google phd new grad level

Google Engineering Levels Demystified

Want the scoop? We have the answers to your most burning leveling questions.

I spoke to a Google recruiter recently in one of our recent Office Hours. We got so many questions on leveling for new hires that I decided it's time to write a post on it.  Post #2 in our series: Here's the tea on what you should know about levels at G.

WTF are levels anyway?

After your interview, the Hiring Committee makes a decision on whether to move forward with your application and assigns you a level. The level determines the seniority of your role and also what salary band you fall into.

But before we get into the nitty-gritty, let's step back and talk about org structure at Google more broadly. There are 2 concepts you should know: ladder and level. They're both super important...

• Ladder = your role
• Level = your scope/ seniority

Similar to other FAANGs, Google has separate tracks for individual contributors (ICs) and managers. Broadly, this means engineers have 2 ladders (SWE and Software Engineering Manager). The difference is mainly in time allocation: SWE's can still manage others, but they're expected to spend most of their time contributing. EMs are expected to spend 80% of their time managing.

Performance reviews will focus on the expectations for your ladder, so being on the wrong ladder can hurt your ability to uplevel. Especially because Google, similar to Goldman, does “lagging promotions” (you need to exemplify that you can work at the next level for approximately six months before they will promote you).

👉Try next : Can you cut it at Google? Try answering 100 recent interview questions

Software Engineer Ladder Explained

Levels are denoted with the letter L + a number, which represents the seniority of the role (from lowest to highest). Most sourcing and hiring Google does goes up to level 6 (or L6).

L2 - Software Engineering Intern, usually in senior year of a four year degree program.

L3 - Full time, entry level Software Engineer, also known as the “new grad level”.

L4 - 1-5 years of industry experience, sometimes awarded to high potential new grads and PhDs.

L5 - Senior Software Engineer: 6-9 years of industry experience. This is the level most engineers are at internally within Google. You’re expected to be able to operate with little direction and handle complex tasks on your own.

L6 - Staff Software Engineer: 9+ years of experience and an expectation that you have extremely strong interpersonal skills. Many engineers will start managing larger projects and teams at this point. A promotion from L5 to L6 is more exponential in nature and it’s rare that candidates get hired into this role externally.

L7 - Senior Staff Software Engineer: 9+ years of experience. These offers are rare and most recruiters can count on one hand the number they’ve seen during their career.

L8 and above - Requires executive sponsorship and not typically recruited externally. This is considered an executive role with large scope.

How do SWEs get leveled?

It’s super complex - no big surprise there. Google works extremely hard to avoid bias, so these decisions are very quantitative. There are over 15 individual factors that play into your level.

Your industry experience

New grads: This is, at least clear cut.  Here’s how it usually goes:

• PhD candidates - L4
• MS/BA with no industry experience - L3

Folks with some industry experience: These situations are not clear-cut and a lot goes into the decision making process. I spoke with over 10 recruiters and hiring committee members for this piece. Their explanations involved a ton of hand-waving. Here’s the TLDR: your interview performance matters a ton, sometimes even more than your past experience.

Other important factors

Your past experience....

A factor that matters a lot is what Googlers call “Trajectory”. Simply put: if you have 10 years of experience on your resume but you don’t perform to that standard in your interview, this will weigh very heavily against you. You may even get a “no hire” decision instead of a lower level.

Your years of experience don’t directly map you to a level - people with the same # of years often level differently from, based on interview performance and where their past experience was from. Titles also don’t map 1:1 from your past job - many industries, like banking, are frivolous with titles. This means someone at Director level might end up as an L4/L5 at Google. Your current title also doesn’t determine your level at Google.

If you’re from FAANG, there is also no guarantee that your level and title will match. Unless you’re working on projects of similar complexity and scale, AND you do well in your interview (in which case, levels are more likely to match).

Interview performance...

By now, you should have guessed that this is incredibly important - but only up to a certain level.  Most recruiters we spoke with agreed on  the following criteria:

• L3/L4 - Main considerations are algorithms and coding ability.
• L5 - System design ability is weighed heavily, along with communication skills.
• L6- This is judged on ability to deliver impact and deal with a significant degree of complexity. The committee will likely consider past experience and responsibilities very heavily here, so your experience will play a larger role in the level assessment.

If you need further help understanding your level or negotiating your compensation, reach out - Candor can help.

