers inclu

69、ding those such as Gretel and Mostly.ai,as well as multi-modal data ingestion like Unstructured.io may present possible opportunities.Our view is that there might also be a continuation of the trend for alternative data strategic partnerships such as those recently announced,including between both G

70、oogle and OpenAI and Reddit and Stack Overflow.44The scramble for training and fine-tuning data has begun to extend to proprietary data sources.OpenAI announced a data licensing deal with News Corp(Wall Street Journal,New York Post,Barrons,The Times UK)in May 2024,“worth US$250 million in the next f

71、ive years”,alongside existing agreements with The Associated Press,Financial Times,and Axel Springer(Politico).45 While there are further unused private data sources that might yet be exploited for training or fine-tuning,these are currently being hampered by privacy concerns and threat vectors.Acco

72、rdingly,there may be opportunities for model and data security solutions such as Hidden Layer and Protect AI.46124.The Dawn of Autonomous AI Agents LAZARD VGB AI INFRA 40While GenAI has delivered significant opportunity over the last 18 months,we believe that the trifecta of transformers,internet-sc

73、ale data and human feedback could be just the start of what is possible.While autonomous agents in the form of coding companions such as co-pilots have already started to emerge,multi-agent systems(MAS)for cooperative GenAI applications may also potentially transform the opportunity space.AI MAS are

74、 complex systems of multiple interacting intelligent agents,which learn from and collaborate with each other.Modern AI(GPT,BERT)47 may enable new opportunities for MAS,which have previously been constrained by complexity and coordination limitations.The market for autonomous agents is forecast to gr

75、ow from US$5 billion in 2023 to US$29 billion by 2028 at a CAGR of 43%.48 Established solutions such as Microsoft 365 Copilot and Githubs Copilot are already widely used by developers,albeit with some accuracy and security risks.49 Microsoft has further developed AutoGen as a framework to enable gen

76、eration of multi-agent systems based on high-level specifications.While still a relatively immature space for growth-stage investors,we believe that the use of multi-agent systems in AI infrastructure and the embedding of autonomous agents in applications could develop and become one of the importan

77、t trends to watch over the next 12 months.Cognition Labs,the creator of autonomous coding agent Devin emerged from stealth only 6 months after formation raising US$175 million in April 2024 led by Founders Fund at a reported US$2 billion valuation.50 The Open-Source project LangGraph seeks to facili

78、tate efficient and complex information flows between multi-agent systems enabling them to collaborate and make decisions based on shared knowledge.The developer of LangGraph,LangChain recently(February 2024)announced a US$25 million Series A from Sequoia,Benchmark,and others.51 Platforms for buildin

79、g and deploying AI applications using complex multi-agent approaches such as Abacus.ai could enable the creation of agents by chaining together user code,data transforms,ML models and LLM prompts which can access both LLMs and abacus APIs in one place.Agent monitoring-as-a-service will likely be nec

80、essary to develop multi-agent“artificial immune systems”with observability tooling including Fiddler and AccelData potentially seeing positive opportunities from the agent and trend towards MAS.13V.Segmentation of AI InfrastructureLAZARD VGB AI INFRA 40There are many ways to segment the AI infrastru

81、cture market,and our approach is designed to provide an overall market view while recognizing the distinct features of each sub-segment.This market map provides an overview of some companies we have identified as incumbent players and some selected emergent players,with our selected VGB AI Infra 40

82、companies highlighted in dotted boxes.Companies have been categorized by us based on what we have identified as their specific AI use case,using category abstractions.Certain companies,however,do not fit neatly into any single box but may have capabilities across multiple categories.14Segmentation o

83、f AI Infrastructure(contd)LAZARD VGB AI INFRA 40Source:Lazard VGB InsightsModel Serving and InferenceFoundational ModelsData InfrastructureHyperscalers and ComputeHardware/SiliconMLOPSOrchestrationExplainability,Monitoring,Observability&GovernanceLLM OpsSynthetic DataModel Development ToolsFeature E

84、ngineeringModel SecurityData PrepData LabelingAI/ML PlatformsOptimization/Acceleration&HostingModel Repository&HostingData Lake/WarehouseVector DBData StreamingData ManagementHyperscalersCompute-as-a-ServiceI/O/NetworkingSpecialized WorkloadsEdge/Embedded AIPhotonic ComputeInference AcceleratorHPC S

85、upercompute15Segmentation of AI Infrastructure(contd)LAZARD VGB AI INFRA 40The following section provides our outline of what we perceive to be the features of each AI Infrastructure subsector as well as identifying some incumbents and some selected growth stage companies identified during our resea

86、rch.Hardware/SiliconSub-SegmentDescriptionPrimary End MarketSelected Players IdentifiedSpecialized WorkloadsDesigners of custom compute chips tailored to accelerate specific applications.Unlike general-purpose processors which handle a wide range of tasks,these chips are optimized for specialized wo

87、rkloadshealthcare,banking,fintech,pharmaHPC SupercomputeDesigners of datacenter processors capable of handling high performance and/or high parallelism vs.traditional server CPUsclimate,security,energy,academia,healthcare,industrialInference AcceleratorsChip developers focused on accelerating and op

88、timizing efficiency for inference workloads for AI/MLhyperscalers,LLM developersIO/NetworkingNetworking chip designers focused on reducing networking costs,latency,and power consumption often for AI and HPC workloadsdata center ecosystemEdge/Embedded AIDevelopers of chips for compute at the edge and

89、/or in applications with requirements for low power,high efficiency,and small footprintsurveillance,aerospace,manufacturing,automobile,wearable devicesPhotonic ComputeDesigners of chips that leverage light waves instead of electricity to handle computing,data storage,or communication which can be mo

90、re powerful than traditional circuits as photons have higher bandwidth and are not affected by electromagnetic interferencecloud service providers,semiconductor companies,enterprises16Source:PitchBook Data,Inc.;Public Sources;Lazard VGB InsightsSegmentation of AI Infrastructure(contd)LAZARD VGB AI I

