CEO Insights

Unleashing the Value of AI for Individuals and Enterprises (Part 2): Viewing AI from a Humanistic Perspective

To keep Taiwan's industry, government, academia, and research institutions ahead of the global technology and industry trends amid this wave of transformation, the Taiwan Science and Technology Hub (Taiwan S&T Hub) has invited internationally renowned AI expert Dr. Fei-Fei Li, current director of Stanford's Human-Centered AI Institute, Chief Scientist and Researcher of ImageNet, and co-founder and chairperson of AI4ALL, to participate in the "What we see and what we value: AI with a human perspective" roundtable forum held on March 23. Together with local experts, they discussed how to make AI a key driving force for the betterment of humanity. The forum was moderated by Erica Lu, a social affairs consultant at the Business Next, and featured guests including the Pegatron Corporation Chairman Tung Tzu-Hsien, PChome CEO Alice Chang, and iKala co-founder and CEO Sega Cheng, among other industry experts.

The following article is based on the insights shared by iKala co-founder and CEO Sega Cheng during the roundtable discussion (Part 2).

Erica Lu:

In the audience, we have a high school student who would like to ask: Nowadays, we encourage everyone to learn about programming and software, and even to write code. In the age of AI, to what extent should we learn AI? Should we focus on learning AI tools, understanding the underlying principles, or actually developing a model?

Sega Cheng:

I have an eight-year-old daughter myself, so I have been considering whether children should learn programming languages at an early age. Let me first share my thoughts on programming languages. I believe that there is no need to invest too early in learning them, and we don't need to turn it into a nationwide movement.

We should look at this issue of children learning programming languages from two perspectives. The first one is from the perspective of AI. There is a research direction focused on redesigning computer systems. Computer systems have been developed with layers of abstraction, which is primarily for human convenience. As a result,it brought the use of many intermediate languages, followed by programming languages and natural languages. However, AI does not require these intermediate parts. So, my expectation is that the future research direction of programming languages will involve AI designing highly efficient programming languages, potentially new ones.  Programming languages will evolve faster than before and the choices we make now will be completely different from those we make ten years from now. AI will likely accelerate the iteration of programming languages, resulting in the most efficient programming language in human history. It won't be too late to start learning it then.

The second perspective is that our interaction with AI has already reached the level of natural language. I often hear language educators say, "Oh no! Language institutions won't be needed anymore since AI can be a teacher.", but I disagree. Natural language skills will only become more critical. Not only do you need to communicate with ChatGPT and design good prompts, but you also need to be able to guide the correct answers and ask the right questions. This skill is not only crucial for communication with humans but also with AI. AI contains vast knowledge waiting to be accessed, but you have to ask the right questions to obtain wisdom. So, I think natural language is even more important. When it comes to education, I believe that natural language is more significant than programming languages. For humans, language is not just a skill; learning an additional language means acquiring a new way of thinking, which is something AI cannot replace. As a result, a person who can speak multiple languages can have more critical thinking, diverse perspectives, and innovative combinations. Innovation essentially involves connecting distant ideas, and humans have this ability due to our language skills. The more languages we learn, the more diverse our thinking frameworks and abilities become.

Returning to the topic of education, should programming languages be included in the curriculum? I think they might eventually become a core subject like English, but one should never learn programming languages just for the sake of it. Moreover, AI might continue to polish the design of programming languages and computer systems. Whatever the case, we should embrace lifelong learning because everything evolves every day. If you are a middle, high school or a college student, I believe the most important thing is to follow the pyramid structure: the base is "self-confidence," the middle is "self-management," and the top is "self-learning." These will remain unchanged regardless of the AI era.

Erica Lu:

How do you view the challenges of limited data faced by startups during their early stages, and what strategies do you suggest for obtaining more data?

Sega Cheng:

First and foremost, AI is already an open community, with numerous pre-trained base models and open datasets available. One strategy is to stand on the shoulders of giants, utilizing their well-trained base models, rather than attempting to train a new model from scratch, which could potentially cost millions of dollars. By fine-tuning the base model, you can add your own desired data and create a more specific model, much like teaching a child's brain a particular skill.. For example, in our influencer search application, we input the influencer data into the AI "brain," which can then answer questions such as "Who is the best influencer in Japan for promoting ramen?" With GPT, it can provide a direct response. Nowadays, every enterprise can start training their own AI "brain" at reduced costs.

