Jira, the quintessential issue tracking tool, has become a household name among software developers since its launch in 2002. The makers of Jira – Atlassian – has come a long way in the last 20-odd years. With over 260,000 customers and 10,000 employees, the company today offers a suite of products including different flavours of Jira, Confluence, Trello, Bitbucket and Compass among others. 

Rajeev Rajan, Atlassian’s CTO, who joined the company in July last year, has an ambitious roadmap to build one of the ‘top engineering teams in the world’. While he is at it, his past experience has proven invaluable. 

Rajeev spent five years as VP and Head of Engineering for Facebook and Head of Office for Meta in the Pacific Northwest Region. Prior to that, he had long stint with Microsoft across multiple products from Exchange to SQL Server to Active Directory, and Office 365.

CIO Dimension caught up with Rajeev during his recent visit to India at Atlassian’s R&D center in Bangalore. 

Excerpts from the interview: 

You have always been a software/engineering leader in your career. How is your experience in companies like Meta and Microsoft guiding you at Atlassian and how are you doing things differently here?  

I was really attracted to the Atlassian story. For an engineer, Atlassian is an iconic engineering company. It built Jira for engineers and serves over 260,000 companies around the world. As an engineer, it was a big attraction to be part of a brand that so many engineers across the world use. 

I obviously bring a lot of best practices, as I have worked for companies that have exceptional engineering teams. I have seen good times and not so good times in both my previous companies. That has helped me identify some of the pitfalls and anti-patterns that we should avoid.

One of the things that I have done differently is to focus on developer productivity. I believe that our own engineers must become the most productive so that they can make every engineer in the world more productive. 

What are the key gaps in developer productivity that you are trying to address here? 
The definition of productivity here is not the traditional yardstick of efficiency or getting an engineer to write more lines of codes. That’s not the productivity I am looking at. It’s more about how we make an engineer feel the ‘developer joy’. As a developer, one needs to get to the ‘flow’ —  you’ll know this better as a writer. Just like writing, coding needs to have a state of flow. I have started a whole new program around developer productivity at Atlassian. The idea is to ensure that we increase developer productivity to the point that the engineers feel excited about coding.

Atlassian has grown over time and a lot of our code has acquired the characteristics of legacy, monolithic codebase. And monoliths become harder to write code in as nobody understands the whole history. That’s where we started leveraging Microservices, by splitting the architecture into smaller components. We have been doing some work in this area so that our engineers are able to move much faster. 

Building a strong engineering team has been a key focus area for Atlassian. Considering the uncertain employment landscape and other macro factors, this seems like a complex undertaking. 
Keeping a high bar on talent is non-negotiable for us. We have up-levelled our practices to ensure that the people we hire are truly world-class engineers. We have done well in attracting talent from some top tier companies. We believe that the future is remote, distributed work and our “Team Anywhere” policy has resonated really well.  

That said, we are not immune to the macro situation. So, we did pull back on some of our hiring goals globally and in India so that we can right-size the teams for the current business priorities. Mid-to-long term, we expect to hire even more as things pick up. We are going to be conscious about how we scale our growth, based on what we need for our business. 

We are a company that invests as much as 40% of our revenue back into R&D; that’s an indication of our commitment. Our global engineering team is roughly about 5,500 engineers and we still continue to hire. We have about 1000 engineers in India alone and it is one of the fastest growing R&D centers globally. 

As you rightly said, the future of work is remote, distributed and hybrid. How are some of these radical shifts influencing innovation at Atlassian? 
We use our own products for everything we do. We track issues in Jira. This plays a crucial role in making our offerings suitable for remote and hybrid work environments. We focus a lot of self-service APIs that allow developers to independently work on and contribute to different software components. That helps them not to be restrained by geographical boundaries or someone else’s work. These practices are then incorporated into our products. Atlassian Compass for example allows Atlassian developers sitting anywhere in the world to locate different components, documents, APIs and so on, which in turn helps with the ‘developer flow’ that I earlier talked about. 

Atlassian’s project tracking tool Jira is a go-to software for developers. How are your efforts to take Jira beyond the development world faring? 
Our Jira Service management suite stemmed out of the idea that IT departments work a lot like software teams. That’s how we pivoted Jira to IT departments. That’s a successful second business that we have grown. We identified similar requirements in the HR, legal and finance departments. That’s how we made the Jira Work Management for all. Our mission is to help unleash productivity in all kinds of teams and we believe that Jira has a universal appeal. 