👉Try next : Can you cut it at Google? Try answering 100 recent interview questions ​

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Google PhD Fellowship

Award type: 

Scholarships

Award description: 

Google PhD Fellowship  students are a select group recognized by Google researchers and their institutions as some of the most promising young academics in the world. The Fellowships are awarded to students who represent the future of research in the following fields: Algorithms, Optimizations and Market; Computational Neuroscience; Human-Computer Interaction; Machine Learning; Machine Perception, Speech Technology and Computer Vision; Mobile Computing; Natural Language Processing (including Information Retrieval and Extraction); Privacy and Security; Programming Languages and Software Engineering; Quantum Computing; Structured Data and Database Management; Systems and Networking.

Value description: 

• Up to 2 year Fellowship with a monetary award
• Full tuition and fees (enrollment fees, health insurance, books) plus a stipend to be used for living expenses, travel and personal equipment
• Google Research Mentor

Eligibility & selection criteria: 

• Full-time graduate students pursuing a PhD
• Must have completed all graduate coursework before the Fellowship begins
• Students must remain enrolled full-time in the PhD program for the duration of the Fellowship or forfeit the award.
• Google employees, and their spouses, children, and members of their household are not eligible.
• Students that are already supported by a comparable industry award are not eligible. Government or non-profit organization funding is exempt.
• Past awardees from Australia and New Zealand, East Asia, India and Southeast Asia PhD Fellowship programs are not eligible to apply again.
• Applications are evaluated on the strength of the research proposal, research impact, student academic achievements, and leadership potential. Research proposals are evaluated for innovative concepts that are relevant to Google’s research areas, as well as aspects of robustness and potential impact to the field. Proposals should include the direction and any plans of where your work is going in addition to a comprehensive description of the research you are pursuing.
• Essay responses are evaluated in addition to application materials to determine an overall recommendation.
• A nominee's status as a member of a historically marginalized group is not considered in the selection of award recipients.
• Research should align with  Google AI Principles .

Open to any program

Citizenship: 

Canadian/Permanent resident, International/study permit student

Selection process: 

Application required

Application deadline: 

Additional instructions: 

Current Waterloo students who are interested in applying for the Google PhD Fellowship must submit their application directly to Waterloo. Applicants are not permitted to apply directly to Google. Waterloo will nominate up to four applicants annually.

The following components are required as part of a complete application:

Reference letters:

Applicants must select 2-3 referees that are familiar with their work (at least one referee must be their thesis advisor). Applicants must ask referees to upload their signed reference letters to the  Computer Science Vault (for  Referees )  by April 1. 

Application package:

Applicants must upload the following documents  in the following order as one PDF  to the  Computer Science Vault (for  Applicants )  by April 1:

• Student CV (with links to website and publications, if applicable)
• Condensed CV of student’s primary advisor (maximum 1 page)
• Research / dissertation proposal (maximum 3 pages excluding references;  note that this max length changed from 8 to 3 pages effective 2024)
• Describe the desired impact your research will make on the field and society, and why this is important to you. Include any personal, educational and/or professional experiences that have motivated your research interests.
• Describe an example of your leadership experience in which you have positively influenced others, helped resolve disputes or contributed to group efforts over time.
• Note: A leadership role can mean more than just a title. It can mean being a mentor to others, acting as the person in charge of a specific task, or taking the lead role in organizing an event or project. Think about what you accomplished and what you learned from the experience. What were your responsibilities? Did you lead a team? How did your experience change your perspective on leading others? Did you help to resolve an important dispute at your school, church, in your community or an organization? And your leadership role doesn’t necessarily have to be limited to school activities. For example, do you help out or take care of your family?
• Waterloo transcript: Applicants must obtain a copy of their most recent Waterloo transcript. They may ask their  department graduate co-ordinator  to generate their unofficial UW transcript. If selected to be nominated by the University, GSPA will replace the unofficial UW transcript with an official one.
• Non-Waterloo transcript: Applicants must obtain official electronic copies of their non-Waterloo transcripts (if applicable). They may ask their  department graduate co-ordinator  to pull the transcripts on-file which were submitted at the time of admission to UW. If UW has not been supplied with a specific institution’s transcript or the version on-file is not the final version, the student is responsible for ordering the most recent transcript themselves and including it with their application package.

Contact person:

Co-ordinator, Graduate Financial Aid and Awards  in Graduate Studies and Postdoctoral Affairs (GSPA)

• Scholarships ,
• Open to any program ,
• Canadian/Permanent resident ,
• International/study permit student ,
• Application required ,

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Google PhD Fellowship recipients

Previous years:, algorithms, optimizations and markets.