91、NFRA 40Hyperscalers and ComputeSource:PitchBook Data,Inc.;Public Sources;Lazard VGB InsightsSub-SegmentDescriptionPrimary End MarketSelected Players IdentifiedHyperscalersLarge tech companies that provide extensive and scalable cloud computing servicesEnterpriseCompute-as-a-ServiceThird-party cloud

92、service providers for users who require high-performance computing power.They run a cloud computing model that provides customers with a platform to develop,run,and manage applications without the complexity of building and maintaining the infrastructureEnterprise,MLOps developersData Infrastructure

93、Sub-SegmentDescriptionPrimary End MarketSelected Players IdentifiedData Lake/WarehouseLarge-scale,centralized storage repositories that hold structured data or raw data in its native format until it is needed for analysisEnterprise,MLOps developersVector DBSpecialized type of database designed to ef

94、ficiently store,manage,and query high-dimensional vector dataEnterprise,MLOps developersData ManagementCentralized management systems for data and metadata leveraged by ML models that streamline the deployment,scaling,and monitoring of these models in production environmentEnterprise,MLOps developer

95、sData StreamingPlatforms/frameworks that enable AI and ML models to operate on live data,facilitating real-time analytics,decision-making,and automated responsesEnterprise,MLOps developers17Segmentation of AI Infrastructure(contd)LAZARD VGB AI INFRA 40Model Serving and InferenceSub-SegmentDescriptio

96、nPrimary End MarketSelected Players IdentifiedOptimizationPlatforms focused on enhancing the performance,scalability,and efficiency of applications,particularly those involving complex computations and large-scale data processingEnterprise,MLOps developersCompute-as-a-ServiceCentralized storage and

97、management systems for ML models that streamline the deployment,scaling,and monitoring of these models in productions environmentEnterprise,MLOps developersFoundational ModelsSub-SegmentDescriptionPrimary End MarketSelected Players IdentifiedFoundational ModelsFoundational models seeking to compete

98、with Chat GPT,Metas Llama,and Microsofts CopilotEnterprise,MLOps developersMLOpsSub-SegmentDescriptionPrimary End MarketSelected Players IdentifiedMLOpsIncludes technologies focused on the ingestion,cleansing,and transformation of data,to the training and optimization of models,and ultimately to the

99、 deployment and monitoring of the completed modelsEnterprise,MLOps developers18Source:PitchBook Data,Inc.;Public Sources;Lazard VGB InsightsMarket Map Company MaturityLAZARD VGB AI INFRA 40We looked at the maturity stages of companies across our AI infrastructure market map and from our analysis hav

100、e observed the following:Significant capital seems to be being deployed to certain disruptors in the Hardware/Silicon and MLOps sector,possibly indicating that these segments are beginning to mature.The market landscape appears highly fragmented across all segments,in our view with no clearcut winne

101、rs in each category,likely encouraging enterprises to adopt one of three strategies:embrace end-to-end solutions,construct their own bespoke systems,or select the best of breed companies.We might see category leaders continuing to attract through-the-cycle funding,and some mid-tier players may poten

102、tially be:i.forced into defensive mergers,ii.acquired by industry leaders,oriii.acquired by strategic investors Companies including Cerebras,Scale and Grafana that have raised US$500 million,may possibly be looking towards the public markets within the next 12-18 months depending on IPO market senti

103、ment.Source:PitchBook Data,Inc.;Lazard VGB Insights123TogetherBasetenCloudalizeAnyscaleOctoAIModularAcceldataNeural MagicModal Labs0150300450600$750Data InfrastructureHyperscalers/ComputeMLOpsHardware/SiliconModel Serving and InferenceTotal Raised($m)19Scale AIGrafana Labs VectraCoroWeights&BiasesDo

104、mino Data LabLabelboxTectonRescaleSnorkelUnravelRelationalAIAbacus.AIUnstructuredGretelLightning AIArizeEdge ImpulseProtect AIFiddlerRobust IntelligenceCalypsoAIKili TechnologyMOSTLY.AILatent AIFeatureBasePredibaseHiddenLayerWEKADataStaxCerebrasLightmatterGroqSiFiveCelestial AITenstorrentSiMa.aiAchr

105、onix Data AccelerationLightelligenceKneronAyar LabsEnfabricad-MatrixUntether AISiPearlCornelis NetworksCornamiSyntiantRain AIAxelera AIKinaraExpederaEnCharge AIAxiadoSalience LabsTogetherBasetenDeGirumCloudalizeAnyscaleOctoAIModularNeural MagicModal LabsAcceldata(25%)0%25%50%75%100%0100200300400500E

106、mployee Growth RateData InfrastructureHardware/SiliconGrowth Trajectories across AI Infrastructure SegmentsLAZARD VGB AI INFRA 40Relying on the aforementioned market segmentation map and companies which we have selected,we used employee growth figures since 2021 relative to total funds raised as a h

107、ypothetical proxy for overall growth.Based entirely on that measure of growth and applied to our selected landscape of companies,our analysis revealed that:Many larger companies are continuing to experience rapid expansion(50%+)even as they continue to scale.A concentrated group of companies seem to

108、 be experiencing rapid growth,particularly at the relatively early stages of funding(highlighted in the grey section of the chart below).2012AnacondaHyperscalers/ComputeMLOpsModel Serving and InferenceTotal Amount Raised$mSource:PitchBook Data,Inc.;Lazard VGB InsightsLAZARD VGB AI INFRA 40Axelera AI

109、 is a developer of a hardware and software platform for AI,designed to deliver exceptional performance within a power envelope of just a few watts while maintaining the flexibility to support multiple networksThe platform combines a custom dataflow architecture with multicore in-memory computing.Thi