As a result, we've transitioned from digital archives to intelligent archives. Previously, the data from vertical industries was the most valuable and held by individuals. By using GPT models to store this data, it becomes an in-house expert within your enterprise or research project. We indeed see a scarcity of vertical data and recognize its value. While AI technology may become widely accessible, data is ultimately the most crucial aspect. We do see the research moving towards small data. In comparison, the human brain is incredibly efficient, consuming only 20-25 watts of power, equivalent to the energy from eating a single hamburger to power a GPT for a whole day. However, running a GPT for an entire day could cost tens of thousands or even millions of dollars, making the human brain much more efficient. The human brain can learn impressive things from very little data, an area in which AI still falls short. Thus, there is no need to worry.

Erica Lu:

How should we contemplate the future AI ecosystem?

Sega Cheng:

Recently, the AI research community's openness has played a crucial role in the rapid development of AI research in the past few years. From Google's Transformer in 2017, to Google's BERT in 2018, and Meta's RoBERTa in 2019, followed by Stanford's Foundation Model, the breakthroughs in AI are actually a result of continuous improvement by the community. However, with the rise of ChatGPT, there are concerns that this openness may be reversed. When the influence of AI has grown to the point of becoming a trade secret, leading AI companies will start protecting their intellectual property. From a pessimistic perspective, it may slow the pace of AI development , especially in the case of AI research coming from these big tech companies. The ecosystem may not continue to develop at the same rapid pace as the past decade, with everything being published. However, it will continue to progress because academic research is still pushing forward. Data will still be a big issue. From the industry perspective, AI will become like water and electricity, and the focus will shift to how to add value to existing business models by finding the right "fields". 

Taiwan's advantage lies in its hardware manufacturing, which is globally renowned, whether in terms of chips or the entire hardware supply chain. These are irreplaceable assets, and the computing power required for AI is still insufficient. Therefore, we can expect Taiwan's semiconductor industry to continue to flourish, as the computing power needed for AI is currently in short supply, with only the largest companies able to access the latest hardware. These shortages will continue for the next few years. On the other hand, in the software industry, it is crucial to think globally from day one, and consider Taiwan as Israel or Singapore, because the software industry seeks scale. While Taiwan's population is relatively small at 23 million, it is definitely not enough to just focus on Taiwan, other markets must be considered as well. Whether it is AI or other software, on the first day, we must look at the world from Taiwan's perspective.

Erica Lu:

As a parent, what kind of AI world would Sega like its future children to live in, and what can we do now?

Sega Cheng:

Expanding the timeline, maybe in 50, 100, or 200 years, human development of digital technology may just be a transitional phase. After 100 years, people may not even be talking about digital technology or AI anymore because they would have matured. At that time, AI will assist in crucial areas like gene editing, protein research, new drugs development, longevity, space exploration, and others. Therefore, I think this period of time is very important because technology is always neutral, human choices about how to use it will determine its impact. This is also a critical moment when AI's influence has reached every corner of society. We must make choices about which areas we really cannot use, which areas we should keep open, and which areas should be used with limitations. These decisions will pose significant challenges in the future.

Every time human society advances, it increases the polarization between people, and as progress continues to widen the gap, the most dangerous issue will be, why does technological progress impact the whole society? The problem lies not in the lack of improvement in human living standards, but in the increase in inequality. Although people today are better off than those  100 years ago, they are not happier or more content. It is because of inequality. If technology continues to widen the gap, then society will collapse. Therefore, I believe that this is a very significant problem that needs to be addressed by AI.

Thus, I think we must look at AI from a societal perspective, not just from a technical standpoint. As we hurtle towards progress, it is crucial to ensure that everyone can board the train and obtain a ticket. This is also a lesson I learned from the industrial revolution because reskilling and upskilling are difficult but necessary to absorb the impact of the entire technological revolution. Therefore, we need to look at AI from a humanistic perspective, not just from an AI perspective.