Brice Huang, Massachusetts Institute of Technology

Debajyoti Kar, Indian Institute of Science

Jamie Tucker-Foltz, Harvard University

Joakim Blikstad, KTH Royal Institute of Technology

Mahdieh Labani, Macquarie University

Rehema Hamis Mwawado, University of Rwanda

Uddalok Sarkar, Indian Statistical Institute Kolkata

Computational Neural and Cognitive Sciences

Gizem Özdil, École polytechnique fédérale de Lausanne

Sreejan Kumar, Princeton University

Bridget Chak, University of Chicago

Li-Wen Chiu, National Yang Ming Chiao Tung University

Md. Saiful Islam, University of Rochester

Rutendo Jakachira, Brown University

Tsai-Min Chen, National Taiwan University

Wenhao Gao, Massachusetts Institute of Technology

Human Computer Interaction

Brianna Wimer, University of Notre Dame

Emily Kuang, Rochester Institute of Technology

Eunkyung Jo, University of California - Irvine

Georgianna Lin, University of Toronto

Gustavo Pacheco Santiago, Universidad Nacional Autónoma de México

Marcelo Marques da Rocha, Universidade Federal Fluminense

Yulia Goldenberg, Ben Gurion University

Zixiong Su, The University of Tokyo

Machine Learning

Berivan Isik, Stanford University

Blake Bordelon, Harvard University

Cristhian Delgado Fajardo, University of Otago

Denish Azamuke, Makerere University

Fuzhao Xue, National University of Singapore

Heinrich Pieter van Deventer, University of Pretoria

Imane Araf, Mohammed VI Polytechnic University

Itamar Franco Salazar Reque, Pontificia Universidad Católica del Perú

Jihoon Tack, Korea Advanced Institute of Science and Technology

Julliet Chepngeno Kirui, Strathmore University

Krystal Dacey, Charles Sturt University

Laura Smith, University of California - Berkeley

Marcos Paulo Silva Gôlo, ​​Universidade de São Paulo

Melisa Yael Vinograd, Universidad de Buenos Aires

Miriam Rateike, Saarland University

Mitchell Wortsman, University of Washington

Natalia Gil Canto, Universidade Estadual de Campinas

Nicolás Esteban Valenzuela Figueroa, Universidad de Chile

Omprakash Chakraborty, Indian Institute of Technology Kharagpur

S. Durga, Indian Institute of Technology Bombay

Strato Angsoteng Bayitaa, C.K. Tedam University of Technology and Applied Sciences

Yiding Jiang, Carnegie Mellon University

Yifan Zhang, National University of Singapore

Machine Perception, Speech Technology and Computer Vision

Antoine Yang, National Institute for Research in Digital Science and Technology (Inria)