110、s enables clients to minimize power consumption and deliver edge applications for a sustainable future,promoting both efficiency and environmental responsibilityHardware/SiliconSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|edge/embedded AIEindhoven

111、,Netherlands|www.axelera.aiFounded2021Total Raised$130mEmployees180Last financingRaised$68m in a Series B roundSelected Investor(s)Platinum CapitalSamsung Catalyst FundThe company develops electronic-photonic chipsets designed for applications demanding high bandwidth,low latency,and power-efficient

112、 short-reach interconnectsUtilizing industry-standard,cost-effective silicon processing techniques,the company creates optical-based interconnect chipsets and lasers to replace traditional electrical-based input-output systems.This technology allows companies to manage large volumes of data more eff

113、ectively by miniaturizing fiber optic transceiversHardware/SiliconAI Use Case|I/O/networkingSan Jose,California|Founded2015Total Raised$219mEmployees154Last financingRaised$25m in a Series C1 roundSelected Investor(s)Alumni VenturesHPIAG Capital PartnersNVIDIA21VI.Lazard VGB AI Infra 40 ProfilesLAZA

114、RD VGB AI INFRA 40Celestial AI is a developer of an innovative data center and AI computing platform that aims to cater to deep learning and machine learning applicationsThe companys technology combines the benefits of photonics,mixed-signal ASICs,and packaging to provide a substantial enhancement i

115、n computing performance.This enables clients to offer AI acceleration hardware and software alternatives,fostering advanced solutions for diverse applications.Hardware/SiliconSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|I/O/networkingSanta Clara,C

116、alifornia|www.celestial.aiFounded2020Total Raised$339mEmployees99Last financingRaised$175m in a Series C roundSelected Investor(s)AMD VenturesIAG PartnersKoch Disruptive TechnologiesTemasekCornami is a developer of reconfigurable computational fabric technology that aims to treat processor cores as

117、scalable resources in the same way as memory,storage,and transistorsThe companys technology delivers scalability from thousands of cores on a single chip to millions across a system for real-time computing at the lowest cost,power,and latency available.It is focused on delivering real-time fully hom

118、omorphic encryption at market prices.This enables developers,large enterprises,and edge computing to deliver high performance anywhere and on any device with the lowest power consumption and latency,enhancing efficiency and productivityHardware/SiliconAI Use Case|specialized workloadsDallas,Texas|Fo

119、unded2011Total Raised$124mEmployees49Last financingRaised$13m in SAFE notesSelected Investor(s)Raptor GroupSoftbank22LAZARD VGB AI INFRA 40Cornelis Networks is a developer of purpose-built high-performance fabrics,designed for leading scientific,commercial,and government organizationsThe company off

120、ers high-performance fabrics that expedite computing,data analytics,and artificial intelligence workloads.This enables customers to effectively concentrate the computational power of multiple processing devices on a single problem,thereby enhancing both the result and accuracy simultaneouslyHardware

121、/SiliconSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|I/O/networkingWayne,Pennsylvania|Founded2020Total Raised$127mEmployees177Last financingRaised$25m in a Series B4 roundSelected Investor(s)Alumni VenturesIAG CapitalIntel Capitad-Matrix is a deve

122、loper of a computing platform tailored for GenAI and LLMS by offering an innovative in-memory computing technique for data centersThe platform concentrates on addressing the physics of memory-compute integration using mixed-signal and digital signal processing techniques.This enables clients to bene

123、fit from enhanced computing efficiency,fostering improved performance and productivityHardware/SiliconAI Use Case|inference acceleratorSanta Clara,California|www.d-matrix.aiFounded2019Total Raised$161mEmployees177Last financingRaised$110m in a Series B roundSelected Investor(s)A&E InvestmentsM12Micr

124、osoftTemasek23LAZARD VGB AI INFRA 40Enfabrica,a developer of high-performance,ultra-scalable infrastructure silicon and software,aims to address the critical interconnect bottlenecks in next-generation computing workloadsThe companys foundational fabric technologies and products are designed to enab

125、le groundbreaking system efficiencies,topologies,and performance across various sectors,including hyper-scale cloud,edge,enterprise,fifth-generation/sixth-generation,and automotive infrastructure.Hardware/SiliconSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI

126、 Use Case|I/O/networkingMountain View,California|Founded2019Total Raised$175mEmployees111Last financingRaised$125m in a Series B roundSelected Investor(s)Alumni VenturesIAG PartnersNVIDIAValor Equity PartnersLightelligence is an operator of an optical computing platform that aims to accelerate infor

127、mation processingThe platform employs artificial intelligence and cutting-edge technology to transmit data via photons,enabling users to convey information with considerably lower latency and higher throughput compared to conventional electronic circuitsHardware/SiliconAI Use Case|phonic computeBost

128、on,Massachusetts|www.lightelligence.aiFounded2017Total Raised$232mEmployees200Last financingRaised$20m in a Series C1 roundSelected Investor(s)CICC CapitalProsperity724LAZARD VGB AI INFRA 40Lightmatter is leading the revolution in networking for AI.The company invented a leading 3D-stacked photonics

129、 engine,Passage,capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloadsHardware/SiliconSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|photonic computeMo

130、untain View,California|Founded2017Total Raised$421mEmployees154Last financingRaised$155m in a Series C2 roundSelected Investor(s)Fidelity GVHPSequoiaViking GlobalNextSilicon develops advanced computing architecture technology focused on enhancing future computer processing methodsThe company special

131、izes in chip design and software development,using innovative software algorithms to speed up compute-intensive applications.This technology provides high-performance architecture for supercomputers,offering a new approach to chip technology that supports organizations in achieving greater computati

132、onal efficiencyHardware/SiliconAI Use Case|HPC SupercomputeAustin,Texas|Founded2017Total Raised$300mEmployees243Last financingRaised$128m in Series C2Selected Investor(s)Playground GlobalStandard InvestmentsCorner VenturesThird Point Ventures25LAZARD VGB AI INFRA 40SiMa.ai facilitates the widespread