CEO Insights

Unleashing the Value of AI for Individuals and Enterprises (Part 1): The Importance of Fields Where AI is Applied

To keep Taiwan's industry, government, academia, and research institutions ahead of the global technology and industry trends amid this wave of transformation, the Taiwan Science and Technology Hub (Taiwan S&T Hub) has invited internationally renowned AI expert Dr. Fei-Fei Li, current director of Stanford's Human-Centered AI Institute, Chief Scientist and Researcher of ImageNet, and co-founder and chairperson of AI4ALL, to participate in the "What we see and what we value: AI with a human perspective" roundtable forum held on March 23. Together with local experts, they discussed how to make AI a key driving force for the betterment of humanity. The forum was moderated by Erica Lu, a social affairs consultant at the Business Next, and featured guests including the Pegatron Corporation Chairman Tung Tzu-Hsien, PChome CEO Alice Chang, and iKala co-founder and CEO Sega Cheng, among other industry experts.

The following article is based on the insights shared by iKala co-founder and CEO Sega Cheng during the roundtable discussion (Part 1).

Erica Lu:

As someone who comes from a software engineering background and has founded a startup that has been focusing on software development, you've even made AI empowerment an important goal for your company. Could you please share with us your thoughts on how AI is shaping the future of software, industry, and software services?

Sega Cheng:

In fact, when we were doing Vision research at Stanford Computer Science 18 years ago, it was really challenging, because there was no Cloud, no Big Data, no AI, and no iPhone. That was back in 2006. So now when we talk about ABC, which stands for AI, Big Data, and Cloud, it actually developed in reverse order. First, there was Cloud, followed by Big Data, and then AI. Once AI had access to algorithms, computing power, and data, it quickly became effective. This has been the development pattern of AI. That's why iKala initially called itself a Human-Centered AI Company, as we believe AI is an augmentation, not a replacement. In fact, from an industry perspective, whether it's under the fields of healthcare, retail, or even military and defense, AI requires a specific field to be useful; without a field, AI is of no use. Regarding the turning point brought by GPT, we were actually quite surprised. We thought that GPT-like technology would only appear in three years, but OpenAI brought it forward by three years.

For the industry, we see this as a positive development. People have already started talking about AI's Moore's Law, which now seems to double its capabilities or reduce its costs approximately every three months, rather than waiting until every year and a half or two years. Since the release of ChatGPT, the software industry has been trying to shrink the AI "brain". Tesla's former AI director, Andrej, initiated the nanoGPT project, aiming to continually reduce AI in terms of training costs, deployment costs, application costs, etc. Thus, AI has begun to exhibit Moore's Law, and given the openness of the AI research community, we can expect rapid progress. There are a variety of models and datasets, and as soon as a paper is published, its datasets and some source code are openly available, enabling AI advancements to happen very quickly. We expect that under this new Moore's Law, AI will eventually become accessible to everyone, with the cost of obtaining intelligence becoming lower and lower.

That's why we think AI empowerment is so important. In the past, when talking about digital transformation, people were skeptical because they couldn't calculate the return on investment. They were uncertain about the long-term cost benefits. But if the purpose of digital transformation is to gain intelligence, then it will become a new wave for all industries. So, when the cost of gaining intelligence is almost zero, it brings up questions like "Will my existing workflow be disrupted? Will my personal productivity increase tenfold?" All of these are possible. So, when we look at AI empowerment, we treat AI as a utility, like water and electricity. In 10 or 20 years, we may not talk about AI anymore, because it will be as ubiquitous as water or electricity, and just like we don't think about how the entire electrical grid works when we plug in our phones or use a bread machine.

In the future, AI will have reached a point where the cost is low enough and everyone, even non-AI experts, can use it. As Professor Fei-Fei Li mentioned, the world seems to turn over every time we wake up. Now, we must understand that we've always treated AI as a value-added service. When we talk about the impact on industries, I think AI will open up countless new possibilities, such as protein folding, better understanding of consumers, and a multitude of scientific experiments. It has already revolutionized scientific exploration, so AI can even have the potential to become a utility in research. We believe the opportunities brought by GPT and large-scale language models are boundless! In our long-term work with influencer searches, we've seen search evolve from keyword search to the next step of natural language search. What ChatGPT has shown us is that when humans can interact with computers in a comfortable and natural way, it represents a revolution in the software industry brought about by AI. Therefore, we can expect natural language search to become increasingly developed in the future.