Astitva Srivastava, International Institute of Information Technology Hyderabad

Chen Yu, National University of Singapore

Ethan Tseng, Princeton University

Matheus Viana da Silva, Universidade Federal de São Carlos

Sunghwan Hong, Korea University

Sungyeon Kim, Pohang University of Science and Technology

Vincent Milimo Masilokwa Punabantu, University of Cape Town

Yanxi Li, The University of Sydney

Yosef Gandelsman, University of California - Berkeley

Ziqi Huang, Nanyang Technological University

Mobile Computing

Ke Sun, University of California - San Diego

Kyungjin Lee, Seoul National University

Natural Language Processing

Allahsera Auguste Tapo, Rochester Institute of Technology

Cheng-Han Chiang, National Taiwan University

Liunian Li, University of California - Los Angeles

Sarah Masud, Indraprastha Institute of Information Technology Delhi

Sumanth Doddapaneni, Indian Institute of Technology Madras

Zhiqing Sun, Carnegie Mellon University

Privacy and Security

Jiayuan Ye, National University of Singapore

Miranda Wei, University of Washington

Neha Jawalkar, Indian Institute of Science

Yihui Zeng, Arizona State University

Programming Technology and Software Engineering

Aaditya Naik, University of Pennsylvania

Thanh Le-Cong, The University of Melbourne

Quantum Computing

Diego Hernando Useche Reyes, Universidad Nacional de Colombia

Elies Gil-Fuster, Free University of Berlin

Juan David Nieto García, Universidade Estadual de Campinas

Lia Yeh, University of Oxford

Structured Data and Database Management

Zezhou Huang, Columbia University

Systems and Networking

Jennifer Switzer, University of California - San Diego

Jiaxin Lin, University of Texas at Austin

Jinhyung Koo, Daegu Gyeongbuk Institute of Science & Technology

Maurine Chepkoech, University of Cape Town

Qinghao Hu, Nanyang Technological University

Anjali Gupta, Indian Institute of Technology Delhi

Shunhua Jiang, Columbia University

Shyam Sivasathya Narayanan, Massachusetts Institute of Technology

Venkata Naga Sreenivasulu Karnati, Indian Institute of Science

Yang P. Liu, Stanford University

Aditi Jha, Princeton University

Klavdia Zemlianova, New York University

Devon Jarvis, University of the Witwatersrand

Emily Schwenger, Albert Einstein College of Medicine

Reihaneh Torkzadehmahani, TU Munich

Xin Liu, University of Washington

Qian Niu, Kyoto University

Karthik Mahadevan, University of Toronto

Meena Muralikumar, University of Washington

Nika Nour, University of California - Irvine

Pang Suwanaposee, University of Canterbury

Ryan Louie, Northwestern University

Tiffany Li, University of Illinois - Urbana-Champaign

Zhongyi Zhou, The University of Tokyo

Eunji Kim, Seoul National University

Hayeon Lee, Korea Advanced Institute of Science and Technology

Julius von Kügelgen, Max Planck Institute for Intelligent Systems

Kaloma Usman Majikumna, Euromed University of Fes, Morocco

Lily Xu, Harvard University

Maksym Andriushchenko, EPFL

Pierre Marion, Fondation Sciences Mathématiques de Paris

Shashank Rajput, University of Wisconsin - Madison

Sheheryar Zaidi, University of Oxford

Sindy Löwe, University of Amsterdam

Tan Wang, Nanyang Technological University

Xiaobo Xia, University of Sydney

Yixin Liu, Monash University

Efthymios Tzinis, University of Illinois - Urbana-Champaign

Elizabeth Ndunge Mutua, Strathmore University

Haipeng Xiong, National University of Singapore

Jianyuan Guo, University of Sydney

Jiawei Ren, Nanyang Technological University

Juhong Min, Pohang University of Science and Technology

Liliane Momeni, University of Oxford

Qianqian Wang, Cornell University

Shuo Yang, University of Technology Sydney

Tahir Javed, Indian Institute of Technology Madras

Wei-Ting Chen, National Taiwan University

Yuming Jiang. Nanyang Technological University

Yu-Ying Yeh, University of California - San Diego

Binbin Xie, University of Massachusetts - Amherst

Clara Isabel Meister, ETH Zurich

Julia Mendelsohn, University of Michigan

Sachin Kumar, Carnegie Mellon University

Saley Vishal Vivek, Indian Institute of Technology Delhi

Swarnadeep Saha, University of North Carolina - Chapel Hill

Shuyi Wang, The University of Queensland

Thong Nguyen, National University of Singapore

Ussen Kimanuka, Pan African University Institute For Basic Sciences, Technology and Innovation