133、 adoption of high-performance machine learning inference at extremely low power in embedded edge applicationsIt offers push-button performance,effortless deployment,and scaling at the embedded edge.This enables businesses to support traditional computing with high-performance,energy-efficient,safe,a

134、nd secure machine learning inferenceHardware/SiliconSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|edge/embedded AISan Jose,California|www.sima.aiFounded2018Total Raised$270mEmployees177Last financingRaised$70m in a Series B1 roundSelected Investor(

135、s)FidelityMSD PartnersMaverick CapitalPoint72 VenturesSiPearl is a manufacturer of microprocessors for the European exascale supercomputing industryThe company designs high-performance,energy-efficient microprocessors for various applications,including computing,artificial intelligence,medical resea

136、rch,climate change mitigation,and energy management.This provides scientific researchers,supercomputing centers,and leading entities from the IT,electronics,and automotive sectors with cutting-edge microprocessors,thereby fostering innovation and progressHardware/SiliconAI Use Case|HPC SupercomputeM

137、aisons Laffitte,France|Founded2019Total Raised$131mEmployees200Last financingRaised$105m in a Series A roundSelected Investor(s)ARMBpifrance26LAZARD VGB AI INFRA 40Untether AI is a developer of AI chips designed to pioneer new frontiers in artificial intelligence applicationsThe companys chips integ

138、rate near-memory design with digital processing to facilitate neural net inference that minimizes the distance data must travel.This enables clients to enhance inference efficiency while consuming fewer resources and energy,and requiring less supporting infrastructure,thereby promoting sustainable a

139、nd efficient operationsHardware/SiliconSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|inference acceleratorToronto,Canada|www.untether.aiFounded2018Total Raised$154mEmployees127Last financingRaised$125m in a Series B1 roundSelected Investor(s)CPP In

140、vestmentsIntel CapitalLambda is a developer of a cloud computing platform tailored for large-scale artificial intelligence training and inference.The companys product portfolio spans from on-prem GPU hardware to hosted GPUs in the cloud and includes Lambdas proprietary software which enables deep le

141、arning/AI teams to access the tools they need via a singular interface regardless of the location of the compute resources(on-prem or cloudThe solution is ideal for tasks such as natural language processing and drug discovery,enabling organizations to accelerate their AI initiatives with ease and pr

142、ecisionHyperscalers and ComputeAI Use Case|compute-as-a-serviceSan Jose,California|Founded2012Total Raised$892mEmployees143Last financingRaised$500m in debt fundingSelected Investor(s)Alumni VenturesB CapitalIAG CapitalMacquarie Group27LAZARD VGB AI INFRA 40NexGen Cloud operates as an Infrastructure

143、-as-a-Service(IaaS)provider with a mission to bridge the gap between Web 2.0 and Web 3.0.Specializing in decentralized blockchain storage services,the company caters to the AI/ML,Meta,and Omniverse industriesBy offering robust and scalable solutions,it empowers large-scale projects to overcome chall

144、enges related to cost,transparency,and centralization,facilitating a seamless transition to the next generation of web technologiesHyperscalers and ComputeSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|compute-as-a-serviceLondon,UK|Founded2020Total

145、Raised$13mEmployees29Last financingRaised$13m in a Seed roundSelected Investor(s)DARMA CapitalTogether AI is an operator of a technology services platform dedicated to offering a decentralized cloud for AIThe platform focuses on constructing extensive,open models that are user-friendly and open-sour

146、ce.This enables researchers,developers,and companies to harness and enhance artificial intelligence through a seamless integration of data,models,and computation platformsHyperscalers and ComputeAI Use Case|compute-as-a-serviceMenlo Park,California|www.together.aiFounded2022Total Raised$233mEmployee

147、s76Last financingRaised$106m in a Series B roundSelected Investor(s)CoatueKleiner PerkinsLux CapitalNVIDIAProsperity728LAZARD VGB AI INFRA 40Anyscale aims to streamline distributed computing.The companys software specializes in an open-source framework that simplifies the creation of complex,compute

148、-intensive applications by easing the underlying hardware decisionsThis enables software developers of all skill levels to build applications capable of running at any scale,from a laptop to a data center,promoting versatility and efficiencyModel Serving and InferenceSource:Company Websites;Funding

149、Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|optimization/acceleration&hosting San Francisco,California|Founded2019Total Raised$260mEmployees355Last financingRaised$199m in a Series C roundSelected Investor(s)a16z AdditionAnt GroupNEAModulars is an integrated,composable suite of

150、 products that simplifies customers AI infrastructure so they can develop,deploy,and innovate fasterModular provides an engine that tries to improve the inferencing performance of AI models on CPUs and GPUs while delivering on cost savingsModulars other flagship product,Mojo,is a programming languag

151、e that aims to combine the usability of Python with features like caching,adaptive compilation techniques,and metaprogrammingModel Serving and InferenceAI Use Case|optimization/acceleration&hosting Palto Alto,California|Founded2022Total Raised$130mEmployees184Last financingRaised$100m in a Series B

152、roundSelected Investor(s)General Catalyst GVGreylock29LAZARD VGB AI INFRA 40OctoAI is a developer of a technology is designed to assist engineering teams in swiftly deploying machine learning models on any hardware,cloud provider,or edge deviceThe companys technology offers a managed service that ut

153、ilizes machine learning to automate machine language code generation and optimization in multi-cloud environments.This ensures that the models continue to operate at peak efficiency,providing businesses with secure deployments of deep learning models,thereby enhancing productivity and securityModel

154、Serving and InferenceSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|optimization/acceleration&hosting Seattle,Washington|www.octo.aiFounded2019Total Raised$133mEmployees109Last financingRaised$85m in a Series C roundSelected Investor(s)AdditionAmpli