As AI continues to advance and integrate into various industries, it will not only transform existing processes but also create new opportunities and potential applications. With the rapid development of AI technologies like GPT, the landscape of software and services is changing at an unprecedented pace. AI is expected to become an indispensable part of our daily lives, much like utilities such as water and electricity. In this future, everyone will be able to utilize AI to enhance their productivity, problem-solving abilities, and overall quality of life, making AI empowerment an integral aspect of our world.

Erica Lu:

When you first started your own business, it was inspired by the birth of the internet and the landscape was uncertain. However, there were many difficulties in embarking on a venture and delving into a technology, and you yourself have gone through several transformations. Now, as we enter a new era of technological advancements, what advice or thoughts do you have for those interested in investing in startups or exploring AI entrepreneurship in the future?

Sega Cheng:

When it comes to generative AI, my primary advice is to avoid starting a generative AI company. The main reason is that the success rate of startups is inherently low, with 90% of companies failing within five years, even in Silicon Valley. As we discussed earlier, AI requires specific fields, so I believe even OpenAI will need broader and deeper moats to succeed. With language models  readily available on GitHub, anyone can deploy their own applications for generating text or images at a low cost.

We realized this in 2018 when we introduced AI-driven MarTech to our clients. At that time, we developed a feature called Picaas, a feature that employed AI  to remove background clutter in images and filled in gaps to create a clean, unaltered photo. This functionality has now been adopted by Google Photos as Magic Eraser. When we launched this feature in 2018, we encountered two challenges. First, we found it challenging to scale the business from a technical standpoint, leading us to realize that startups should prioritize customer needs rather than technology. Although AI has the potential to perform incredible tasks, what do you want it to do for you? How can it be applied to your industry? McKinsey's research shows that 70% of AI's value comes from added services that enhance existing business models, rather than creating entirely new ones. This was a valuable lesson that we learned from our first experience with AI.

The second lesson was centered around an ethical issue that arose after launching the background removal and replacement feature. The design community expressed concerns about the potential for image theft and alteration. In response, we retrained our model to identify and avoid modifying images from paid libraries, preventing copyright issues.  By addressing these concerns, we gained valuable insights into the feasibility of generative AI's business models.

On the other hand, while people worry that generative AI may lead to job loss, AI inherently replaces tasks rather than entire occupations. It leads to a "deconstruction" of job roles rather than the sudden disappearance of jobs. Throughout human history, it's rare for a group of jobs to vanish suddenly. Instead, as technology advances, it replaces certain tasks. For example, ChatGPT is great for summarizing information, but you'll find it takes a lot of time to edit its output. The time saved on ideation, summarization, and drafting still increases productivity, but time spent on certain tasks may increase or decrease. When looking at how AI technology can be popularized and create consumer value, it's essential to deconstruct the entire workflow and determine which tasks AI can and cannot solve.

We've been discussing generative AI and software, but robotics will also experience significant advancements this year due to GPT and multi-model technologies. In Professor Fei-Fei Li's lab, robots can now recognize and execute over a thousand actions. From online to physical applications, we can expect substantial progress in the coming quarters.

Concerning the risk of AI technology running amok, we don't need to worry too much about narrow or weak AI. However, when AI becomes a utility, we can expect governments to regulate it similarly to how they regulate water, electricity, and national defense. As AI transforms into a utility, governments worldwide will intensify their supervision and regulation of this technology.

CEO Insights

An Overview of Business Model Evolution in the Technology Industry Through the emergence of ChatGPT

Many believe that multinational tech giants continuously create new business value through sheer technological breakthroughs and open sourcing their results, however, this perspective is only partially correct. The reason they are able to continuously create significant value is not just due to their technology, but also because of the "network externality", also known as the network effect, and the economic principle of "increasing marginal revenue".

"Network externality" has been fully understood by the industry since the rise of the digital economy. Simply put, a few technology companies have control over user scale and service entry points. In this case, not only does increased usage of a service lead to its increased value, creating a positive cycle, but even small improvements to the service can generate substantial new business value. In the classic book "Zero to One," Peter Thiel also emphasizes the importance of network externality, which is inherent to digital products.