Amy Elizabeth Gooden, University Kwazulu-Natal

Promise Ricardo Agbedanu, University of Rwanda

Alexander Bienstock, New York University

Daniel De Almeida Braga, Universite Rennes 1

Gaurang Bansal, National University of Singapore

Nicolas Huaman Groschopf, Leibniz University of Hanover

Simon Spies, Max Planck Institute for Software Systems

Ilkwon Byun, Seoul National University

Margaret Fortman, University of Wisconsin - Madison

Oscar Higgott, University College London

Sam Gunn, University of California - Berkeley

Recommender Systems

Jessie J. Smith, University of Colorado - Boulder

Wenjie Wang, National University of Singapore

Nikolaos Tziavelis, Northeastern University

Humphrey Owuor Otieno, University of Cape Town

Jiarong Xing, Rice University

Shweta Pandey, Indian Institute of Science

Sunil Kumar, Indraprastha Institute of Information Technology Delhi

Yang Zhou, Harvard University

Yujeong Choi, Korea Advanced Institute of Science and Technology

Daniel Mutembesa, Makerere University

Kevin Tian, Stanford University

Prerona Chatterjee, Tata Institute of Fundamental Research

Sampson Wong, The University of Sydney

Santhoshini Velusamy, Harvard University

Sruthi Gorantla, Indian Institute of Science

Wenshuo Guo, University of California, Berkeley

Malvern Madondo, Emory University

Steffen Schneider, University of Tübingen

Nalini Singh, Massachusetts Institute of Technology

Roman Koshkin, Okinawa Institute of Science and Technology

Vishwali Mhasawade, New York University

Anupriya Tuli, Indraprastha Institute of Information Technology - Delhi

Chia-Hsing Chiu, National Taiwan University of Science and Technology

Dennis Makafui Dogbey, University of Cape Town

George Hope Chidziwisano, Michigan State University

Harmanpreet Kaur, University of Michigan

Srishti Palani, University of California, San Diego

Amir-Hossein Karimi, Max Planck Institute for Intelligent Systems

Anastasia Koloskova, EPFL, Lausanne

Anirudh Goyal, University of Montreal

Daniel Kang, Stanford University

Elena Fillola, University of Bristol

Emmanuel Chinyere Echeonwu, Nnamdi Azikiwe University, Nigeria

Gal Yona, Weizmann Institute of Science

Hae Beom Lee, KAIST

Jaekyeom Kim, Seoul National University

Logan Engstrom, Massachusetts Institute of Technology

Piyushi Manupriya, Indian Institute of Technology - Hyderabad

Qinbin Li, National University of Singapore

Shen Li, National University of Singapore

Shubhada Agrawal, Tata Institute of Fundamental Research

Theekshana Dissanayake, Queensland University of Technology

Tianyuan Jin, National University of Singapore

Yun Li, The University of New South Wales

Andrea Burns, Boston University

Fangzhou Hong, Nanyang Technological University

Haibin Wu, National Taiwan University

Jogendra Nath Kundu, Indian Institute of Science

Kelvin C.K. Chan, Nanyang Technological University

Sanghyun Woo, KAIST

Sara El-Ateif, National School For Computer Science (ENSIAS)