155、fy PartnersTiger GlobalAbacus.AI is an autonomous AI platform that aims to assist organizations in creating large-scale,real-time customizable deep learning systemsThe platform offers an end-to-end autonomous AI service that trains machine and deep learning models for common enterprise AI use cases

156、such as churn prediction,time-series forecasting,and deep-learning-based personalization.It also allows for the creation of custom,specific models with a state-of-the-art toolset.This enables clients to integrate cutting-edge deep learning models into their business processes or customer experiences

157、,fostering innovation and improved efficiencyMLOPsAI Use Case|AI/ML platformsSan Francisco,California|www.abacus.aiFounded2019Total Raised$90mEmployees122Last financingRaised$50m in a Series C roundSelected Investor(s)General Catalyst GVGreylock30MLOPsLAZARD VGB AI INFRA 40Acceldata is a data and an

158、alytics platform designed to simplify data operationsThe platform provides information integration and data streaming services,enabling clients to stream,collect,and process data,construct data clusters,and gain actionable insights from the data.It also allows for optimization of workflow operations

159、 and capitalization on opportunities identified through predictive analytics.This empowers enterprises to proactively manage performance,security,data quality,and workflow,fostering improved efficiency and decision-makingSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB I

160、nsightsAI Use Case|monitoring and observabilityCampbell,California|www.acceldata.ioFounded2018Total Raised$106mEmployees259Last financingRaised$60m in a Series C roundSelected Investor(s)Aramco VenturesLightspeed Venture PartnersProsperity731Anaconda provides an enterprise grade platform for open-so

161、urce software development with a focus on AI and data science capabilities.Anacondas platform enables AI developers,data scientists and IT teams with secure access to curated open-source software artifacts,with security,policy and governance control,capabilities to develop and deploy on-prem LLMs,an

162、d a high-performance version of Python that optimizes workload performance for both numerical and general code.The platform helps AI developers and data scientists ensure complete reproducibility and reliability of their projects and in-production workloads,and supports IT teams with reobust governa

163、nce and control over how developers leverage open-source software artifacts.MLOPsAI Use Case|model development toolsAustin,Texas|Founded2012Total Raised$77mEmployees366Last financingRaised$31m in a Series B roundSelected Investor(s)BlackrockIn-Q-TelMorningsideSnowflake VenturesMLOPsLAZARD VGB AI INF

164、RA 40Domino Data Lab is a developer of an enterprise data science management platform designed to assist companies in building and deploying ideas through collaborative,reusable,and reproducible analysisThe platform expedites research,accelerates model deployment,and fosters collaboration for code-f

165、irst data science teams at scale.This enables data scientists to contribute to various fields,such as developing medicines,increasing crop productivity,adapting risk models to major economic shifts,constructing cars,and enhancing customer supportSource:Company Websites;Funding Press Releases;PitchBo

166、ok Data,Inc.;Lazard VGB InsightsAI Use Case|AI/ML platformsSan Francisco,California|www.domino.aiFounded2013Total Raised$224mEmployees323Last financingRaised$100m in a Series F roundSelected Investor(s)CoatueAmgen CapitalNVIDIASequoiaSnowflake VenturesEdge Impulse is an ML development platform desig

167、ned to bring about positive societal change through machine learning.The platform streamlines the process of building,deploying,and scaling embedded ML applications.This enables developers to create intelligent devices by simplifying the collection of real sensor data,live signal processing from raw

168、 data to neural networks,testing,and deployment to any target device,fostering innovation and efficiency in creating smart solutionsMLOPsAI Use Case|model development toolsSan Jose,California|Founded2019Total Raised$54mEmployees111Last financingRaised$34m in a Series B roundSelected Investor(s)Canaa

169、n PartnersCoatueIn-Q-Tel32MLOPsLAZARD VGB AI INFRA 40Featurebase is a data virtualization software is designed to secure access to large,fragmented,and geographically dispersed datasetsThe companys software aids in the mass parallelization of large,high cardinality,ad hoc queries for managing massiv

170、e,unbounded datasets and takes advantage of the efficiency,performance,and simplicity of bitmaps as a foundation for powering AI with real-time information.This enables clients to perform time-based sharing to capture streaming data and carry out segmentation based on historical data or time ranges,

171、ultimately enhancing data accessibility and analysis capabilitiesSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|feature engineering Austin,Texas|Founded2017Total Raised$30mEmployees30Last financingRaised$24m in a Series A round Selected Investor(s)D

172、rive CapitalOracleFiddler is an enterprise AI platform designed to create AI services that are transparent,explainable,and understandableThe platform utilizes the AI engine to provide statistical metrics,performance monitoring,and security services through a common language,centralized controls,and

173、actionable insights.This enables businesses to analyze,manage,and deploy their machine learning models at scale,enhancing efficiency and decision-making capabilitiesMLOPsAI Use Case|monitoring and observability Palo Alto,California|www.fiddler.aiFounded2018Total Raised$45mEmployees77Last financingRa

174、ised$32m in a Series B roundSelected Investor(s)Lightspeed Venture PartnersInsight PartnersLux Capital33MLOPsLAZARD VGB AI INFRA 40Gretel is a data categorization and identification platform,is designed to automatically generate an anonymized version of a datasetThe platform leverages machine learni

175、ng to categorize data across various customer identifiers such as names and addresses.It features automatic data labeling,power testing,and synthetics.This enables developers to safely and swiftly experiment,collaborate,and build with customer data,promoting innovation and data privacySource:Company

176、 Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|synthetic dataSan Diego,California|www.gretel.aiFounded2017Total Raised$68mEmployees77Last financingRaised$52m in a Series B round Selected Investor(s)Drive CapitalOracleHiddenLayers AISec Platform is an AI/ML Protec

177、tion Suite that ensures the integrity of customers models throughout the MLOps pipelineBy ensuring the security of pretrained models,detecting malicious injections,and monitoring algorithm inputs and outputs for potential threats,The AISec Platform delivers an automated and scalable defense tailored