More critically, the knowledge worker economy is ripe for revolution. The Office 365, Google Docs or Slack or Zoom are commodity tools and often really don’t make the big difference (in productivity of workforce). Companies, on the other hand, are seeking how they can unleash the productivity of knowledge workers. Especially the younger workforce that are mobile-first and are used to doing things digitally. Many of today’s office tools are simply digital versions of the analogue processes. In the new world, we need digital-first ways of doing things. And that’s the space that Atlassian is innovating in. 

You alluded to the changing definition of developer productivity earlier. Does AI have a role in accelerating that shift? How is Gen AI going to change software development from here on? 
AI is going to change everything. It’s as big a change that I have seen in my lifetime in technology – just like the revolution led by iPhone and previously Internet. Developers are already using coding assistants. They don’t have to write everything from the beginning on a code editor. A lot of mechanical and repetitive work is being done by Generative AI. Coding itself is changing. 

Will we reach a point where everything gets written by AI? I don’t know. 

But I strongly believe that human beings are moving up the (value) chain in terms of knowledge work. You will always need the humans to do the most intelligent and complex things. If you are an engineer writing codes, there is a lot of repetitive work that you do today. If those things are taken care of, the developers can focus on the real idea. It drastically improves their productivity. 

At Atlassian, we believe that Gen AI is truly the next revolutionary wave and we are squarely on that journey. While we have been using AI in our products, we now have the power of LLMs (Large Language Models). A few months ago, we tapped OpenAI to introduce smarter features to our products. For example, we now have a chat agent and natural language interface which allows users to use different languages while using our tools. AI’s ability understand human language is truly transformational for software engineering. 

That’s a big shift. What would this mean for today’s developers? Is this going to fundamentally change the way they look at software engineering? 
Today, a lot of coding happens at the file level. The future, which is not far away, is going to be about assembling components. Let me use Home Depot (a multinational home improvement retailer) as a case in point. They have tools and components that help you build something entirely new or remodel an existing structure. That’s exactly how software is going to be – less about writing codes and more like the Lego blocks. 

You don’t have to make everything from the scratch. If you go to any software company, almost everything is already written. Developers write some variation of it. There is a lot of effort wasted because we are not reusing. Reusable software is the future. 

There are over four million engineers in India and a lot of them are rewriting things that are already written. If we had a repository of reusable objects, we wouldn’t have to constantly rewrite software modules. Which is why I believe that the  low-code, no-code revolution is very powerful. Coding is not something only coding engineers should understand. Getting that power to more people will be the next evolution where programming is not just a domain of some set of engineers who understand 0s and 1s in their heads. 

That’s going to be one of our focus areas going forward.      

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Nutanix, the hybrid multicloud computing, announced the Nutanix GPT-in-a-Box solution for customers looking to jump-start their artificial intelligence (AI) and machine learning (ML) innovation, while maintaining control over their data. The new offering is a software-defined AI-ready platform, along with services to help organizations size and configure hardware and software infrastructure suitable to deploy a curated set of large language models (LLMs). It allows customers to easily procure AI-ready infrastructure to fine-tune and run generative pre-trained transformers (GPT), including LLMs at the edge or in their datacenter, said the company. 

Many enterprises are grappling with how to quickly, efficiently and securely take advantage of the power of generative AI and AI/ML applications, especially for use cases that cannot be run in the public cloud because of data sovereignty, governance and privacy concerns. From automated transcription of internal documents, to high-speed search of multimedia contents, and automated analysis, many organizations see the opportunity with AI but are struggling with growing concerns regarding intellectual property leakage, compliance and privacy. Additionally, organizations looking to build an AI-ready stack often struggle with how to best support ML administrators and data scientists, while the prospect of large AI investment costs has enterprises stalled in their AI and ML strategy.

“As customers look to design and deploy generative AI solutions, they find themselves struggling with balancing the deep expertise required to install, configure, and run these workloads with concerns around their data security and protecting company IP – all while controlling costs,” said Greg Macatee, Senior Research Analyst, Infrastructure Systems, Platforms and Technologies Group at IDC. “With GPT-in-a-Box, Nutanix offers customers a turnkey, easy-to-use solution for their AI use cases, offering enterprises struggling with generative AI adoption an easier on-ramp to deployment.”