Imagine, if Facebook or Google improves its computer vision technology by 1% in recognition rate, it may represent a new advertising value of over 1 billion or even up to 10 billion US dollars. But for a startup company, the added value would be relatively limited as the foundation and economies of scale for advertising is dominated by technology giants, rather than general start-ups.

"Increasing marginal revenue" is an inherent phenomenon in the knowledge economy. It refers to the fact that as more knowledge and technology are invested in a knowledge-dependent economy, output will increase and producer revenue will also trend upward. In contrast, traditional agricultural and industrial economies rely on material resources, which have a distinct exclusionary characteristic: their value can only be utilized by one user at a time. Furthermore, these resources are scarce, and as their usage increases, costs become higher, ultimately leading to a decrease in producer revenue. Knowledge-based resources are shareable, and the same knowledge can be simultaneously occupied and used by multiple people. Also, they are not consumed, but rather utilized, and generate new knowledge during use. Information and knowledge resources accumulate and develop with use, and their cost decreases with repetitive use, leading to increasing revenue.

Why do technology giants continuously open source their cutting-edge technology without reservation? Because open source code is not their core competitive advantage. Instead, the real competitive advantages lie in "network effects" and "increasing marginal revenue." Open-sourcing these code is to further utilize existing network externalities, to create a larger user base. A startup company that develops a framework like TensorFlow or PyTorch might carefully protect it as intellectual property, see it as a core asset, and even try to profit directly from it; but for big tech, TensorFlow or PyTorch is just like any other product, an amazing technological innovation, but its core purpose is to expand the network effects of its other products. These two business logic are fundamentally different.

With the combination of these two effects, the phenomenon of "big gets bigger" in the digital economy occurs. That's why the global technology giants can continuously attract top talents, continuously invest in new technology and knowledge-based products, and continue to grow year after year. Scholars from Harvard Business School have also summarized this economic phenomenon with a simpler term: "hub economy". This means that companies who control the entry points for users and services have a significant competitive advantage."

However, the emergence of ChatGPT has shaken the competitive advantage in the industry. Before November 2022, it was unexpected that a non-profit organization such as OpenAI would achieve such success with deep learning. With funding from Microsoft's investment, OpenAI was able to train and launch ChatGPT, resulting in its overnight popularity. Its applications might quickly expand into various fields, and may also have a significant impact on the core business models of technology giants.

Certainly, technology giants still have powerful product channels and network effects, and are actively deploying technology equivalent to ChatGPT into each of their products, to strengthen their own network effects and defend against the threat posed by ChatGPT. Should technology giants continue to share their advanced AI technology with nimble competitors, who may then use it against them? The industry has likely begun to take notice of this potential issue, which is not a technical challenge or a question of high-minded universal values, but a fundamental business problem.

On the other hand, some start-ups have built their business models directly on GPT-3, and their decisions and actions are already considered fast. However, OpenAI recently announced the launch of ChatGPT, which is based on GPT-3.5, and it is rumored that GPT-4 is on the horizon. This is significant and unpredictable for companies that have built their entire business model on GPT-3, akin to a black swan event. Nevertheless, this is the pace of technological evolution in the industry today.

We are now witnessing a historic transformation in the technology industry, both in terms of technology and business.

iKala Young Talent

I Enjoy Fast Changes And Constant Communication In the KOL Radar Internship

Maggie Tsao, Public Relations Intern, KOL Radar

graduated with a double major in Business Administration and Finance at NTUST. Now I study at Institute of Technology Management as a graduate student at NTHU. Though I learned a broad knowledge in management, a business degree can lead to so many  opportunities, which makes me wonder what to do in the future.

When I was a junior, I participated in many activities and internships to explore my career path, such as assisting in a research project with my professor and participating in an innovation and entrepreneurship competition with classmates. What's more, we won a prize in that contest. It was then that made me feel interested in the startup industry. Nevertheless, with a double major in Business Administration and Finance, I still wanted to try it in the financial sector. Hence, I started an internship at a famous financial leasing company. I got along well with my colleagues there, but I found a regular nine-to-five work pattern was not suitable for me. Through this experience, I understood what I want to join is a startup with a flexible and free culture.