Soo Ye Kim, KAIST

Tewodros Amberbir Habtegebrial, Technical University of Kaiserslautern

Xinlong Wang, The University of Adelaide

Xueting Li, University of California, Merced

Zhiqin Chen, Simon Fraser University

Byungjin Jun, Northwestern University

Soundarya Ramesh, National University of Singapore

Derguene Mbaye, Universite Cheikh Anta Diop

Eya Hammami, LARODEC

Haoyue Shi, Toyota Technological Institute at Chicago

Kalpesh Krishna, University of Massachusetts Amherst

Peter Hase, University of North Carolina at Chapel Hill

Rochelle Choenni, University of Amsterdam

Chandan Kumar, Indian Institute of Technology - Kharagpur

Kevin Loughlin, University of Michigan

Teodora Baluta, National University of Singapore

Yuqing Zhu, University of California, Santa Barbara

Aishwarya Sivaraman, University of California, Los Angeles

Jenna Wise, Carnegie Mellon University

Alicja Dutkiewicz, Leiden University

Hsin-Yuan Huang, California Institute of Technology

Mykyta Onizhuk, The University of Chicago

Sayantan Chakraborty, Tata Institute of Fundamental Research

Brian Kundinger, Duke University

Yiru Chen, Columbia University

Yu Meng, University of Illinois at Urbana-Champaign

Zheng Wang, Nanyang Technological University

Aishwariya Chakraborty, Indian Institute of Technology - Kharagpur

Alireza Farshin, KTH Royal Institute of Technology

Erika Hunhoff, University of Colorado Boulder

S. VenkataKeerthy, Indian Institute of Technology - Hyderabad

Soroush Ghodrati, University of California, San Diego

Yejin Lee, Seoul National University

Jan van den Brand, KTH Royal Institute of Technology

Mahsa Derakhshan, University of Maryland, College Park

Sidhanth Mohanty, University of California, Berkeley

Computational Neuroscience

Connor Brennan, University of Pennsylvania

Abdelkareem Bedri, Carnegie Mellon University

Brendan David-John, University of Florida

Hiromu Yakura, University of Tsukuba

Manaswi Saha, University of Washington

Muratcan Cicek, University of California, Santa Cruz

Prashan Madumal, University of Melbourne

Alon Brutzkus, Tel Aviv University

Chin-Wei Huang, Universite de Montreal

Eli Sherman, Johns Hopkins University

Esther Rolf, University of California, Berkeley

Imke Mayer, Fondation Sciences Mathématique de Paris

Jean Michel Sarr, Cheikh Anta Diop University

Lei Bai, University of New South Wales

Nontawat Charoenphakdee, The University of Tokyo

Preetum Nakkiran, Harvard University

Sravanti Addepalli, Indian Institute of Science

Taesik Gong, Korea Advanced Institute of Science and Technology

Vihari Piratla, Indian Institute of Technology - Bombay

Vishakha Patil, Indian Institute of Science

Wilson Tsakane Mongwe, University of Johannesburg

Xinshi Chen, Georgia Institute of Technology

Yadan Luo, University of Queensland

Benjamin van Niekerk, University of Stellenbosch

Eric Heiden, University of Southern California

Gyeongsik Moon, Seoul National University

Hou-Ning Hu, National Tsing Hua University

Nan Wu, New York University

Shaoshuai Shi, The Chinese University of Hong Kong

Yifan Liu, University of Adelaide

Yu Wu, University of Technology Sydney

Zhengqi Li, Cornell University

Xiaofan Zhang, University of Illinois at Urbana-Champaign

Anjalie Field, Carnegie Mellon University

Mingda Chen, Toyota Technological Institute at Chicago

Shang-Yu Su, National Taiwan University

Yanai Elazar, Bar-Ilan

Julien Gamba, Universidad Carlos III de Madrid

Shuwen Deng, Yale University

Yunusa Simpa Abdulsalm, Mohammed VI Polytechnic University

Adriana Sejfia, University of Southern California

John Cyphert, University of Wisconsin-Madison

Amira Abbas, University of KwaZulu-Natal

Mozafari Ghoraba Fereshte, EPFL

Yanqing Peng, University of Utah

Huynh Nguyen Van, University of Technology Sydney

Michael Sammler, Saarland University, MPI-SWS

Sihang Liu, University of Virginia

Yun-Zhan Cai, National Cheng Kung University

Aidasadat Mousavifar, EPFL Ecole Polytechnique Fédérale de Lausanne

Peilin Zhong, Columbia University

Siddharth Bhandari, Tata Institute of Fundamental Research

Soheil Behnezhad, University of Maryland at College Park

Zhe Feng, Harvard University

Caroline Haimerl, New York University

Mai Gamal, German University in Cairo

Catalin Voss, Stanford university

Hua Hua, Australian National University

Zhanna Sarsenbayeva, University of Melbourne

Abdulsalam Ometere Latifat, African University of Science and Technology Abuja

Adji Bousso Dieng, Columbia University

Anshul Mittal, IIT Delhi

Blake Woodworth, Toyota Technological Institute at Chicago

Diana Cai, Princeton University

Francesco Locatello, ETH Zurich

Ihsane Gryech, International University Of Rabat, Morocco

Jaemin Yoo, Seoul National University

Maruan Al-Shedivat, Carnegie Mellon University

Ousseynou Mbaye, Alioune Diop University of Bambey

Rendani Mbuvha, University of Johannesburg

Shibani Santurkar, Massachusetts Institute of Technology

Takashi Ishida, University of Tokyo

Chenxi Liu, Johns Hopkins University

Kayode Kolawole Olaleye, Stellenbosch University

Ruohan Gao, The University of Texas at Austin

Tiancheng Sun, University of California San Diego

Xuanyi Dong, University of Technology Sydney

Yu Liu, Chinese University of Hong Kong

Zhi Tian, University of Adelaide

Naoki Kimura, University of Tokyo

Abigail See, Stanford University

Ananya Sai B, IIT Madras

Byeongchang Kim, Seoul National University

Daniel Patrick Fried, UC Berkeley

Hao Peng, University of Washington

Reinald Kim Amplayo, University of Edinburgh

Sungjoon Park, Korea Advanced Institute of Science and Technology

Ajith Suresh, Indian Institute of Science

Itsaka Rakotonirina, Inria Nancy

Milad Nasr, University of Massachusetts Amherst

Sarah Ann Scheffler, Boston University

Caroline Lemieux, UC Berkeley

Conrad Watt, University of Cambridge

Umang Mathur, University of Illinois at Urbana-Champaign

Amy Greene, Massachusetts Institute of Technology

Leonard Wossnig, University College London