The Nutanix GPT-in-a-Box solution delivers ready-to-use customer-controlled AI infrastructure for the edge or the core data center and allows customers to run and fine-tune AI and GPT models while maintaining control over their data. Nutanix provides a full complement of security and data protection offerings ideal for AI data protection.

This new solution includes:

• Nutanix Cloud Infrastructure platform, with the Nutanix Files Storage and Objects Storage solutions, the Nutanix AHV hypervisor and Kubernetes, along with NVIDIA GPU acceleration, which can be sized for large to small scale.
• Nutanix services to help customers size their cluster and deploy an opinionated stack with the leading open source deep learning and MLOps frameworks, inference server, and a curated set of large language models such as Llama2, Falcon and MPT.
• Ability for data scientists and ML administrators to immediately consume these models with their choice of applications, enhanced terminal UI, or standard CLI.
• The platform can also be leveraged to run other GPT models, as well as fine tune these models leveraging internal data, hosted on included Nutanix Files or Objects Storage services.

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DXC Technology, a Fortune 500 global technology services company, has forecast five ways software will help create a more sustainable future in the next five years.

“Technology has an outsized role in almost every aspect of sustainability – from increasing productivity, efficiency, and cost savings, to monitoring and modelling progress,” said Henrik Hvid Jensen, Chief Technology Strategist, DXC. “As a global IT services leader, DXC sees a number of pivotal ways that technology will help us to drive sustainable development in the next five years.”

1. Organizations will adopt circular economy business models

Globally, natural resources are dwindling and their increased exploitation to cater for global demand is having a negative impact on the environment. To build a competitive global circular economy that produces no waste, companies must adapt their business models to maximize resource efficiency, develop recyclable products, and repurpose waste as new offerings.

One of the biggest challenges of moving to a circular economy is collecting and sharing data about a product throughout its entire lifecycle. Digital product passports (DPPs) offer that capability and promise to act as a transparent record of a product’s sustainability, environmental and recyclability attributes. The European Union is positioning itself as a first mover in the space and expects most products in the region to be covered by DPP regulation by 2030. 

2. AI will help manage natural resources

AI will become increasingly important for tackling most environmental sustainability issues including biodiversity, energy, transportation, and the management of agroecosystems. In the agriculture sector, AI can produce insight and increase automation to improve environmental stewardship and detect diseases and potential infestations before crops or livestock are threatened. Technology not only impacts individual farms’ output, but data generates valuable insights that can positively influence policy decisions at the local or national level.

3. AI will increase the viability of renewable energy

McKinsey estimates that by 2026, global renewable-electricity capacity will rise more than 80 percent from 2020 levels. For example, Europe will add approximately 36 million renewable-class assets, such as solar panels, electric vehicles (EVs), and energy storage, to the grid in 2025, and 89 million by 2030.

Automation and data analytics can help manage decentralized energy sources, direct excess electricity and flag potential grid weak points before they become significant issues, and help utilities redirect power to where it’s needed in real time.

4. There will be a major shift to software-defined EVs in the next decade

The automotive industry accounts for nearly a quarter of global greenhouse emissions and is a major cause of city air pollution. To address this, regulators in the U.S., Europe and elsewhere are reviewing policy and implementing laws to limit the sale of new gas and diesel cars. As a result, eighteen of the world’s largest automakers have switched or pledged to switch, either completely or significantly, to EV manufacturing in the coming years.

EVs will be software-defined vehicles (SDVs) with automated capabilities to manage the car more efficiently with particular attention to environmental sensitivities. SDVs feature smart routing and energy optimization that can mitigate issues related to charging capacity and range.

5. Finance systems will be re-engineered to consume less energy

Transitioning to more environmentally sustainable operations is a top priority for banks and financial services organizations. More sustainable software, more efficient algorithms and better data processing are key to these efforts. The global green finance market has thus grown from $5.2 billion in 2012 to more than $540 billion in 2021.

In addition to growing environmentally conscious portfolios, the financial services sector is significantly reducing its energy consumption by enabling efficiencies in data centers. Upgrades include data deduplications and compression which can improve data storage layout and storage efficiency while slashing energy usage. Beyond the traditional financial sector, new approaches are helping to increase the sustainability of the cryptocurrency-mining process.

“We can all look forward to the day when sustainability is the new standard and software will be at the heart of helping us create a climate-secure and competitive future,” added Henrik Hvid Jensen, Chief Technology Strategist, DXC.

Source: DXC 

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