178、 for MLThis enables proactive responses to attacks without necessitating access to private data or modelsMLOPsAI Use Case|model securityAustin,Texas|Founded2022Total Raised$56mEmployees50Last financingRaised$50m in a Series A roundSelected Investor(s)Capital One Ventures IBM VenturesM12Moore Strateg

179、ic Ventures34MLOPsLAZARD VGB AI INFRA 40Human Signal specializes in the development of advanced data labeling software engineered to centralize and streamline training data management The platform enhances operational efficiency by integrating robust management and annotator functionalities,which fa

180、cilitate and optimize collaborative data labeling processes,quality assurance,and analytical evaluations.This enables enterprises to expedite dataset annotation and achieve precise,high-performance machine learning and artificial intelligence models at scale,thereby maintaining a competitive edge in

181、 the industrySource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|data labelling San Francisco,California|Founded2019Total Raised$30mEmployees53Last financingRaised$25m in a Series A roundSelected Investor(s)Bow Capital500 GlobalUnusual VenturesLatent AI

182、is an inference platform designed to support edge computing workloadsThe company offers a quantization optimizer for edge AI devices to automate the exploration of low-bit-precision training deploying efficient neural networks for on-device intelligence and inference.This enables software developers

183、 to feasibly access,deploy,and manage AI for the edge,promoting innovation and efficiency in edge computing applicationsMLOPsAI Use Case|model development toolsMenlo Park,California|Founded2018Total Raised$31mEmployees45Last financingRaised$27m in a Series A1 roundSelected Investor(s)Lockheed Martin

184、 Ventures35MLOPsLAZARD VGB AI INFRA 40Lightning AI is a multi-cloud machine learning systems designed to aid in building simple research demosThe platform offers monitoring,training management,single command cloud training,experiment analysis,engineering automation,and automated artifact backups.Thi

185、s enables engineers,data scientists,and AI researchers to save time and train machine learning models on the cloud directly from their laptops,enhancing efficiency and convenienceSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|AI/ML platformsNew York

186、,New York|www.lightning.aiFounded2019Total Raised$62mEmployees64Last financingRaised$40m in a Series B roundSelected Investor(s)Bain Capital VenturesCoatueIndex VenturesMad Street Den is a cloud-based AI platform is designed to build models of generalizable intelligence and create actionable ways to

187、 contextualize AI on a large scaleThe companys platform offers artificial intelligence and computer vision modules to facilitate various features,including object recognition,gaze tracking,emotion-expression detection,head and facial gestures,as well as 3D facial reconstruction.This enables clients

188、to build models of generalizable intelligence on a grand scale,which can be deployed through meaningful applications across various industries,enhancing efficiency and innovationMLOPsAI Use Case|data preparationFounded2013Total RaisedUndisclosedEmployees313Last financingUndisclosedSelected Investor(

189、s)Alpha Wave GlobalChimera CapitalSequoia Capital36Redwood City,California|MLOPsLAZARD VGB AI INFRA 40Source:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB Insights37MLOPsMostly AI is a pioneer in GPU-powered technology designed to simulate synthetic customer data at scale.Th

190、is cutting-edge technology enables the generation of an unlimited number of realistic and representative synthetic customers,closely mirroring the patterns and behaviors of actual customers with unprecedented accuracyBy leveraging this advanced simulation capability,businesses can unlock a wealth of

191、 opportunities from previously inaccessible data,driving faster innovation while mitigating risks and reducing costs.This transformative approach empowers organizations to harness the full potential of their data assets,opening new avenues for growth and efficiencyAI Use Case|synthetic dataVienna,Au

192、stria|www.mostly.aiFounded2017Total Raised$31mEmployees61Last financingRaised$25m in a Series B roundSelected Investor(s)Citi Ventures42CAPMolten VenturesProtect AI is a cybersecurity platform is designed to concentrate on machine learning workflows and pipelinesThe companys platform offers innovati

193、ve security products and performs security scans using machine learning models and artificial intelligence systems to access curated resources,learn best practices in machine learning security,listen to podcasts with thought leaders,and connect with a thriving community.This enables enterprises to b

194、uild a safer,AI-powered world,fostering enhanced security and innovationMLOPsAI Use Case|model securityFounded2022Total Raised$49mEmployees46Last financingRaised$35m in a Series A roundSelected Investor(s)Evolution Equity PartnersSalesforce VenturesSeattle,Washington|MLOPsLAZARD VGB AI INFRA 40Resca

195、le,a developer of a cloud-based infrastructure platform,is designed to streamline scientific and engineering simulationsThe platform offers infinite scalability,customization tools,and the ability to make adjustments optimized for specific workloads,ultimately reducing turnaround times.This enables

196、businesses to transform their information technology into unified,agile environments,enhancing overall outcomes and efficiencySource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|model development toolsSan Francisco,California|Founded2011Total Raised$157m

197、Employees234Last financingRaised$105m in a Series C roundSelected Investor(s)A&E Investmentsa16zDCVCDST GlobalRelationalAI is a relational knowledge graph system designed to address complex business challengesThe company concentrates on the rich interdependencies and structures inherent in every bus

198、iness,complementing the modern data stack to expedite the development of intelligent data applications.This enables clients to implement intelligent applications with semantic layers on a data-centric foundation,lowering the barrier to codifying and utilizing knowledge,and ultimately enhancing busin

199、ess efficiency and decision-makingMLOPsAI Use Case|data preparationFounded2017Total Raised$122mEmployees169Last financingRaised$75m in a Series B roundSelected Investor(s)AdditionMadrona Venture GroupMenlo VenturesTiger Global Management38Berkeley,California|www.relational.aiMLOPsLAZARD VGB AI INFRA

200、 40Snorkel is an AI-powered programmatic data labeling tool is designed for extracting information from text documents such as scientific articles and electronic health recordsThe companys tool leverages theoretically grounded techniques to perform data augmentation and slicing data into different c

201、ritical subsets,and then identifies subsets of the data.This enables users to quickly leverage structured data resources available in domains such as bioinformatics,enhancing efficiency and productivity in data processing and analysisSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.