Yuan Su, University of Maryland at College Park

Amir Gilad, Tel Aviv University

Nofar Carmeli, Technion

Zhuoyue Zhao, University of Utah

Chinmay Kulkarni, University of Utah

Nicolai Oswald, University of Edinburgh

Saksham Agarwal, Cornell University

Emmanouil Zampetakis, Massachusetts Institute of Technology

Manuela Fischer, ETH Zurich

Pranjal Dutta, Chennai Mathematical Institute

Thodoris Lykouris, Cornell University

Yuan Deng, Duke University

Ella Batty, Columbia University

Neha Spenta Wadia, University of California - Berkeley

Reuben Feinman, New York University

Human-Computer Interaction

Gierad Laput, Carnegie Mellon University

Mike Schaekermann, University of Waterloo

Minsuk (Brian) Kahng, Georgia Institute of Technology

Niels van Berkel, The University of Melbourne

Siqi Wu, Australian National University

Xiang Zhang, The University of New South Wales

Abhijeet Awasthi, Indian Institute of Technology - Bombay

Aditi Raghunathan, Stanford University

Futoshi Futami, University of Tokyo

Lin Chen, Yale University

Qian Yu, University of Southern California

Ravid Shwartz-Ziv, Hebrew University

Shuai Li, Chinese University of Hong Kong

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Stephen Tu, University of California - Berkeley

Steven James, University of the Witwatersrand

Xinchen Yan, University of Michigan

Zelda Mariet, Massachusetts Institute of Technology

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Antoine Miech, INRIA

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Arulkumar S, Indian Institute of Technology - Madras

Joseph Redmon, University of Washington

Raymond Yeh, University of Illinois - Urbana-Champaign

Shanmukha Ramakrishna Vedantam, Georgia Institute of Technology

Lili Wei, Hong Kong University of Science & Technology

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Shilin Zhu, University of California - San Diego

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Haifeng Xu, University of Southern California

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Riley Spahn, Privacy, Columbia University

Yun Teng, Computer Graphics, University of California, Santa Barbara

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Xiaolan Wang, Structured Data, University of Massachusetts Amherst

Tan Zhang, Mobile Systems, University of Wisconsin-Madison

Wojciech Zaremba, Machine Learning, New York University

Guosheng Lin, Machine Perception, University of Adelaide

Kellie Webster, Natural Language Processing, University of Sydney

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UN Summit of the Future: AI opportunity for everyone

Sep 21, 2024

[[read-time]] min read

Editor’s note: This week in New York City, leaders from around the world are gathering for the 79th United Nations General Assembly (UNGA) — including the first ever “ Summit of the Future ” — where Google CEO Sundar Pichai delivered a keynote address today.

What follows is a transcript of the remarks, as prepared for delivery.

Introduction

Mr. Secretary-General, President of the General Assembly, excellencies, ladies and gentlemen — it’s a privilege to join you today.

I’m energized by the summit’s focus on the future. We have a once-in-a- generation opportunity to unlock human potential, for everyone, everywhere.

I believe that technology is a foundational enabler of progress. Just as the internet and mobile devices expanded opportunities for people around the world, now AI is poised to accelerate progress at unprecedented scale.

I’m here today to make the case for three things:

• Why I believe AI is so transformative
• How it can be applied to benefit humanity and make progress on the UN’s Sustainable Development Goals
• And where we can drive deeper partnerships to ensure that the technology benefits everyone

Expanding opportunity through technology

But first let me share why this is so important — to me personally, and to Google as a company.

Growing up in Chennai, India, with my family, the arrival of each new technology improved our lives in meaningful ways. Our first rotary phone saved us hours of travel to the hospital to get test results. Our first refrigerator gave us more time to spend as a family, rather than rushing to cook ingredients before they spoiled.

The technology that changed my life the most was the computer. I didn’t have much access to one growing up. When I came to graduate school in the US, there were labs full of machines I could use anytime I wanted — it was mind blowing. Access to computing inspired me to pursue a career where I could bring technology to more people.

And that path led me to Google 20 years ago. I was excited by its mission: to organize the world’s information and make it universally accessible and useful.

That mission has had incredible impact:

Google Search democratized information access, and opened up opportunities in education and entrepreneurship. Platforms like Chrome and Android helped bring one billion people online.

Today, 15 of our products serve more than half a billion people and businesses each. And six of them – such as Search, Maps and Drive – each serve more than 2 billion. There’s no cost to use them, and most of our users are in the developing world.

The AI opportunity

Today we’re working on the most transformative technology yet: AI.

We’ve been investing in AI research, tools, and infrastructure for two decades because it’s the most profound way we can deliver on our mission — and improve people’s lives.

I want to talk today about four of the biggest opportunities we see, many of which align with the SDGs.

One is helping people access the world’s information and knowledge in their own language.

Using AI, in just the last year, we have added 110 new languages to Google Translate, spoken by half a billion people around the world. That brings our total to 246 languages, and we’re working toward 1,000 of the world’s most spoken languages.

A second area is accelerating scientific discovery to benefit humanity.

