Editorial 1: The Great Unravelling
Context:
- We’re approaching the 15th anniversary of the Global Financial Crisis (GFC) of 2008. A grim milestone. Not because it reminds us of those fateful months of 2008. But because the chain of events the crisis engendered is likely to have profound consequences for our future.
Era of globalisation and Reform:
- It is appreciated that the rising tide of globalisation in the 1990s and 2000s lifted many economics of the world and also led to growing disenchantment in the West. The “China shock” had hollowed out blue-collar jobs and spawned increasing inequality.
Not learning from the Global Financial Crisis (GFC), 2008
- As per the IMF. “Structural” fiscal deficits in advanced economies tightened a whopping five to 10 percentage points of GDP from peak to trough between 2007 and 2017,
- Some of this was ideological in case of US and some of this was complex and inflexible European fiscal rules that unduly emphasised austerity.
- Worried about public debt, fiscal policy was fighting the last war, even as the hysteresis from the GFC crisis was getting deeper and broader
Tight fiscal policy is half solution to counter the crisis like GFC-2008
- Excessively tight fiscal policy was only half the problem. It induced excessively loose monetary policy that was struggling to combat post-crisis economic malaise.
- The too-tight-fiscal, too-loose-monetary over the last decade was exactly the wrong policy prescription for advanced economies and the world for several reasons.
- First, monetary and fiscal policy, working at cross purposes nullified each other and labour markets in advanced economies were too slow to recover
- Second, excessive monetary easing distorted and inflated asset prices that accentuated inequality, and induced a substitution away from labour towards cheap capital, accentuating the employment malaise.
- Third, quantitative easing became the cure for every ailment, obviating the need for more fundamental reform in advanced economies: Re-tooling and re-skilling workers confronting the China shock, building infrastructure and cutting regulation, building deeper and smarter safety nets to compensate those who had been displaced from globalisation.
- Fourth, all this sowed the seeds for deglobalisation. Frustrated by the economic malaise and deepening inequality, politicians in advanced economies did the easy thing — assign blame outside. It was the decade of Brexit and the US-China trade war.
- Instead, structural fiscal deficits surged in advanced economies. Fiscal transfers kept private sector demand strong, and in the wake of myriad supply shocks, contributed to the highest inflation in five decades.
- Fiscal policy had gone from being countercyclical to counterproductive. Meanwhile, monetary policy which was initially fighting the last war, is now scrambling to catch up.
Rising geo-political uncertainties are moving toward de globalisation
- Rising geo-political uncertainty is inevitably inducing multinationals to de-risk their supply chains.
- What’s more worrying is pre-Covid deglobalisation tendencies by governments have only gotten stronger, with muscular industrial policy in the West (Inflation Reduction Act, Chips Act) aimed, in part, at re-shoring production and boosting domestic blue-collar job creation. The ostensible justification is resilience and “national security.”
- But national security is often the first refuge of the protectionists. History is replete with examples of how this is a very slippery slope. Incentives are only likely to deepen and broaden. Today it’s semi-conductors and electric vehicles. Tomorrow it could easily be pharmaceuticals and agriculture
- As, Unsurprisingly, South Korea, Japan, Taiwan and Europe have all responded with their own version of subsidies and, in this environment, emerging markets will unfortunately become more emboldened to be protectionist.
The reason for deglobalization
- First, allocative efficiency inevitably suffers, hurting medium-term productivity, competitiveness and growth.
- Second, industrial policy that succeeds in recovering the growth will result in economic balkanisation and risks undoing the gains of the last 30 years.
- Globalisation induced an outward shift in the global supply curve, boosted global growth, helped low-income countries catch up, structurally reduced inflation, and pulled millions out of poverty. De-globalisation risks undoing all those gains. We are on the cusp of the “Great Unravelling”.
The challenges ahead
- Today, the bigger threat is rapid advances in technology and AI. The “China shock” hollowed out blue-collar jobs in the West.
- The expected impact of ChatGPT on out white-collar jobs . As Every industrial revolution has increased the share of capital vis-à-vis labour. The current technological revolution threatens the same.
Conclusion:
- Therefore, there is need for an intelligent and coordinated global response to these challenges. Educate, train and skill workforces is need of hour so that they can complement technology, not be substituted by it and it will enable creative destruction in response to the breathtaking pace of technological change.
- Along with it, there is need for intelligent and robust safety nets to protect those left behind and need to take measures for counter-productive protectionism.
Editorial 2: AI and the environment: What are the pitfalls?
Recent Context:
- Recently, the field of artificial intelligence is booming. It has captured the public imagination with its ability to converse, write code, and compose poetry and essays in a surprisingly human way.
Upcoming challenges with the development of AI in future:
- Investment in artificial intelligence is growing rapidly. The global AI market is currently valued at $142.3 billion (€129.6 billion), and is expected to grow to nearly $2 trillion by 2030
- AI systems are already a big part of our lives, helping governments, industries and regular people be more efficient and make data-driven decisions.
- But there are some significant downsides to this technology. Environment sustainability is one the major challenges with the development and use of AI.
AI has a big carbon footprint
- In order to carry out the tasks they’re supposed to, AI models need to process with bulk of data to learn to recognize an image of a car, for example, an algorithm will need to churn through millions of pictures of cars. Or in the case of ChatGPT, it’s fed colossal text databases from the internet to learn to handle human language.
- This data crunching happens in data centers. It requires a lot of computing power and is energy-intensive.
- “The entire data center infrastructure and data submission networks account for 2-4% of global CO2 emissions,”. This is not only AI, but AI is a large part of that.” That’s on a par with aviation industry emissions.
- It’s important to note that the Massachusetts study’s estimate was for an especially energy-intensive AI model.
- Smaller models can run on a laptop and use less energy. But those that use deep learning, such as algorithms that curate social media content, or ChatGPT, need a significant amount of computing power.
So what can be done to tackle AI’s footprint?
- Environmental concerns need to be taken into account right from the start in the algorithm design and training phases.
- There is need to consider the entire production chain and all the environmental problems that are connected to this chain… most notably energy consumption and emissions, but also material toxicity and electronic waste
- Rather than building bigger and bigger AI models, as is the current trend, companies could scale them down, use smaller data sets and ensure the AI is trained on the most efficient hardware available.
- Using data centers in regions that rely on renewable energy and don’t require huge amounts of water for cooling could also make a difference.
- As, huge facilities in parts of the US or Australia, where fossil fuels make up a significant chunk of the energy mix, will produce more emissions than in Iceland, where geothermal power is a main source of energy and lower temperatures make cooling servers easier.
- Energy isn’t the only consideration. The huge amount of water data centers need to prevent their facilities from overheating has raised concerns in some water-stressed regions, such as Santiago, Chile.
Emissions aside, there is need of efficient use of AI for environment protection?
- Even if big tech companies shrink AI’s energy use, there’s another issue that is potentially more damaging to the environment.
- There should be more focus on the way AI is being used to speed up activities that contribute to counter climate change.
- For e.g. example e use of AI for advertising. These are deliberately “designed to increase consumption, which assuredly comes with a very significant climate cost,”
- Google has since said it will no longer build customized AI tools to help companies extract fossil fuels.
Conclusion:
- The role of artificial intelligence is only likely to become more significant in the future. And keeping up with such rapidly advancing technology will be a challenge.
- Therefore, regulation is crucial to ensuring AI development is sustainable and doesn’t make emissions targets harder to reach.
- Along with it, governments should also make aware the people about how to deal with AI — to encourage innovation in the field and reap the benefits this new technology brings, while avoiding the potential dangers and protecting citizens.