202、;Lazard VGB InsightsAI Use Case|data labelling Redwood City,California|www.snorkel.aiFounded2019Total Raised$138mEmployees157Last financingRaised$85m in a Series C roundSelected Investor(s)A&EAccelAdditionBlackrockGreylockGVUnravel leverages AI,ML and analytics to offer actionable recommendations an

203、d automation,enabling businesses to understand and optimize their data-driven applicationsUnravels purpose-built AI data observability and FinOps for Databricks,Snowflake,BigQuery and other modern data stacks provides granular visibility for cost allocation,metadata correlation for data reliability,

204、and AI-powered insights for data performance managementMLOPsAI Use Case|monitoring and observabilityFounded2012Total Raised$128mEmployees140Last financingRaised$70m in a Series D roundSelected Investor(s)Bridge BankGGV CapitalM12Menlo Ventures39Palo Alto,California|MLOPsLAZARD VGB AI INFRA 40Unstruc

205、tured.io is an open-source data transformation platform designed to simplify the preprocessing of natural language data for downstream machine learning servicesThe platform utilizes open-source libraries and application programming interfaces to construct custom preprocessing pipelines for labeling,

206、training,or production machine learning pipelines.This enables clients to convert simple data into language data,fostering innovation and improved efficiency in processing natural language informationSource:Company Websites;Funding Press Releases;PitchBook Data,Inc.;Lazard VGB InsightsAI Use Case|da

207、ta preparation Rocklin,California|www.unstructured.ioFounded2022Total Raised$68mEmployees50Last financingRaised$43m in a Series B roundSelected Investor(s)Alumni VenturesBain Capital VenturesMenlo VenturesNVIDIAWeights&Biases is a dataset optimization tool,is dedicated to creating high-quality softw

208、are tools for deep learning practitionersThe companys tool offers performance visualization for machine learning and assists teams in tracking their models,visualizing model performance,and effortlessly automating the training and enhancement of models.This enables companies to transform deep learni

209、ng research projects into deployed software,fostering innovation and efficiencyMLOPsAI Use Case|model development toolsFounded2017Total Raised$265mEmployees262Last financingRaised$65m in an Undisclosed roundSelected Investor(s)BOND CapitalCoatueFelicisNVIDIASapphire Ventures40San Francisco,Californi

210、a|www.wandb.aiDisclaimerLAZARD VGB AI INFRA 40This document has been prepared by Lazard Frres&Co.LLC(Lazard)solely for general information purposes and is based on publicly available information which has not been independently verified by Lazard.The information contained herein is preliminary and s

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219、bute it to any third party,or excerpt from or reproduce this document(in whole or in part),without the prior written consent of Lazard.41VII.ReferencesLAZARD VGB AI INFRA 401.McKinsey Digital,“The Economic Potential of Generative AI:The Next Productivity Frontier”,June 20232.Axios,“OpenAIs Chris Leh

220、ane says AI is Critical Infrastructure”,April 2024,as quoted in Axios,“Behind the Curtain:AIs Ominous Scarcity Crisis”,May 20243.Bank of America“Global Fund Manager Survey”,March 2024,as quoted in Morningstar,“Is the World in an AI Bubble?Money Managers are Split?”,March 20244.Bill Janeway,“Producti

221、ve Bubbles”,in Noema Magazine,July 20215.Bloomberg Intelligence,“2023 Generative AI Growth Report”,June 2023,as quoted in Bloomberg Press Release,“Generative AI to Become a$1.3 Trillion Market by 2032,Research Finds”,June 20236.McKinsey Digital,“The Economic Potential of Generative AI:The Next Produ

222、ctivity Frontier”,June 20237.Additional quotations in text boxes from:Mark Benioff,“Salesforce+Salesforce AI Day”video,cited in Forbes,“Shifting the AI Narrative:From Doomsday Fears to Pragmatic Solutions”,March 2024;GMO,“The Great Paradox of the U.S.Market”,March 2024;Jensen Huang,as cited in Reute

223、rs,“Chip Giant Nvidia Nears Trillion-Dollar Status on AI Bet”,May 2023;Bessemer Venture Partners,“Roadmap:AI Infrastructure”,June 20248.Gartner CEO Survey November 2023,quoted in Harvard Business Review,“5 Forces That Will Drive the Adoption of GenAI”,December 20239.Gartner Press Release,“More than

224、80%of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026”,October 202310.See Press Releases:“Amazon and Anthropic Deepen Their Shared Commitment to Advancing Generative AI”,March 2024;“Snowflake Partners with Mistral AI to Bring Industry-Leading Lang

225、uage Models to Enterprises Through Snowflake Cortex”,March 2024;“Hugging Face and Google Partner for Open AI Collaboration”,January 202411.Reuters,“US sets Stage for Antitrust Probes into Microsoft,OpenAI and Nvidia”,June 2024;Fortune,“Why Microsofts Surprise Deal with$4 Billion Startup Inflection i

226、s the Most Important Non-Acquisition in AI”,March 2024;Reuters,“Microsoft Pays Inflection$650 Million in Licensing Deal While Poaching Top Talents”,March 202412.Stanford Institute for Human-Centered Artificial Intelligence,“Artificial Intelligence Index 2024”,Chapter 4:Economy,Figure 4.3.8,p.3513.De

227、alroom.co,“State of AI Investing”,May 202442References(contd)LAZARD VGB AI INFRA 4014.See Anupam Chander,Haochen Sun(eds),“Data Sovereignty:From the Digital Silk Road to the Return of the State”,Oxford University Press 2023,Chapter 5:Andrew Keane Woods“Digital Sovereignty+Artificial Intelligence”;Wo