My Three Factors For An Internship - Interest, Prospect, And Learning Depth

The most crucial point is whether I am interested in the job. Instead of being a full-time worker, I could explore what I am passionate about as a student. Second, I would consider the prospects of industries and choose a promising company from them. Third, whether I could learn a lot during the internship period is also essential for me.

In addition to the above three points, I came into contact with some startup teams when I did the research project. The experience of winning an award in the innovation and entrepreneurship competition made me interested in the startup industry. With the hope of joining a mature startup, iKala became one of my choices.

KOL marketing is now all the rage as social media becomes more and more prevalent. Compared to other KOL marketing companies, iKala's services are broader and richer, which I consider a potential corporation. Besides, iKala emphasizes a human-centered culture, which demonstrates that it regards everyone's voice and employees' career plans. Moreover, iKala's 8 core values and its lively culture also attract me. Before I applied, I inquired about an upperclassman who ever did an internship at iKala. She had a wonderful experience at iKala, and recommended it to me sincerely. For these reasons, I made up my mind to join iKala.

Numerous oral presentations in graduate courses cultivate my communication and data collection skills, which push me to do research on different kinds of industries and follow the latest trends. These trainings and the experience of participating in an innovation and entrepreneurship competition gave me a better understanding of clients' needs. And these soft skills are essential in my daily work as a KOL public relations intern.

As A Bridge Between the Company And KOLs, I Patiently Accomplished Many Long-term Business Cases

The above abilities help me achieve my primary mission "To accomplish business cases," which are long processes with several trifles and details. I have to communicate with internal departments and clients closely, so the ability to communicate and coordinate is essential.

Once we proceed with cases, we have to check numerous rules of business cases. And I will contact KOL candidates who are provided and filtered by planners and me, to inquire whether they would like to cooperate with us and then I will report to account managers and brands. If they agree to work with us, I will communicate with them frequently. What I have to do includes sending samples to them, scheduling social media posts, reviewing copywriting and image of posts before publishing. After releasing these posts, I have to check whether materials conform to the rules and ask KOLs to provide data of post performance to me.

Contacting Nearly 100 KOLs At One Time, I Deal With All Kinds Of Problems And Crises

The experience made me feel a sense of accomplishment was contacting nearly 100 KOLs at one time. And I had to tackle many emergencies, but if they had gotten posts done, I would have a sense of fulfillment.

Sometimes it got me frustrated, too. For example, KOL didn't reply to me in time or they wanted to break up the partnership with us, and it made me have no idea what to do in the beginning. After my mentor gave me some suggestions, I could calmly apply my previous experience to deal with these problems. Like one time, a KOL thought he only needed to take a photo in a case, yet we told him that a video had to be filmed in this business plan. Then he would like to cancel this business case. Hence, I tried to propose some alternatives, such as increasing the case fee and providing him with free products for fans. Eventually, he changed his mind to keep our partnership.

In another experience, a KOL used a skincare product provided by a partner brand, then her face peeled. Out of the blue, I had to comfort her right away and clarify the reason by asking the brand to provide its ingredients. My colleague also assisted me in dealing with the follow-up process. This experience also made me feel a sense of fulfillment. 

To Handle Lots Of Communication, You Need To Excel In Time Management

The challenge in this job is that you need to excel in time management, for I have to communicate with several KOLs at one time. Furthermore, they often text me when I am off work. If I don't reply to them in time and make them wait for too long, they may think I am impolite. But if I constantly respond to their problems, my daily routine will be interrupted by work. As a consequence, I scheduled matters according to their priorities and put off things with lower priority until I was on duty. In sum, communication skill and time management are the most important abilities I learned here.

If You Are Also Interested In the Position of KOL Public Relations Intern...

Suppose you embrace a flexible culture and lifting atmosphere in the office, and you follow KOLs in different industries and command communication skills and time management. In that case, last but not least, if you value personal growth, then maybe the position of KOL public relations will be suitable for you.

We are hiring:

iKala Young Talent

Embrace the Unknown Before Learning; Unleash My Potentials In A Flat Organization

Shawn Yang, Software Engineer, iKala Cloud

I graduated from the Department of Computer Science and Information Engineering at NTU. I didn't take part in any internship when I was an undergraduate, and I chose to look for a full-time job right after graduating. To be frank, little did I want to do a job related to information engineering before entering the workforce. I had every intention of being a dive instructor, then I could emigrate to Australia with friends. While it might not be a thoughtful and long-term plan, I decided to turn back to work in the information industry.