Our AlphaFold breakthrough is solving big challenges in predicting some of the building blocks of life, including proteins and DNA. We opened up AlphaFold to the scientific community free of charge, and it’s been accessed by more than two million researchers from over one hundred and ninety countries. Thirty percent are in the developing world - for example over 25,000 researchers just in Brazil. Globally, AlphaFold is being used in research that could help make crops more resistant to disease, discover new drugs in areas like malaria vaccines and cancer treatments, and much more.

A third opportunity is helping people in the path of climate-related disaster, building on the UN's initiative for “Early Warnings for All.” Our Flood Hub system provides early warnings up to seven days in advance, helping protect over 460 million people in over 80 countries.

And for millions in the paths of wildfires, our boundary tracking systems are already in 22 countries on Google Maps. We also just announced FireSat technology, which will use satellites to detect and track early-stage wildfires, with imagery updated every 20 minutes globally, so firefighters can respond. AI gives a boost in accuracy, speed and scale.

Fourth, we see the opportunity for AI to meaningfully contribute to economic progress. It’s already enabling entrepreneurs and small businesses …empowering governments to provide public services… and boosting productivity across sectors. Some studies show that AI could boost global labor productivity by 1.4 percentage points, and increase global GDP by 7%, within the next decade.

For example, AI is helping improve operations and logistics in emerging markets, where connectivity, infrastructure and traffic congestion are big challenges. Freight startup Gary Logistics in Ethiopia is using AI to help move goods to market faster and bring more work opportunities to freelance drivers.

These are just early examples. And there are so many others across education, health and sustainability. As technology improves, so will the benefits.

The risks of AI

As with any emerging technology, AI will have limitations … be it issues with accuracy, factuality, and bias … as well as the risks of misapplication and misuse, like the creation of deep fakes.

It also presents new complexities, for example the impact on the future of work.

For all these reasons, we believe that AI must be developed, deployed, and used responsibly, from the start.

We’re guided by our AI Principles, which we published back in 2018. And we work with others across the industry, academia, the UN, and governments in efforts like the Frontier Model Forum, the O.E.C.D., and the G7 Hiroshima Process.

Preventing an AI divide

But I want to talk about another risk that I worry about.

I think about where I grew up, and how fortunate I was to have access to technology, even if it came slowly.

Not everyone had that experience. And while good progress has been made by UN institutions like the I.T.U., gaps persist today in the form of a well known digital divide.

With AI, we have the chance to be inclusive from the start, and to ensure that the digital divide doesn’t become an AI divide. This is a challenge that needs to be met by the private sector and public sector working together. We can focus in three key areas:

First is digital infrastructure.

Google has made big investments globally in subsea and terrestrial fiber optic cables.

One connects Africa with Europe, and two others will be the first intercontinental fiber optic routes that connect Asia Pacific and South America, and Australia and Africa.

These fiber optic routes stitch together our network of 40 cloud regions around the world that provide digital services to governments, entrepreneurs, SMBs and companies across all sectors.

In addition to compute access, we also open up our technology to others. We did this with Android; and now our Gemma AI models are open to developers and researchers, and we will continue to invest here.

A second area is about investing in people.

That starts with making sure people have the skills they need to seize new opportunities.

Our Grow with Google program has already trained one hundred million people around the world in digital skills.

And today I’m proud to announce our Global AI Opportunity Fund. This will invest one hundred and twenty million dollars to make AI education and training available in communities around the world. We’re providing this in local languages, in partnership with nonprofits and NGOs.

We’re also helping to support entrepreneurs for the AI revolution. In Brazil, we worked with thousands of women entrepreneurs to use Google AI to grow their businesses. In Asia, where fewer than six percent of start-ups are founded by women, we’re providing many with mentorship, capital, and training.

An enabling policy environment

The third area is one where we especially need the help of the member countries and leaders in this room: creating an enabling policy environment. One that addresses both the risks and worries around new technologies, and also encourages the kinds of applications that improve lives at scale.

This requires a few things:

• Government policymaking that supports investments in infrastructure, people, and innovation that benefits humanity,
• Country development strategies and frameworks like the Global Digital Compact that prioritize the adoption of AI solutions,
• And smart product regulation that mitigates harms and resists national protectionist impulses — that could widen an AI divide and limit AI’s benefits.

We are excited to be your partner, and to work with you to make sure bold innovations are deployed responsibly so that AI is truly helpful for everyone.

The opportunities are too great … the challenges too urgent ... and this technology too transformational, to do anything less.

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