228、rld Economic Forum,“Sovereign AI:What it is,and 6 Strategic Pillars for achieving it”,April 202415.Lazard Geopolitical Advisory,“The Geopolitics of Artificial Intelligence”,October 202316.Allied Market Research,“AI Infrastructure Market 2023”,September 202317.AI Infrastructure Alliance,“The State of

229、 Infrastructure at Scale 2024”,March 202418.AI Infrastructure Alliance,March 2024,ibid.19.Ayar Labs CEO Charlie Wuischpard,quoted in Press Release,“Ayar Labs Showcases 4 Tbps Optically-Enabled Intel FPGA at Supercomputing 2023”,November 202320.The“Memory Wall”refers to a mismatch between the slow gr

230、owth of on-chip memory capabilities and the dramatic expansion of data requirements for advanced AI.See Optics and Photonics News,“Celestial AI Cultivates a Photonic Fabric Ecosystem”,April 202421.The Wall Street Journal,“Nvidias Business is Booming.Heres What Could Slow It Down”,May 202422.Quote fr

231、om Sequoia,“Generative AIs Act Two”,Sonya Huang and Pat Grady,September 202323.eeNews Embedded,“SiPearl Partners with Samsung for built-in HBM in Rhea”,May 202424.Grand View Research,“Edge Computing Market Size and Trends”,March 202425.Gartner,“Gartner Forecasts Worldwide AI Chips Revenue to Grow 33

232、%in 2024”,May 202426.Gartner,“3 Bold and Actionable Predictions for the Future of GenAI”,April 202427.International Energy Agency(IEA),“Electricity 2024”,January 2024:IEA,Paris https:/www.iea.org/reports/electricity-2024,Licence:CC BY 4.028.Luccioni,Sasha,Yacine Jernite,and Emma Strubell.Power Hungr

233、y Processing:Watts Driving the Cost of AI Deployment?In The 2024 ACM Conference on Fairness,Accountability,and Transparency,pp.85-99.2024,arXiv:2311.16863 cs.LG43References(contd)LAZARD VGB AI INFRA 4029.The“Magnificent Seven”includes Meta,Apple,Tesla,Nvidia,Amazon,Microsoft,and Alphabet30.Data Cent

234、er Dynamics,“Microsoft and OpenAI Consider$100 Billion,5GW Stargate AI Data Center”,March 202431.Data Center Dynamics,“Microsoft Signs 24/7 Nuclear Power Deal with Constellation for Boydton Data Center”,June 202332.Data Center Dynamics,“Is Microsoft and OpenAIs 5GW Stargate Supercomputer Feasible?”,

235、April 202433.Financial Times,“Microsoft to Power Data Centers with Big Brookfield Renewables Deal”,May 202434.Data Center Dynamics,“AWS Acquires Talens Nuclear Data Center Campus in Pennsylvania”,March 2024.See also Talen Energy,“Mar-24 Business Update Presentation”,available at https:/ 35.Capital G

236、roup,“Tech Giants Ratchet Up Spending in AI Race”,May 202436.See,for example,The Economist,“Big Techs Capex Splurge May be Irrationally Exuberant”,May 202437.Nicole Tanenbaum,Chequers Financial Management,quoted in“Big Techs AI Spending Spree Comes with a Catch”,May 202438.Google,“100%Renewable is J

237、ust the Beginning”,December 201639.Gartner,April 2024,ibid40.Venture Beat,“Why Small Language Models are the Next Big Thing in AI”,April 202441.Each ChatGPT search uses an estimated gallon of water.Microsoft,Meta,and Alphabet have all set targets to become water positive by 2030.See Liontrust,“The N

238、ew Investment Landscape for the Water Industry”,June 202442.Epoch AI,“Will We Run Out of Data?Limits of LLM Scaling Based on Human-Generated Data”,June 2024.See also full paper:Pablo Villalobos,Anson Ho,Jaime Sevilla,Tamay Besiroglu,Lennart Heim,and Marius Hobbhahn.“Will We Run Out of Data?Limits of

239、 LLM Scaling Based on Human-Generated Data”.ArXiv arXiv:2211.04325v2 cs.LG,2024.https:/doi.org/10.48550/arXiv.2211.04325 Graph generated from selected model data in EpochAIs Notable AI Models set,under Creative Commons Attribution license.Epoch AI,“Data on Notable AI Models”.Published online at epoc

240、hai.org.Retrieved from https:/epochai.org/data/notable-ai-models online resource.Accessed 1 Jul 2024.44References(contd)LAZARD VGB AI INFRA 4043.Gartner,April 2024,ibid44.Company Press Releases,“Stack Overflow and Google Cloud Announce Strategic Partnership to Bring Generative AI to Millions of Deve

241、lopers”,February 2024;“Stack Overflow and OpenAI Partner to Strengthen the Worlds Most Popular Large Language Models”,May 2024;“OpenAI and Reddit Partnership”,May 2024;Reddit,“Expanding our Partnership with Google”,February 2024 45.The Verge,“OpenAIs News Corp Deal Licenses Content from WSJ,New York

242、 Post and More”,May 2024;OpenAI Announcement,“A Landmark Multi-Year Global Partnership with News Corp”,May 202446.For more on Security for AI,see Menlo Ventures,“Security for AI:The New Wave of Startups Racing to Secure the AI Stack”,February 202447.Bidirectional Encoder Representations from Transfo

243、rmers,LLM introduced by Google in October 201848.See for example,Markets and Markets,“Autonomous AI and Autonomous Agents Market”,June 2023 49.Axios,“When AI-Produced Code Goes Bad”,June 202450.Quartz,“An AI Startup Thats Not Even Six Months Old Says Its Worth$2 Billion”,April 202451.Next Unicorn,“LangChain Secures$25 Million in Funding”,February 2024.See also LangChain Press Release,“Announcing the General Availability of LangSmith and Our Series A Led by Sequoia Capital”,February 202445