Benefit Becomes A Less Important Factor While Selecting A Company To Work With

When I was looking for a job after graduating, I saw many positions which required 3-5 years work experience. Therefore, I chose to join an interesting company without this requirement. At first, I regarded employee benefits highly. For example, when we had a make-up workday, we could go out and have fun for half-day, and take the other half-day off. We also had birthday parties and club allowance. Nevertheless, when we were so busy, we might not have a chance to enjoy these benefits. Although there were so many coworkers, their abilities and competencies varied – that made me think it is not always better to work at a big company.

Two Perks Of Working At iKala – High Productivity And Horizontal Communication

iKala is the second company I joined after graduating. Before iKala, I worked at a camera/streaming related company in the traditional industry. The biggest difference between my last company and iKala is the efficiency of clarifying problems. At my last job, my team may spend an afternoon finding out the problem of wrong way of connecting joints for clients. But at iKala, we can get to the root of problems quickly with correspondent personnel solving them, so that we can have a high productivity at iKala. What's more, coworkers are highly qualified and reliable here, so you can learn a lot from them.

Another difference between two companies is ways of communication. Because my first company is a very hierarchical organization, you couldn't refute the boss and managers. But at iKala, everyone has the right to express your thoughts. As long as you explain your ideas clearly with proper reasons, your opinions may be adopted. It is a more horizontal communication way at iKala, which means your ideas wouldn't be ignored even if you are a rookie. 

My Opinion On WFH

I think it is great to work from home, which has been useful before the outbreak of Covid-19. Nonetheless, it was totally not allowed at my last company. Work-life balance is pretty essential for me. That is, you have to manage your working hours. Sometimes I get inspiration out of working hours, and I will get some work done. I think it is hard to separate your work and life clearly, yet you have to strike a proper balance for yourself. Hence, the flexible culture of iKala is suitable for me. Except for discussing stuff with a large scope, it is more efficient to meet in person. Other times, I really enjoy the working mode of WFH.

You Can Learn Everything Here

Many internal documents are left by predecessors at iKala, so you can learn how to deal with problems from them. If you still have no idea after referring to these documents, you can consult a colleague. Senior coworkers will also provide wonderful ideas at meetings, then you decide how to do it by yourself.

iKala's employees are active learners, and everyone is willing to answer others' questions. Even though sometimes they don't know the solutions, they will introduce colleagues who know better for you. Compared to senior coworkers, I have more chances to ask questions when I do something wrong or don't have a good understanding of something. As a rookie, I can attain numerous feedback from learning. At iKala, information and communication are considerably transparent. In other words, everyone's willing to share what they know with each other.

In addition to hard skills, what I learn most at iKala is to reduce costs of communication. For instance, each feature may be assigned to only one member in our team. And everyone has a backup partner who will cover you when you are absent. Thus, you need to organize a document with easy instructions within the least time, which could be understood by someone who doesn't have the basic knowledge. For me, it is a tough and challenging task.

As A Rookie, You Can Get the Hang Of Work By Two Tips

As a rookie, before I ask colleagues questions, I will prepare a big picture and different ideas. After that, I will let my manager or coworkers know what's the key problem I encounter, then they may evaluate pros and cons of different methods for me. Namely, you should organize your thoughts before discussions, which can reduce costs of communication. On the other hand, if I am stuck at the same place for too long, I will set a stop loss point like 2 hours. In case I still can't figure it out, I will seek help in time. Since it would be a waste of time to keep holding on, I would rather learn what others will do. To put it in a nutshell, listing problems and reporting regularly are useful tips to get the hang of work as a rookie, which are also good ways to build a sense of trust in your team.

Before Getting Started On Learning, You Need To Embrace the Unknown

If you would like to join iKala, you need a strong ability to learn because there are too many things you can learn here. If you specialize in a specific area deeply, even you are assigned a task never seen before, you can get the hang of it swiftly. Yet, before you have a strong ability of learning, you need to take the first step into the unknown, for you will never know what you will encounter.

We are hiring: